CN108309262A - Multi-parameter monitoring data analysing method and multi-parameter monitor - Google Patents

Abstract

The present embodiments relate to a kind of multi-parameter monitoring data analysing method and multi-parameter monitor, the method includes：Sign monitoring data acquisition is carried out to measurand, obtains the sign monitoring data of measurand；Wave group feature recognition is carried out to ECG data therein, obtain the characteristic signal of ECG data, beat classification is carried out to ECG data according to characteristic signal, beat classification information is obtained in conjunction with electrocardiogram basic law reference data, it generates ECG events data and determines corresponding ECG events information, and determine whether ECG events information is preset anomalous ecg event information；When for anomalous ecg event information, the first warning message is exported；And one or more of determining pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data whether there is the abnormal data beyond corresponding given threshold, and other abnormal events informations are generated according to abnormal data；When beyond given threshold, the second warning message is exported.

Description

Multi-parameter monitoring data analysing method and multi-parameter monitor

Technical field

The present invention relates to technical field of data processing more particularly to a kind of multi-parameter monitoring data analysing method and multi-parameters Patient monitor.

Background technology

Multi-parameter monitor is a kind of common clinical treatment equipment.The characteristics of this custodial care facility is that have multigroup sensing Device can monitor the vital signs index such as electrocardio, blood pressure, blood oxygen, pulse, breathing, body temperature simultaneously.It is join in ward mornitoring more Number patient monitors can become the important references to patient care of doctor, enable the clinician to find in time patient's problem simultaneously and Shi Jinhang processing, to ensure that the life security of patient.Patient monitor clinical application is found in：In operation, operation after, wound shield Reason, coronary heart disease, urgent patient, newborn, premature, hyperbaric oxygen chamber, delivery room etc..

Most of multi-parameter monitors carry out the triggering of alert event by the way of threshold value is arranged currently on the market.Such as It carries out alarming or alarming when bradycardia when heart rate is too fast.Although the method letter of this set threshold value It is single intuitive, but accuracy is poor, because of the weak current that the electrical activity that electrocardiosignal is cardiac muscle cell reflects in body surface, leads to It crosses external electrode and amplification record system is recorded.Other non-cardiogenic telecommunications can also be recorded simultaneously in recording process Number, for example, skeletal muscle active belt come electromyography signal interference etc..These signals may result in incorrect heartbeat signal detection, To trigger alarm.These wrong reports frequently occurred cause patient and medical staff to loosen the police to alarm events in the course of time It is cautious, and patient effectively cannot pay close attention to and handle when really needing the event of Clinical Processing to occur.Meanwhile doctor and shield Scholar can spend a large amount of energy on processing false positive event, waste the medical resource of hospital.According to the data of American Heart Association, In the inpatient that heart arrest occurs, it can be survived only less than a quarter.

In addition, electrocardiosignal is the embodiment of myocardial electrical activity process, therefore electrocardiosignal is in addition to that can be used for detecting heart rate In addition, the information of a large amount of heart state can also be embodied.When heart state goes wrong, electrocardiosignal will appear It is corresponding to change, many times not necessarily it is embodied on heart rate.Current multi-parameter monitoring equipment can only carry out electrocardiosignal Very limited analysis and alarm, this, which also causes to have, largely fails to report event, and the life and health of patient cannot obtain effectively Protection.

Invention content

The object of the present invention is to provide a kind of to solve the multi-parameter monitoring data analysis side that prior art defect proposes Method and multi-parameter monitor.

First aspect of the embodiment of the present invention provides a kind of multi-parameter monitoring data analysing method, including：

Sign monitoring data acquisition is carried out to measurand, obtains the sign monitoring data of the measurand；The body Monitoring data is levied with time attribute information, the sign monitoring data includes：ECG data, pulse data, blood pressure data, Breath data, blood oxygen saturation data and temperature data；

Wave group feature recognition is carried out to the ECG data, the characteristic signal of the ECG data is obtained, according to institute It states characteristic signal and beat classification is carried out to the ECG data, beat classification is obtained in conjunction with electrocardiogram basic law reference data Information, and generate ECG events data；

Corresponding ECG events information is determined according to the ECG events data, and determines the ECG events letter Whether breath is preset anomalous ecg event information；When for preset anomalous ecg event information, the first warning message is exported； First warning message includes the anomalous ecg event information and time of fire alarming information；The anomalous ecg event information tool There is corresponding project information；And

Determine one in the pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data or It is multiple to generate other anomalous events letter with the presence or absence of the abnormal data beyond corresponding given threshold, and according to the abnormal data Breath；When beyond the given threshold, the second warning message is exported；Second warning message includes other described anomalous events Information and time of fire alarming information；Other described abnormal events informations have corresponding project information.

Preferably, the method further includes：

The sign monitoring data is summarized according to the time attribute information of the sign monitoring data, described in generation The time series data of sign monitoring data, and stored.

Preferably, the method further includes：

When for preset anomalous ecg event information, according to ECG data pair described in the time attribute acquisition of information The ECG data in front and back preset period of time between seasonable generates anomalous event and records data；

Generate the related information of anomalous event the record data and first warning message.

It is further preferred that after the first warning message of the output, the method further includes：

It receives and the access of first warning message is instructed；

The anomalous event record data are obtained according to the related information and are exported.

It is further preferred that the method further includes：

Analyzing processing is carried out to anomalous event record data, generates simultaneously output abnormality incident report data.

Preferably, the method further includes：

Alarm event data is generated according to first warning message and the second warning message；

According to the time of fire alarming information, output is carried out to the alarm event data and is shown；Wherein, the alert event Data include sign monitoring data project information and corresponding anomalous ecg event information and/or other anomalous events letter Breath.

Preferably, described that wave group feature recognition is carried out to the ECG data, obtain the feature of the ECG data Signal carries out beat classification, in conjunction with electrocardiogram basic law reference data according to the characteristic signal to the ECG data Beat classification information is obtained, and generates ECG events data and specifically includes：

The data format of the ECG data is converted into preset standard data format by resampling, and to conversion after The ECG data of preset standard data format first be filtered；

ECG data after being filtered to described first carries out heartbeat detection process, identifies the ECG data packet The multiple heartbeat data included, each heartbeat data one cardiac cycle of correspondence, including corresponding P waves, QRS complex, T waves Amplitude and beginning and ending time data；

The detection confidence level of each heartbeat is determined according to the heartbeat data；

Disturbance ecology is carried out to the heartbeat data according to two disaggregated model of disturbance ecology, heartbeat data is obtained and whether there is Interfering noise, and a probability value for judging interfering noise；

The validity of heartbeat data is determined according to the detection confidence level, also, according to the effective heartbeat data of determination Lead parameter and heartbeat data, result and time rule based on the disturbance ecology, which merge, generates heart beat time sequence data； Heartbeat, which is generated, according to the heart beat time sequence data analyzes data；

According to beat classification model to heartbeat analysis data carry out amplitude and time representation data feature extraction and Analysis obtains a classification information of the heartbeat analysis data；

ST sections are input to the heartbeat analysis data of the specific heartbeat in a classification information result and T waves change mould Type is identified, and determines ST sections and T wave evaluation informations；

According to the heart beat time sequence data, P waves are carried out to heartbeat analysis data and T wave characteristics detect, are determined The detailed features information of P waves and T waves in each heartbeat, detailed features information includes amplitude, direction, form and the number of beginning and ending time According to；

To the heartbeat analyze data under a classification information according to the electrocardiogram basic law reference data, The detailed features information and ST sections and the progress secondary classification processing of T wave evaluation informations of the P waves and T waves, obtain heartbeat Classification information；

Analysis matching is carried out to the beat classification information, generates the ECG events data.

Preferably, sign monitoring data acquisition is being carried out to measurand, is obtaining the sign monitoring number of the measurand According to before, the method further includes：

Monitoring criteria data are determined according to the measurand；

The given threshold is determined according to the monitoring criteria data.

Multi-parameter monitoring data analysing method provided in an embodiment of the present invention realizes the multi-parameter prison based on artificial intelligence The data analysis of shield and alarm flow can carry out the measurement data that monitoring obtains automatic, quick, complete analysis, to different Normal electrocardio state and other vital sign parameters provides early warning, and reduces the wrong report phenomenon of interference fringe, and alarm accuracy is high, The type of detectable exception type especially anomalous ecg is more, has a good application prospect.

Second aspect of the embodiment of the present invention provides a kind of multi-parameter monitor, which includes memory and processor, Memory is used to execute the method in each realization method of first aspect and first aspect for storing program, processor.

The third aspect of the embodiment of the present invention provides a kind of computer program product including instruction, when computer program produces When product are run on computers so that computer executes the method in each realization method of first aspect and first aspect.

Fourth aspect of the embodiment of the present invention provides a kind of computer readable storage medium, on computer readable storage medium It is stored with computer program, in each realization method that first aspect and first aspect are realized when computer program is executed by processor Method.

Description of the drawings

Fig. 1 is multi-parameter monitoring data analysing method flow chart provided in an embodiment of the present invention；

Fig. 2 is the flow chart of the processing method of ECG data provided in an embodiment of the present invention；

Fig. 3 is the schematic diagram of two disaggregated model of disturbance ecology provided in an embodiment of the present invention；

Fig. 4 is the schematic diagram of beat classification model provided in an embodiment of the present invention；

Fig. 5 is the schematic diagram that ST sections provided in an embodiment of the present invention and T waves change model；

Fig. 6 is the structural schematic diagram of multi-parameter monitor provided in an embodiment of the present invention.

Specific implementation mode

Below by drawings and examples, technical scheme of the present invention will be described in further detail.

The present invention relates to the multi-parameter monitoring data analysing methods for clinical monitoring, and execute the multi-parameter of this method Patient monitor.Multi-parameter monitor is a kind of clinical treatment custodial care facility.The characteristics of this custodial care facility is that have multigroup sensor, The vital signs index such as electrocardio, blood pressure, blood oxygen, pulse, breathing, body temperature can be monitored simultaneously, by being handled in real time from each biography The incoming data of sensor, provide alarm signal when corresponding index occurs abnormal, allow doctors and nurses in time to the state of an illness It is handled.

In the vital signs indexs such as electrocardio, blood pressure, blood oxygen, pulse, breathing, the body temperature of multi-parameter monitor detection, electrocardio Monitoring different from other parameters, the algorithm by a series of complex is needed by the electrocardiosignal that sensor obtains Calculating can just extract effective information therein, the more complicated difficulty of processing procedure for other opposite signals, and be easy out Now detect the link of mistake.

Electrocardiosignal is the weak current that the electrical activity of cardiac muscle cell reflects in body surface, is remembered by external electrode and amplification Recording system is recorded.Other non-cardiogenic electric signals, such as skeletal muscle activity can also be recorded simultaneously in recording process Electromyography signal interference brought etc..Therefore it is considered that needing to carry out effective disturbance ecology and exclusion to electrocardiosignal, It can effectively reduce because being reported by mistake caused by interference signal.

In addition, electrocardiosignal is the embodiment of myocardial electrical activity process, therefore electrocardiosignal is in addition to that can be used for detecting heart rate In addition, the information of a large amount of heart state can also be embodied.When heart state goes wrong, electrocardiosignal will appear It is corresponding to change.It was found that existing monitoring is set during to existing multi-parameter monitoring equipment is studied in the industry It is standby to carry out very limited analysis and alarm to electrocardiosignal.In this regard, knowing in addition to carrying out effective interference to electrocardiosignal It not and excludes, to reduce because except wrong report caused by interference signal, it is believed that can also be improved from the following：

First, it needs to accurately identify P waves, T waves in heartbeat feature extraction, the more inspections that can be detected to avoid heartbeat And missing inspection, such as to some special ECG signals, such as the letter of the tall and big T waves of the slower patient of the rhythm of the heart or T wave hypertrophys Number more inspections.

Second, more careful division is carried out to the classification of heartbeat, and cannot only rest on sinus property, supraventricular and room property this Three kinds of classification require to meet the complicated comprehensive analysis of electrocardiogram doctor.

Third accurately identifies room and flutters atrial fibrillation and ST-T changes, changes ST sections and T waves to the heart so as to help to provide The help of myocardial ischemia analysis.

4th, heartbeat and cardiac electrical event are accurately identified.

In the present invention, we are directed to above-mentioned several points, by the analysis calculating to electrocardiogram (ECG) data, especially introduce artificial intelligence Energy (AI) technology is fluttered and is trembleed to the digital signal of acquisition carries out arrhythmia analysis, long pause stops fighting, and block is early It fights and escape beat, bradycardia is aroused in interest too fast, and ST sections change detection, the analyses and classification of cardiac electrical event, and accurate report is generated to reach The purpose of alert signal, to effectively carry out the monitoring of patient vital signs.

For this purpose, the present invention proposes a kind of multi-parameter monitoring data analysing method, method and step flow as shown in Figure 1, This method mainly includes the following steps：

Step 110, sign monitoring data acquisition is carried out to measurand, obtains the sign monitoring data of measurand；

Specifically, measurand refers to carrying out the life entity of bedside monitoring by multi-parameter monitor, wherein most conventional quilt It refers to people to survey object.

Patient monitor has the sign signal acquisitions devices such as electrode, probe, the cuff being in contact with measurand, passes through sign Signal pickup assembly acquires the sign of measurand, and obtains sign monitoring data by digitized processing.Sign is guarded Data can specifically include：ECG data, pulse data, blood pressure data, breath data, blood oxygen saturation data and body temperature number According to etc..Sign monitoring data has time attribute information, and each data point has corresponding data acquisition time, this time is It is time attribute information.While carrying out data acquisition, this data acquisition time is also recorded, and is supervised as sign The time attribute information of shield data is stored.

To more fully understand the intent of the present invention and realization method, below to the acquisition method of all kinds of sign monitoring datas and Principle carries out briefly introducing explanation：

ECG data：Guard of honor heart cell bioelectrical activity is recorded by the ecg signal acquiring of noninvasive ECG examination The signal of generation is acquired record in the form of single lead or multi-lead.

Pulse data：Pulse be arteries with heart relax contracting and the phenomenon that periodic pulses, pulse include intravascular pressure, The variation of a variety of physical quantitys such as volume, displacement and wall tension.We preferably use photoelectricity positive displacement pulses measure, sensor It is made of, is could be sandwiched on the finger tip or auricle of measured light source and photoelectric transformer two parts.Light source is selected to oxygen in arterial blood The selective wavelength of hemoglobin is closed, for example uses spectrum in the light emitting diode of 700-900nm.This Shu Guang is through outside human body All blood vessels change the light transmittance of this Shu Guang when arterial hyperemia volume changes, by photoelectric transformer receive through tissue transmission or The light of reflection is changed into electric signal and amplifier is sent to amplify and export, and thus reflects arterial vascular volume variation.Pulse is to follow one's inclinations Dirty beating and periodically variable signal, arteries volume also change periodically, the signal intensity week of photoelectric transformer Phase is exactly pulse frequency, i.e. pulse data.

Blood pressure data：The maximum pressure reached when heart contraction is known as systolic pressure, and blood is advanced to aorta by it, and Maintain systemic circulation.The minimum pressure reached when cardiac enlargement is known as diastolic pressure, it enables blood to flow back into atrium dextrum.Blood pressure Integral divided by heart cycle T of the waveform in one week are known as mean pressure.The measurement of blood pressure data can be realized there are many method, specifically may be used It is divided into invasive measurement and non-invasive measurement.We preferably use Korotkoff's Sound method and the noninvasive survey of two class of vibration measuring method in multi-parameter monitor Amount method.Korotkoff's Sound method is to detect the Korotkoff's Sound (pulse sound) under cuff to measure blood pressure, Korotkoff's Sound non-invasive blood pressure monitoring system System includes cuff inflation system, cuff, korotkoff sound sensor, audio is amplified and automatic gain adjustment circuit, A/D converter, micro- Processor and display unit grade.Vibration measuring method is to detect the oscillation wave of gas in gas sleeve, and oscillation wave is derived from the beating of vascular wall, measures The reference point of oscillation wave can measure blood pressure data, including systolic pressure (PS), diastolic pressure (PD) and mean pressure (PM).Vibration measuring method obtains The method for obtaining pulsatile motion wave can obtain pulsatile motion wave by measurement and obtain blood pressure number by microphone and pressure sensor According to.For under some special application scenarios, blood pressure data can also be obtained by way of invasive measurement.Such as weight Some patients in the ward disease intensive care group (ICU) are monitored, so that it may by being directly intubated in artery, by the another of intubation One end is connected to the real-time acquisition that blood pressure data is realized in the pressure detecting system for filling liquid sterilized.This invasive monitoring The advantages of method includes：Blood pressure size can be shown in real time, and can show continuous blood pressure waveform；In low blood pressure State can have accurate reading；The patient comfort of long-term record gets a promotion, and avoids inflation deflation for a long time in non-invasive measurement Caused wound；More information can be extracted, include that can extrapolate Vascular capacitance etc. from the form of blood pressure waveform.

Breath data：Respiration measurement is the pith of lung kinetic energy inspection.Patient monitor is exhaled by measuring respiratory wave to measure Frequency (beat/min) is inhaled to get to breath data.The measurement of respiratory rate can directly measure respiratory air flow by thermistor This variation is transformed into voltage signal by temperature change by bridge circuit；Impedance method can also be used to measure respiratory rate, because For respiratory movement when, wall of the chest muscle alternation tension and relaxation, thorax alternately deforms, and the electrical impedance of injected organism tissue also alternately changes therewith.It surveys The various ways such as bridge method, modulation method, constant pressure source method and Method of constant flow source can be used in the variation of amount respiratory impedance value.In patient monitor In, respiratory impedance electrode can also be shared with electrocardioelectrode, can detect respiratory impedance variation and breathing when detecting electrocardiosignal simultaneously Frequency.

Blood oxygen saturation data：Blood oxygen saturation is to weigh the important parameter for the ability that blood of human body carries oxygen.Blood oxygen is full Transmission beam method (or bounce technique) dual wavelength (feux rouges R and infrared light IR) photoelectric detecting technology may be used in measurement with degree, and detection is red The ratio between alternating component caused by the light absorption of light and infrared light by arterial blood and non-pulsating tissue (epidermis, muscle, venous blood Deng) stabilization component (direct current) value for causing light absorption, by can be calculated oximetry value SpO2, i.e. blood oxygen saturation number According to.Since the pulsation rule of photosignal is consistent with the rule of heartbeat, so also can be simultaneously according to the period of detecting signal Determine pulse data.

Temperature data：Body temperature is the important indicator for understanding life state.The measurement of body temperature uses the temperature-sensitive of negative temperature coefficient Resistance is as temperature sensor, using electric bridge as detection circuit.Integrated thermometric may be used in we in specific application Circuit measures to obtain temperature data.Also the temperature measurement circuit that twice or more can be used, measures the temperature difference pair of two different parts Measured value is modified.Body surface probe and body cavity probe can also be used, guards body surface and cavity temperature respectively.It is special at some Application in, in order to avoid cross infection, using infrared Infrared Technique can also put question to the monitoring of data. In patient monitor, we set temperature measurement accuracy at 0.1 DEG C, to there is faster thermometric to respond.

In the present invention, we can carry out sign prison using multi-parameter monitor by the above method to measurand Data acquisition is protected, the sign monitoring data of measurand is obtained.

After obtaining sign monitoring data, so that it may to carry out corresponding data identification and exception according to sign monitoring data Judge, and be directed to different exceptions, generates corresponding alarm, play the role of effective monitoring.

In front it has been noted that cardiac electrical monitoring is relative to vital signs such as other blood pressures, blood oxygen, pulse, breathing, body temperature The monitoring of index is increasingly complex, therefore ECG data is used different from other sign monitoring datas in the present invention Processing method, the specific identification for using the electrocardiogram automatic analysis method based on artificial intelligence self study to carry out ECG data, Processing and abnormal judgement.

Following steps 120- steps 140 are the processing procedures for ECG data, and step 150 and step 160 are to be directed to Other sign monitoring datas such as pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data process Journey.Two processing procedures can synchronize execution, without the limitation of priority execution sequence.

Step 120, wave group feature recognition is carried out to ECG data, the characteristic signal of ECG data is obtained, according to spy Reference number carries out beat classification to ECG data, and beat classification information is obtained in conjunction with electrocardiogram basic law reference data, and Generate ECG events data；

Specifically, the processing procedure of the ECG data of the present invention, uses the electrocardiogram based on artificial intelligence self study Automatic analysis method is realized based on artificial intelligence convolutional neural networks (CNN) model.CNN models are in deep learning Supervised learning method, be exactly multi-layer network (hidden layer hidden layer) connection structure of a simulative neural network, Input signal passes sequentially through each hidden layer, wherein carry out a series of complex Mathematical treatment (Convolution convolution, The ponds Pooling, Regularization regularizations, prevent over-fitting, Dropout from temporarily abandoning, Activation activation, one As use Relu activation primitives), some features of object to be identified are successively automatically taken out, then using these features as defeated Enter to be transmitted to higher leveled hidden layer again and be calculated, to the last several layers of full articulamentum (Full Connection) reconstruct Entire signal carries out logic (logistics) using Softmax functions and returns, reaches the classification of multiple target.

CNN belongs to the supervised learning method in artificial intelligence, and in the training stage, input signal is by multiple hidden layers Reason reaches last full articulamentum, and the classification results that softmax logistic regressions obtain, (label is marked with known classification results Label) between can be there are one error, a core concept of deep learning is exactly by a large amount of sample iteration come constantly minimum Change this error, to which the parameter for connecting each hidden layer neuron be calculated.This process generally requires construction one especially Loss function (cost function), utilize the gradient descent algorithm and error backpropagation algorithm of nonlinear optimization (backpropagation algorithm, BP), fast and effeciently minimization entire depth (number of plies of hidden layer) and range All Connecting quantities in (dimension of feature) all sufficiently complex neural network structure.

The data that needs identify are input to training pattern by deep learning, by the first hidden layer, the second hidden layer, third Hidden layer is finally output recognition result.

In the present invention, wave group feature recognition, disturbance ecology, beat classification etc. are carried out to ECG data and is all based on people The training pattern of work intelligent self-learning is exported as a result, analyze speed is fast, and order of accuarcy is high.

Specifically, carrying out wave group feature recognition to ECG data, the characteristic signal of ECG data is obtained, according to feature Signal carries out beat classification to ECG data, obtains beat classification information in conjunction with electrocardiogram basic law reference data, and raw It can specifically be realized by following steps as shown in Figure 2 at electrocardiogram event data.The processing procedure be it is real-time, therefore Handling result can be obtained in real time rapidly.

Step 121, the data format of ECG data is converted into preset standard data format by resampling, and to turning The ECG data of preset standard data format after changing carries out first and is filtered；

Specifically, the format adaptation of ECG data is read, there is different readings to realize different equipment, after reading, Need adjust baseline, according to gain conversions at millivolt data.By data resampling, converting data to whole process can be handled Sample frequency.Then high frequency is removed by filtering, it is accurate to improve artificial intelligence analysis for the noise jamming and baseline drift of low frequency Rate.By treated, ECG data is preserved with preset standard data format.

It is solved by this step different using different leads, the difference of sample frequency and transmission data format, Yi Jitong Cross digital signal filter removal high frequency, the noise jamming and baseline drift of low frequency.

High-pass filter, low-pass filter and medium filtering can be respectively adopted in digital signal filter, Hz noise, flesh Electrical interference and baseline drift interference are eliminated, and the influence to subsequent analysis is avoided.

More specifically, low pass, the progress zero phase-shift filtering of high pass Butterworth filter may be used, to remove baseline drift And High-frequency Interference, retain effective electrocardiosignal；Medium filtering can then utilize data point electricity in the sliding window of preset duration The median of pressure amplitude value substitutes the amplitude of window center sequence.The baseline drift of low frequency can be removed.

Step 122, the ECG data after being filtered to first carries out heartbeat detection process, identifies ECG data packet The multiple heartbeat data included；

Each heartbeat data correspond to a cardiac cycle, including when corresponding P waves, QRS complex, the amplitude of T waves and start-stop Between data.Heartbeat detection in this step is made of two processes, when signal processing, after described first is filtered ECG data in extract QRS complex characteristic spectra；Second is that when determining the generation of QRS complex by the way that rational threshold value is arranged Between.Generally can include P waves, QRS complex, T wave components and noise contribution in electrocardiogram.The frequency range of general QRS complex 5 between 20Hz, QRS wave group congruences can be proposed by a bandpass filter within this range.However P waves, T waves The frequency range and QRS complex frequency range of frequency range and noise overlap, therefore can not be gone completely by the method for signal processing Unless the signal of QRS complex.Therefore it needs to extract QRS complex position from signal envelope by the way that rational threshold value is arranged.Tool The detection process of body is a kind of process based on peak detection.Threshold decision is carried out for each peak value sequence in signal, is surpassed Enter QRS complex when crossing threshold value and judge flow, carries out the detection of more features, such as phase, form etc. between RR.

Multi-parameter monitor often carry out non-volatile recording, during heartbeat signal amplitude and frequency at every moment All changing, and under morbid state, this characteristic can show stronger.When carrying out threshold value setting, need according to data Feature dynamically carries out adjusting thresholds in the situation of change of time domain.In order to improve the accuracy rate and positive rate of detection, QRS complex inspection It surveys the big mode for mostly using double width degree threshold value binding time threshold value to carry out, there is high threshold higher positive rate, Low threshold to have Higher Sensitivity rate, the phase is more than certain time threshold value between RR, is detected using Low threshold, and missing inspection situation is reduced.And low threshold Value is easy to be influenced by T waves, myoelectricity noise since threshold value is relatively low, be easy to cause more inspections, therefore preferentially carried out using high threshold Detection.

All there is lead parameter for the heartbeat data of different leads, to characterize the heart which lead is the heartbeat data be It fights data.Therefore also the information that source determines its lead can be transmitted according to it while obtaining ECG data, it will Lead parameter of this information as heartbeat data.

Step 123, the detection confidence level of each heartbeat is determined according to heartbeat data；

Specifically, confidence calculations module is during heartbeat detects, according in the phase between the amplitude and RR of QRS complex The amplitude proportional of noise signal can provide the estimated value that confidence level is detected for QRS complex.

Step 124, disturbance ecology is carried out to heartbeat data according to two disaggregated model of disturbance ecology, whether obtains heartbeat data There are interfering noises, and a probability value for judging interfering noise；

Because multi-parameter monitor is easily influenced interference phenomenon occur during non-volatile recording by a variety of, lead to acquisition Heartbeat data invalid or inaccuracy cannot correctly reflect the situation of testee, while also increase diagnosis difficulty and workload； And interference data are also the principal element for causing intellectual analysis tool not work effectively.Therefore, outer signals are interfered and is dropped It is particularly important to minimum.

This step has that precision is high based on using deep learning algorithm as the end-to-end two Classification and Identifications model of core, extensive The strong feature of performance, can efficiently solve that electrode slice, which falls off, the main interference source such as motion artifacts and electrostatic interference generates disturbs Dynamic problem, overcome traditional algorithm because interference data variation it is various it is irregular caused by recognition effect difference problem.

It can specifically realize by the following method：

Step A carries out disturbance ecology to heartbeat data using two disaggregated model of disturbance ecology；

Step B identifies in heartbeat data, heartbeat interval be more than or equal to it is default between phase decision threshold data slot；

Step C, the data slot that phase decision threshold between presetting is more than or equal to heartbeat interval carry out abnormal signal judgement, really Whether fixed is abnormal signal；

Wherein, the identification of abnormal signal mainly include whether to fall off for electrode slice, low-voltage situations such as.

Step D, then with preset time width, is determined sliding in data slot if not abnormal signal according to setting duration The initial data point and termination data point of dynamic sampling, and sliding sampling is carried out to data slot by initial data point, until end Only until data point, multiple sampled data sections are obtained；

Step E carries out disturbance ecology to each sampled data section.

Above-mentioned steps A-E is illustrated with a specific example.That the heart rate of each lead is set according to this One data volume carries out cutting sampling, is then separately input to two disaggregated model of disturbance ecology and classifies, and obtains disturbance ecology knot A probability value for fruit and corresponding result；It is more than or equal to 2 seconds heartbeat data to heartbeat interval, first judges whether it is that signal overflows Go out, low-voltage, electrode delamination；If not the above situation, just according to the first data volume, since the heartbeat of the left side, to fight continuity It is not overlapped sliding sampling with the first data volume, is identified.

Input can be the first data volume heartbeat data of any lead, then two disaggregated model of disturbance ecology be used to carry out Whether classification, directly output are the classification results interfered, and acquisition result is fast, and accuracy is high, and stability is good, can be carried for subsequent analysis For more effective good data.

Because data is interfered often as caused by the effect of external disturbance factor, mainly to have electrode slice to fall off, low electricity Situations such as pressure, electrostatic interference and motion artifacts, the interference data that not only different disturbing sources generate are different, and identical disturbing source produces Raw interference data are also varied；Simultaneously in view of although interference data diversity cloth is wider, the difference with normal data It is different very big, so being also to ensure diversity as far as possible, while moving window being taken to slide when collecting the training data of interference Sampling increases the diversity of interference data as far as possible, so that model is more robust to interference data, even if following interference data Any interference different from the past, but compared to normal data, can also be more than normal data with the similarity of interference, to make Model Identification interferes the ability enhancing of data.

Two disaggregated model of disturbance ecology used in this step can with as shown in figure 3, network first use level 2 volume lamination, Convolution kernel size is 1x5, and a maximum value pond is added after every layer.Convolution kernel number often passes through primary maximum pond since 128 Change layer, convolution kernel number is double.It is two full articulamentums and a softmax grader after convolutional layer.Due to the model Number of classifying is 2, so there are two output units by softmax, respective classes is corresponding in turn to, using cross entropy as loss function.

Training for the model, we use the data slot accurately marked from 300,000 patients nearly 4,000,000.Mark Note is divided into two classes：Normal ECG signal either has the ECG signal segment significantly interfered with.We pass through customized development Tool carries out segment mark, then preserves interference fragment information with self-defined standard data format.

In training process, carries out tens repeating queries using two GPU servers and train.In a specific example, adopt Sample rate is 200Hz, and data length is a segment D [300] of 300 ecg voltage values (millivolt), and input data is： InputData (i, j), wherein i is i-th of lead, and j is j-th of segment D of lead i.Input data all by breaing up at random Just start to train, ensure that training process restrains, meanwhile, too many sample is collected in control from the ECG data of the same patient This, improves the generalization ability of model, both the accuracy rate under real scene.After training convergence, 1,000,000 independent test datas are used It is tested, accuracy rate can reach 99.3%.Separately there is specific test data such as the following table 1.

	Interference	Normally
			Sensitivity rate (Sensitivity)	99.14%	99.32%
Positive prediction rate (Positive Predicitivity)	96.44%	99.84%

Table 1

Step 125, the validity of heartbeat data is determined according to detection confidence level, also, according to the effective heart rate of determination According to lead parameter and heartbeat data, result based on disturbance ecology and time rule, which merge, generates heart beat time sequence data, And heartbeat is generated according to heart beat time sequence data and analyzes data；

Specifically, due to the complexity of ECG signal and each lead may by different degrees of interference effect, There can be the case where more inspections and missing inspection by single lead detection heartbeat, different leads detect the time representation number of heartbeat result According to not being aligned, so needing to merge the heartbeat data of all leads according to disturbance ecology result and time rule, life At a complete heart beat time sequence data, the time representation data of unified all lead heartbeat data.Wherein, time representation Data are for indicating temporal information of each data point on electrocardiographic data signals time shaft.When according to this unified heartbeat Between sequence data can use the threshold values that pre-sets when subsequent analysis calculates, each lead heartbeat data are cut It cuts, the heartbeat to generate each lead that concrete analysis needs analyzes data.

The heartbeat data of above-mentioned each lead need to be determined according to the detection confidence level obtained in step 123 before merging The validity of heartbeat data.

Specifically, the heartbeat data merging process that lead heartbeat merging module executes is as follows：According to electrocardiogram basic law The refractory period of reference data obtains the time representation data combination of different lead heartbeat data, abandons the larger heartbeat of its large deviations Data, voting for generating to the combination of above-mentioned time representation data merges heart beat locations, when will merge heart beat locations addition merging heartbeat Between sequence, be moved to next group of pending heartbeat data, cycle executes until completing the merging of all heartbeat data.

Wherein, electrocardiographic activity refractory period can be preferably between 200 milliseconds to 280 milliseconds.The different lead hearts of acquisition The time representation data combination for data of fighting should meet the following conditions：Each lead is most in the time representation data combination of heartbeat data Include the time representation data of a heartbeat data more.When voting the time representation data combination of heartbeat data, make Occupy the percentage of effect lead number with the lead number of detection heartbeat data to determine；If the time representation data of heartbeat data correspond to Think that the lead is invalid lead to this heartbeat data when being low-voltage section, interference section and electrode delamination in the position of lead. When calculating merging heartbeat specific location, the time representation statistical average that heartbeat data may be used obtains.In merging process, This method avoids wrong merging provided with a refractory period.

In this step, a unified heart beat time sequence data is exported by union operation.The step simultaneously can Reduce the more inspection rates and omission factor of heartbeat, the effective susceptibility and positive prediction rate for improving heartbeat detection.

Step 126, the feature that according to beat classification model heartbeat analysis data are carried out with amplitude and time representation data carries It takes and analyzes, obtain a classification information of heartbeat analysis data；

Specifically, Different Dynamic ecg equipment exists in signal measurement, acquisition or leads of output etc. Difference, therefore simple single lead sorting technique or multi-lead sorting technique can be used as the case may be.It is more Lead sorting technique includes two kinds of lead ballot Decision Classfication method and lead synchronization association sorting technique again.Lead ballot decision Sorting technique is that the heartbeat analysis data based on each lead carry out lead independent sorting, then result is voted for merging and determines classification knot The ballot decision-making technique of fruit；Lead synchronization association sorting technique then synchronizes association using the heartbeat analysis data to each lead The method of analysis.Single lead sorting technique is exactly to analyze data to the heartbeat of single lead equipment, directly uses Correspondence lead model Classify, decision process of not voting.Several sorting techniques described above are illustrated respectively below.

Single lead sorting technique includes：

According to heart beat time sequence data, single lead heartbeat data are subjected to the heartbeat analysis number that cutting generates single lead According to, and the beat classification model for being input to the correspondence lead that training obtains carries out the feature extraction of amplitude and time representation data And analysis, obtain a classification information of single lead.

Lead ballot Decision Classfication method can specifically include：

The first step, according to heart beat time sequence data, each lead heartbeat data are cut, to generate each lead Heartbeat analyzes data；

Second step, according to the obtained corresponding beat classification model of each lead of training to the heartbeat of each lead analyze data into The feature extraction and analysis of row amplitude and time representation data, obtain the classification information of each lead；

Third step carries out classification ballot decision calculating according to the classification information and lead weighted value of each lead with reference to coefficient, Obtain a classification information.Specifically, lead weighted value with reference to coefficient is obtained based on electrocardiogram (ECG) data bayesian statistical analysis To each lead to the ballot weight coefficient of different beat classifications.

Lead synchronization association sorting technique can specifically include：

According to heart beat time sequence data, each lead heartbeat data are cut, to generate the heartbeat point of each lead Analyse data；Then the multi-lead synchronization association disaggregated model obtained according to training synchronizes the heartbeat analysis data of each lead The feature extraction and analysis of amplitude and time representation data obtain a classification information of heartbeat analysis data.

The synchronization association sorting technique input of heartbeat data is all leads of Holter equipment, according to heartbeat point The unified heartbeat site of data is analysed, the data point of same position and certain length in each lead is intercepted, synchronous transport is given by instructing Experienced artificial intelligence deep learning model carries out calculating analysis, and output is that each heart beat locations point has considered all lead hearts The accurate beat classification of electrical picture signal feature and the heartbeat rhythm of the heart feature of forward-backward correlation in time.

This method has fully considered that electrocardiogram difference leads are actually to measure cardiac electric signals different The information flow that cardiac electric axis vector direction is transmitted, the various dimensions numerical characteristic that ECG signal is transmitted over time and space carry out Comprehensive analysis significantly improves conventional method and relies solely on single lead and independently analyze, result is then summarized progress Statistical ballot mode and be easier the defect of classification error obtained, greatly improve the accuracy rate of beat classification.

The beat classification model used in this step can be with as shown in figure 4, be specifically as follows based on artificial intelligence depth The end-to-end multi-tag disaggregated model that the models such as the convolutional neural networks AlexNet, VGG16, Inception of habit inspire.Specifically Say, the network of the model is one 7 layers of convolutional network, and an activation primitive is closely followed after each convolution.First layer is two The convolutional layer of a different scale is six convolutional layers later.The convolution kernel of seven layers of convolution is 96,256,256,384,384 respectively, 384,256.In addition to first layer convolution kernel is 5 and 11 respectively there are two scale, other layer of convolution kernel scale is 5.Third, five, six, It is pond layer after seven layers of convolutional layer.Finally follow two full articulamentums.

Beat classification model in this step, we use training set include 300,000 patients 17,000,000 data samples into Row training.These samples are that the requirement diagnosed according to ambulatory ECG analysis carries out accurately mark generation, mark to data Primarily directed to common arrhythmia cordis, block and ST sections and the change of T waves can meet the model instruction of different application scene Practice.The information of mark is specifically preserved with preset standard data format.In the pretreatment of training data, to increase the extensive of model Ability has done small size sliding for the less classification of sample size and has carried out amplification data, has been exactly using each heartbeat as base specifically Plinth, it is 2 times mobile according to a fixed step size (such as 10-50 data point), it can thus increase by 2 times of data, improve to this The recognition accuracy of the fewer classification samples of a little data volumes.It is verified by actual result, generalization ability is also improved.

Having used two GPU servers to carry out tens repeating queries training after training convergence in a hands-on process makes It is tested with 5,000,000 independent test datas, accuracy rate can reach 91.92%.

Wherein, the length of the interception of training data can be 1 second to 10 seconds.For example sample rate is 200Hz, is with 2.5s Sampling length, the data length of acquirement are a segment D [500] of 500 ecg voltage values (millivolt), and input data is： InputData (i, j), wherein i is i-th of lead, and j is j-th of segment D of lead i.Input data all by breaing up at random Just start to train, ensure that training process restrains, meanwhile, too many sample is collected in control from the ECG data of the same patient This, improves the generalization ability of model, both the accuracy rate under real scene.It is synchronous to input all corresponding of leads when training Segment data D, according to the multichannel analysis method of image analysis, to multiple Spatial Dimensions (different cardiac electric axis of each time location Vector) leads synchronize study, to obtain a classification results more more accurate than conventional algorithm.

Step 127, ST sections are input to the heartbeat of the specific heartbeat in classification information result analysis data and T waves changes Varying model is identified, and determines ST sections and T wave evaluation informations；

ST sections are specially the corresponding lead position to change with T waves ST section of heartbeat analysis data with T wave evaluation informations Information.Because clinical diagnosis requires to navigate to specific lead for the change of ST sections and T waves.

Wherein, the specific heartbeat data of a classification information refer to that other may change comprising sinus property heartbeat (N) and comprising ST The heartbeat of the heart beat type of change analyzes data.

ST sections and T waves change lead locating module by the specific heartbeat data of a classification information, according to each lead according to It is secondary to be input to the artificial intelligence deep learning training patterns that one is ST sections of identification and T waves change, calculating analysis is carried out, output As a result illustrate whether the feature of lead segment meets the conclusion of ST sections and the change of T waves, be assured that ST sections and the change of T waves in this way The information in those specific leads occurred, i.e. ST sections and T wave evaluation informations.Specific method can be：A classification information In the result is that each lead heartbeat of sinus property heartbeat analyzes data, input to ST section and T waves and change model, to sinus property heartbeat analysis number Judge according to identification one by one is carried out, to determine sinus property heartbeat analysis data with the presence or absence of ST section and T waves change feature and generation Specific lead location information, determines ST sections and T wave evaluation informations.

The ST sections and T waves used in this step changes model can be with as shown in figure 5, be specifically as follows based on artificial intelligence depth Spend the end-to-end disaggregated model that the models such as the convolutional neural networks AlexNet and VGG16 of study inspire.Concretely, the model It is one 7 layers of network, model contains 7 convolution, 5 pondizations and 2 full connections.The convolution kernel that convolution uses is 1x5, The number of filter of every layer of convolution is different.It is 96 that level 1 volume, which accumulates number of filter,；Level 2 volume accumulates and the 3rd layer of convolution connects With number of filter 256；The 5th layer of convolution of 4th layer of convolution sum is used in conjunction, number of filter 384；6th layer of convolution filter Number is 384；7th layer of convolution filter number is 256；It is pond after 1st, 3,5,6,7 layer of convolutional layer.Two are followed by entirely to connect It connects, result is also finally divided by two classes using Softmax graders.In order to increase the non-linear of model, data more higher-dimension is extracted The feature of degree, therefore the pattern being used in conjunction using two convolution.

Because accounting of the heartbeat in all heartbeats with ST section and the change of T waves is relatively low, training data in order to balance The harmony of diversity and each categorical data amount chooses the training number for changing without ST sections and T waves and having ST sections and the change of T waves It is about 2 according to ratio:1, it ensure that model good generalization ability and does not occur more to training data accounting in assorting process A kind of tendentiousness.Since the form of heartbeat is varied, the form of Different Individual performance is not quite similar, therefore, for model More preferably estimate the distribution of each classification, can effectively extract feature, training sample is from all ages and classes, weight, gender and residence Individual is collected；In addition, because ECG data of the single individual within the same period is often that height is similar, in order to Overlearning is avoided, when obtaining the data of single individual, a small amount of sample in different time periods is randomly selected from all data； Finally, due to that there are inter-individual differences is big for the heartbeat form of patient, and the feature that a internal similarity is high, thus instructed dividing Practice, test set when, different patients is assigned to different data sets, avoid the data of same individual and meanwhile appear in training set and In test set, gained model test results ensure that the reliability and universality of model closest to true application scenarios as a result,.

Step 128, according to heart beat time sequence data, P waves is carried out to heartbeat analysis data and T wave characteristics detect, are determined The detailed features information of P waves and T waves in each heartbeat；

Specifically, detailed features information includes amplitude, direction, form and the data of beginning and ending time；To heartbeat signal In analysis, the various features in P waves, T waves and QRS wave are also the important evidence in ecg analysis.

In P waves and T wave characteristic detection modules, by calculating cutting for cut-off position and P waves and T waves in QRS complex Branch position, to extract the various features in P waves, T waves and QRS complex.Can be detected respectively by QRS complex cut-off, Single lead PT detection algorithms and multi-lead PT detection algorithms are realized.

QRS complex cut-off detects：According to the QRS complex section power maximum point of QRS complex detection algorithm offer and rise Stop finds the R points of QRS complex in single lead, R ' points, S points and S ' points.When there are multi-lead data, calculate each The median of cut-off is as last cut-off position.

Single P wave in lead, T wave detection algorithms：P waves and T waves are low with respect to QRS complex amplitude, signal is gentle, are easy to be submerged in low It is the difficult point in detection in frequency noise.This method is according to QRS complex detection as a result, eliminating QRS complex to low frequency band After influence, third filtering is carried out to signal using low-pass filter, PT wave relative amplitudes is made to increase.Pass through peak detection later Method finds T waves between two QRS complexes.Because T waves are the wave groups that ventricular bipolar generates, therefore between T waves and QRS complex Relationship when having specific lock.On the basis of the QRS complex detected, in each QRS complex takes to the phase between next QRS complex Point (for example being limited in the range after first QRS complex between 400ms to 600ms) detects end point as T waves, in this section It is interior to choose maximum peak as T waves.The maximum peak of selecting range is P waves in remaining peak value again.Simultaneously also according to P waves and T The peak value and position data of wave determine direction and the morphological feature of P waves and T waves.Preferably, the cutoff frequency setting of low-pass filtering Between 10-30Hz.

Multi-lead P waves, T wave detection algorithms：In the case of multi-lead, due to the generation time phase of each wave in heartbeat Together, spatial distribution is different, and the distribution of the time and space of noise is different, can carry out the detection of P, T wave by tracing to the source algorithm.It is first QRS complex Processing for removing first is carried out to signal and third filtering is carried out to remove interference to signal using low-pass filter.Later Each independent element in original waveform is calculated by independent composition analysis algorithm.In each independent element isolated, according to Distribution characteristics according to its peak value and QRS complex position choose corresponding ingredient as P waves and T wave signals, while determining P waves Direction with T waves and morphological feature.

Step 129, data are analyzed under a classification information according to electrocardiogram basic law reference data, P waves to heartbeat Secondary classification processing is carried out with T wave evaluation informations with the detailed features information and ST section of T waves, obtains beat classification information；With And analysis matching is carried out to beat classification information, generate ECG events data.

Specifically, electrocardiogram basic law reference data is followed in authoritative electrocardiogram textbook to cardiac muscle cell's electro physiology The description of the primitive rule of activity and electrocardiogram clinical diagnosis generates, such as time interval minimum between two heartbeats, P waves with The minimum interval etc. of R waves, for a classification information after beat classification to be finely divided again；Main basis is RR between heartbeat Between the medicine conspicuousness of phase and different heartbeat signal in each lead；Heartbeat auditing module is referred to according to electrocardiogram basic law Data combine the centainly Classification and Identification of continuous multiple heartbeats analysis data and the detailed features information of P waves and T waves by the room property heart Classification of fighting splits thinner beat classification, including：Ventricular premature beat (V), room escape (VE), acceleration ventricular premature beat (VT), will Supraventricular class heartbeat is subdivided into supraventricular premature beat (S), atrial escape (SE), junctional escape beat (JE) and room acceleration premature beat (AT) etc..

In addition, handled by secondary classification, can also correct occur in a subseries do not meet electrocardiogram basic law The wrong Classification and Identification of reference data.Beat classification after subdivision is subjected to pattern according to electrocardiogram basic law reference data Match, find the Classification and Identification for not meeting electrocardiogram basic law reference data, is corrected as according to phase between RR and front and back class indication Rational classification.

Specifically, being handled by secondary classification, a variety of beat classifications can be exported, such as：It is normal sinus heartbeat (N), complete Full property right bundle branch block (N_CRB), completeness left bundle branch block (N_CLB), intraventricular block (N_VB), first degree A-V block (N_B1), it is pre- swash (N_PS), ventricular premature beat (V), room escape (VE), acceleration ventricular premature beat (VT), supraventricular premature beat (S), The classification knots such as atrial fibrillation (AF), artifact (A) are flutterred in atrial escape (SE), junctional escape beat (JE), acceleration atrial premature beats (AT), room Fruit.

By this step, the calculating of basal heart rate parameter can also be completed.The hrv parameter of wherein basic calculation includes：RR Between the parameters such as phase, heart rate, QT times, QTc times.

It then, can be with according to heartbeat secondary classification as a result, carry out pattern match according to electrocardiogram basic law reference data These the following typical ECG events of classification corresponding to ECG events data are obtained, including but not limited to：

Supraventricular premature beat

Supraventricular premature beat is pairs of

Supraventricular premature beat bigeminy

Supraventricular premature beat trigeminy

Atrial escape

The atrial escape rhythm of the heart

Junctional escape beat

The junctional escape beat rhythm of the heart

Non- paroxysmal supraventricular tachycardia

Most fast supraventricular tachycardia

Longest supraventricular tachycardia

Supraventricular tachycardia

Short battle array supraventricular tachycardia

Auricular flutter-auricular fibrillation

Ventricular premature beat

Ventricular premature beat is pairs of

Ventricular premature beat bigeminy

Ventricular premature beat trigeminy

Room escape

Ventricular escape rhythm

Accelerated idioventricular rhythm

Most fast Ventricular Tachycardia

Longest Ventricular Tachycardia

Ventricular Tachycardia

Burst ventricular tachycardia

Two degree of I type sinoatrial blocks

Two degree of II type sinoatrial blocks

First degree A-V block

Two degree of I type atrioventricular blocks

Two degree of II type atrioventricular blocks

Two degree of II types (2：1) atrioventricular block

Advanced A-V block

Completeness left bundle branch block

Complete right bundle branch block

Intraventricular block

Pre-excitation syndrome

ST sections and the change of T waves

Most Long RR interval

Step 130, it determines corresponding ECG events information according to ECG events data, and determines ECG events letter Whether breath is preset anomalous ecg event information；

Specifically, according to ECG events data, it may be determined that the ECG events information of corresponding different ECG events, For example can be the event information of corresponding above-mentioned ECG events, can also be further to be summarized based on above-mentioned ECG events Event information, for example, by various block event classifications be block event information under anomalous event.

It is stored with preset anomalous ecg event information in multi-parameter monitor, preset anomalous ecg event information, The corresponding event information of the ECG events of generation alarm is as needed, that is, needs to generate the improper electrocardio alarmed The event information of figure event.These information pass through default settings, can be stored in multi-parameter monitor local, can also be stored in In the system of multi-parameter monitor access or the memory of network, it can be acquired by multi-parameter monitor.

When electrocardiogram event data determines that corresponding ECG events information is preset anomalous ecg event information, hold Row step 140.

If ECG events data determine that corresponding ECG events information is not preset anomalous ecg event information, Then explanation does not have the abnormal electrocardiogram situation for needing to generate alarm, continues for the monitoring for carrying out step 110.

Step 140, the first warning message is exported；

Specifically, when electrocardiogram event data determines that corresponding ECG events information is believed for preset anomalous ecg event When breath, generates and export the first warning message.

First warning message refers to just the warning message of anomalous ecg event, including anomalous ecg event information and report Alert temporal information.Therefore the first warning message is generated according to anomalous ecg event information and time of fire alarming information.When alarm Between information can be further anomalous ecg event occur time information, i.e., the time obtained from time attribute information Information can also be to determine that ECG events information is preset anomalous ecg event information after handling ECG data System time information.

To carry out different parameters differentiation, anomalous ecg event information has corresponding project information, i.e. electrocardio project, can Show that the anomalous event is cardiac electrical event.

The present invention can generate the type of alarm that warning message exports：

1, sinus rate event：

A) nodal tachycardia

B) sinus bradycardia

2, supraventricular tachycardia

A) atrial tachycardia

B) room is flutterred

C) atrial fibrillation

D) chamber is turned back

3, Ventricular Tachycardia

A) pure Ventricular Tachycardia

B) multiform ventricular tachycardia

C) bidirectional ventricular tachycardia

D) de pointes

E) premature beat Ventricular Tachycardia

F) room is flutterred

G) room is quivered

4, ST-T sections of changes

A) R-on-t ventricular premature beat

B) R-on-P ventricular premature beat

C) roomy T waves are alternately present

5, block

A) high two degree of blocks

B) Third degree heart block

It is above to realize the process that the Data Analysis Services of ECG data are exported to abnormal alarm.

Wherein, the output of the first warning message can locally export on the display of multi-parameter monitor, or by more Parameter patient monitor local printing exports, can also be by transmission of network to receiving terminal, for example work station or server-side (specifically can be with Such as mobile device), to meet different use demands.

Step 150, one in pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data is determined It is a or multiple with the presence or absence of the abnormal data beyond corresponding given threshold；

During real data is handled, step 150 can execute parallel with step 120-140, or arbitrary priority It executes, has no strict sequence therebetween.

Specifically, for pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data, Ke Yishe It is equipped with corresponding parameter threshold.The parameter threshold of each parameter can have different multigroup parameter thresholds, can be according to being supervised The actual conditions of survey person are selected.

Preferably, in the present invention it is possible to determine monitoring criteria data previously according to measurand before monitoring, then root Corresponding given threshold is determined according to monitoring criteria data.

For example, the setting of the parameter threshold for neonatal pulse data, breath data, than normal adult human than Height can select the parameter threshold being suitble to accordingly so that multi-parameter monitor can be fine as needed in practical applications Matching be monitored person, achieve the purpose that effectively monitor, accurately alarm.

When occur in above-mentioned sign monitoring data it is one or more exceed given threshold when, execute step 160；Otherwise after It is continuous to execute step 110, continue sign detection.

Step 160, other abnormal events informations are generated according to abnormal data, and exports the second warning message；

Specifically, when pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data have beyond phase When answering the data of setup parameter range, other abnormal events informations are generated beyond item according to specific.Wherein, other anomalous events Information has corresponding project information, and it is that the data of which project exception occur to be used to indicate.Such as pulse, blood pressure, breathing, Blood oxygen or body temperature etc..

Second warning message refers to just the warning message of above-mentioned other anomalous events in addition to anomalous ecg, including it His abnormal events information and time of fire alarming information.Therefore the second warning message is according to other abnormal events informations and time of fire alarming What information generated.Time of fire alarming information can be further the information for the time that other anomalous events occur, i.e., belong to from the time Property information in the temporal information that obtains, can also be to determine this after handling other sign datas in addition to ECG data There are the information of the system time of other anomalous events in a little monitoring data.

Likewise, the output of the second warning message can locally export on the display of multi-parameter monitor, Huo Zheyou Multi-parameter monitor local printing exports, and (can also specifically may be used by transmission of network to receiving terminal, such as work station or server-side With such as mobile device), to meet different use demands.

In addition, the multi-parameter monitoring data analysing method of the present invention, additionally it is possible to anomalous event occur front and back data into Row record, in order to be able to easily carry out anomaly analysis.

For this purpose, the multi-parameter monitoring data analysing method of the present invention can also be believed according to the time attribute of sign monitoring data Breath summarizes sign monitoring data, generates the time series data of sign monitoring data, and is stored.

When having determined anomalous event corresponding with preset anomalous ecg event information, believed according to anomalous event ECG data in the front and back preset period of time of the corresponding time attribute acquisition of information anomalous event time of origin of breath, generates different Normal event log data, while the related information of anomalous event record data and the first warning message is generated, and store.This is default The length of period can be set as needed, preferably 36 seconds in the present embodiment.

After exporting the first warning message, first can be shown in the user interface of multi-parameter monitor in list of thing column The record of warning message, when user clicks the record by the operable equipment such as touch screen or mouse, multi-parameter monitor connects Receive the access instruction of the first warning message；At this point, the first warning message is corresponded to according to the record being clicked, and according to exception The anomalous event that the related information inquiry of event log data and the first warning message gets the first alert information correlation records Data.

In the present solution, can also record data to anomalous event carries out analyzing processing, generation and output abnormality event report Accuse data.By data reporting output abnormality event and to the detailed description of anomalous event, it can specifically include but be not limited to Each anomaly parameter, time of origin, the analysis result etc. based on anomaly parameter, and data can be broadcast with patterned way Put, for example, ECG data it is graphical playback etc..

Likewise, when being generated for the second warning message, it can also in kind, before recording the generation of the second warning message Each parameter afterwards, easily to be analyzed and determined, details are not described herein again.

It further, can also be according to ECG data, pulse data, blood pressure data, breath data, blood oxygen saturation number Comprehensive consideration is carried out according to each parameter such as temperature data, alert event number is generated according to the first warning message and the second warning message According to then according to time of fire alarming information, carrying out output to alarm event data and show；Alarm event data includes sign monitoring number According to project information and corresponding anomalous ecg event information and/or other abnormal events informations.

Fig. 6 is a kind of structural schematic diagram of multi-parameter monitor provided in an embodiment of the present invention, which includes： Processor and memory.Memory can be connect by bus with processor.Memory can be nonvolatile storage, such as hard disk Driver and flash memory are stored with software program and device driver in memory.Software program is able to carry out implementation of the present invention The various functions for the above method that example provides；Device driver can be network and interface drive program.Processor is for holding Row software program, the software program are performed, and can realize method provided in an embodiment of the present invention.

The data patient monitor further preferably include output equipment, can be specifically display, printer or network interface Equipment etc., to the display, output, transmission etc. of data.

It should be noted that the embodiment of the present invention additionally provides a kind of computer readable storage medium.This is computer-readable It is stored with computer program on storage medium, when which is executed by processor, can realize that the embodiment of the present invention carries The method of confession.

The embodiment of the present invention additionally provides a kind of computer program product including instruction.When the computer program product exists When being run on computer so that processor executes the above method.

Multi-parameter monitoring data analysing method and multi-parameter monitor provided in an embodiment of the present invention, can supervise multi-parameter It protects the sign datas such as electrocardio, blood pressure, blood oxygen, pulse, breathing, the body temperature of instrument monitoring and carries out automatic, quick, complete analysis, it is right The anomaly parameter and the two of abnormal electrocardio state, other every signs combine and provide early warning, and can reduce interference fringe Report phenomenon by mistake.Accuracy of alarming is high, and the type of detectable exception type especially anomalous ecg is more, and can be according to instruction pair Electrocardiogram (ECG) data carries out dynamic playback output.The multi-parameter monitoring data analysing method and relevant multi-parameter monitor of the present invention It is applied widely, it has a good application prospect.

Professional should further appreciate that, described in conjunction with the examples disclosed in the embodiments of the present disclosure Unit and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, hard in order to clearly demonstrate The interchangeability of part and software generally describes each exemplary composition and step according to function in the above description. These functions are implemented in hardware or software actually, depend on the specific application and design constraint of technical solution. Professional technician can use different methods to achieve the described function each specific application, but this realization It should not be considered as beyond the scope of the present invention.

The step of method described in conjunction with the examples disclosed in this document or algorithm, can use hardware, processor to execute The combination of software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field In any other form of storage medium well known to interior.

Above-described specific implementation mode has carried out further the purpose of the present invention, technical solution and advantageous effect It is described in detail, it should be understood that the foregoing is merely the specific implementation mode of the present invention, is not intended to limit the present invention Protection domain, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include Within protection scope of the present invention.

Claims (11)

1. a kind of multi-parameter monitoring data analysing method, which is characterized in that the method includes：

Sign monitoring data acquisition is carried out to measurand, obtains the sign monitoring data of the measurand；The sign prison Data are protected with time attribute information, the sign monitoring data includes：ECG data, pulse data, blood pressure data, breathing Data, blood oxygen saturation data and temperature data；

Wave group feature recognition is carried out to the ECG data, the characteristic signal of the ECG data is obtained, according to the spy Reference number carries out beat classification to the ECG data, and beat classification letter is obtained in conjunction with electrocardiogram basic law reference data Breath, and generate ECG events data；

Corresponding ECG events information is determined according to the ECG events data, and determines that the ECG events information is No is preset anomalous ecg event information；When for preset anomalous ecg event information, the first warning message is exported；It is described First warning message includes the anomalous ecg event information and time of fire alarming information；The anomalous ecg event information have pair The project information answered；And

Determine one or more of the pulse data, blood pressure data, breath data, blood oxygen saturation data and temperature data Other abnormal events informations are generated with the presence or absence of the abnormal data beyond corresponding given threshold, and according to the abnormal data； When beyond the given threshold, the second warning message is exported；Second warning message includes that other described anomalous events are believed Breath and time of fire alarming information；Other described abnormal events informations have corresponding project information.

2. multi-parameter monitoring data analysing method according to claim 1, which is characterized in that the method further includes：

The sign monitoring data is summarized according to the time attribute information of the sign monitoring data, generates the sign The time series data of monitoring data, and stored.

3. multi-parameter monitoring data analysing method according to claim 1, which is characterized in that the method further includes：

When for preset anomalous ecg event information, according to ECG data described in the time attribute acquisition of information to it is corresponding when Between front and back preset period of time in ECG data, generate anomalous event record data；

Generate the related information of anomalous event the record data and first warning message.

4. multi-parameter monitoring data analysing method according to claim 3, which is characterized in that in the first alarm of the output After information, the method further includes：

It receives and the access of first warning message is instructed；

The anomalous event record data are obtained according to the related information and are exported.

5. multi-parameter monitoring data analysing method according to claim 4, which is characterized in that the method further includes：

Analyzing processing is carried out to anomalous event record data, generates simultaneously output abnormality incident report data.

6. multi-parameter monitoring data analysing method according to claim 1, which is characterized in that the method further includes：

Alarm event data is generated according to first warning message and the second warning message；

According to the time of fire alarming information, output is carried out to the alarm event data and is shown；Wherein, the alarm event data Project information including sign monitoring data and corresponding anomalous ecg event information and/or other abnormal events informations.

7. multi-parameter monitoring data analysing method according to claim 1, which is characterized in that described to the electrocardiogram number According to wave group feature recognition is carried out, the characteristic signal of the ECG data is obtained, according to the characteristic signal to the electrocardiogram Data carry out beat classification, obtain beat classification information in conjunction with electrocardiogram basic law reference data, and generate ECG events Data specifically include：

The data format of the ECG data is converted into preset standard data format by resampling, and to transformed pre- It is filtered if the ECG data of standard data format carries out first；

ECG data after being filtered to described first carries out heartbeat detection process, identifies that the ECG data includes Multiple heartbeat data, each heartbeat data correspond to a cardiac cycle, include the amplitude of corresponding P waves, QRS complex, T waves With beginning and ending time data；

The detection confidence level of each heartbeat is determined according to the heartbeat data；

Disturbance ecology is carried out to the heartbeat data according to two disaggregated model of disturbance ecology, obtains heartbeat data with the presence or absence of interference Noise, and a probability value for judging interfering noise；

The validity of heartbeat data is determined according to the detection confidence level, also, according to the lead of the effective heartbeat data of determination Parameter and heartbeat data, result and time rule based on the disturbance ecology, which merge, generates heart beat time sequence data；According to The heart beat time sequence data generates heartbeat and analyzes data；

According to beat classification model heartbeat analysis data are carried out with the feature extraction and analysis of amplitude and time representation data, Obtain a classification information of the heartbeat analysis data；

To the heartbeat of the specific heartbeat in classification information result analysis data be input to ST section and T waves change model into Row identification, determines ST sections and T wave evaluation informations；

According to the heart beat time sequence data, P waves are carried out to heartbeat analysis data and T wave characteristics detect, are determined each The detailed features information of P waves and T waves in heartbeat, detailed features information include amplitude, direction, form and the data of beginning and ending time；

Data are analyzed under a classification information according to the electrocardiogram basic law reference data, described to the heartbeat The detailed features information and ST sections and the progress secondary classification processing of T wave evaluation informations of P waves and T waves, obtain beat classification Information；

Analysis matching is carried out to the beat classification information, generates the ECG events data.

8. multi-parameter monitoring data analysing method according to claim 1, which is characterized in that carrying out body to measurand Monitoring data acquisition is levied, before obtaining the sign monitoring data of the measurand, the method further includes：

Monitoring criteria data are determined according to the measurand；

The given threshold is determined according to the monitoring criteria data.

9. a kind of multi-parameter monitor, including memory and processor, which is characterized in that the memory is used to store program, The processor is for executing such as claim 1 to 8 any one of them method.

10. a kind of computer program product including instruction, when run on a computer so that computer executes such as right It is required that 1 to 8 any one of them method.

11. a kind of computer readable storage medium, including instruction, when described instruction is run on computers, make the calculating Machine is executed according to claim 1 to 8 any one of them method.