CN103720462A - Pulse wave signal analyzing method and device - Google Patents
Pulse wave signal analyzing method and device Download PDFInfo
- Publication number
- CN103720462A CN103720462A CN201310545727.3A CN201310545727A CN103720462A CN 103720462 A CN103720462 A CN 103720462A CN 201310545727 A CN201310545727 A CN 201310545727A CN 103720462 A CN103720462 A CN 103720462A
- Authority
- CN
- China
- Prior art keywords
- pulse wave
- wave signal
- signal
- circuit
- analog
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000006243 chemical reaction Methods 0.000 claims abstract description 29
- 238000007405 data analysis Methods 0.000 claims abstract description 24
- 238000005457 optimization Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 10
- 230000003068 static effect Effects 0.000 claims abstract description 8
- 238000004458 analytical method Methods 0.000 claims description 29
- 230000001133 acceleration Effects 0.000 claims description 24
- 230000003321 amplification Effects 0.000 claims description 6
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 6
- 238000013500 data storage Methods 0.000 claims description 4
- 238000007493 shaping process Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims description 2
- 230000002526 effect on cardiovascular system Effects 0.000 abstract description 5
- 230000009466 transformation Effects 0.000 abstract 1
- 230000008859 change Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 4
- 230000033001 locomotion Effects 0.000 description 4
- 210000000748 cardiovascular system Anatomy 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 230000000004 hemodynamic effect Effects 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 206010003210 Arteriosclerosis Diseases 0.000 description 2
- 206010044565 Tremor Diseases 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 208000011775 arteriosclerosis disease Diseases 0.000 description 2
- 230000000747 cardiac effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 231100000915 pathological change Toxicity 0.000 description 2
- 230000036285 pathological change Effects 0.000 description 2
- 230000001575 pathological effect Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 210000000707 wrist Anatomy 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000009084 cardiovascular function Effects 0.000 description 1
- 208000015606 cardiovascular system disease Diseases 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000010297 mechanical methods and process Methods 0.000 description 1
- 230000005226 mechanical processes and functions Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000010349 pulsation Effects 0.000 description 1
- 230000000241 respiratory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
Images
Landscapes
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention relates to a pulse wave signal analyzing method and device. The pulse wave signal analyzing method comprises the following steps of (a) receiving pulse wave signals; (b) carrying out optimization processing on the pulse wave signals to obtain improved signals; (c) carrying out analog-digital conversion on the optimized pulse wave signals; (d) identifying an accompany state when the pulse wave signals are collected, executing a step (f) when the accompany state is a static state, and executing a step (e) when the accompany state is a dynamic state; (e) carrying out Fourier transformation on the pulse wave signals processed through the analog-digital conversion; (f) carrying out pulse wave data analysis by combining a characteristic point method and an area graph method to obtain the characteristic quantity of the pulse wave signals. By means of the pulse wave signal analyzing method and device, the characteristic quantity related to cardiovascular indexes can be obtained in a processor executable mode.
Description
Technical Field
The present invention relates to a signal processing method, and more particularly, to a pulse wave signal analysis method and apparatus.
Background
Cardiovascular mortality accounts for the first significant disease in people who step into high living standards. The world health organization has listed it as the first killer that endangers human health in the 2l century. As with most diseases, early detection findings are of significant help for disease treatment.
The currently widely used cardiovascular function non-invasive detection methods include an ultrasonic cardiogram, an electrocardio-mechanical graph, an impedance cardiogram, an impedance differential wave graph and the like. However, these devices are expensive special medical instruments, and the detection techniques are complex and require special environmental conditions. In addition, these devices only give quantitative indications of the parameters and must be specifically determined by a medical professional. These deficiencies limit the widespread use of these devices by home users.
Due to the advantages of detection, such as no trauma, simple operation, stable performance, low cost, and the practicability thereof has been verified in clinical medical instruments, the pulse wave technology is becoming a cheap monitoring technology suitable for individual patients, middle-aged and elderly people, and sub-health patients in young groups and other family users.
Analysis of pulse waves has been found to be an effective means for detecting cardiovascular system diseases. During each cardiac cycle, the ejection activity of the heart causes periodic fluctuations in the pressure within the arterial wall, causing periodic pulsations in the wall of the vessel, called pulses. The pulse can propagate along the arterial system, called pulse wave. Each marker point (also called inflection point) and curve in the pulse map have clear hemodynamic rheological meanings. Fig. 8 illustrates a relationship between the degree of arteriosclerosis and the pulse wave characteristic value.
A number of clinical findings confirm the following facts: the shape, intensity, speed and rhythm of the pulse wave reflect many physiological and pathological characteristics of the cardiovascular system of the human body; a great deal of hemodynamic information is contained in the pulse wave; clinical manifestations precede abnormal or off-normal changes in hemodynamic parameters.
However, how to perform non-artificial automatic analysis on the pulse wave signals to obtain physiologically meaningful characteristics still remains a problem to be solved.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method and an apparatus for analyzing a pulse wave signal, which can analyze the pulse wave signal in a way executable by a processor to obtain a characteristic quantity related to a cardiovascular index.
The invention adopts the technical scheme to solve the technical problems and provides a pulse wave signal analysis method, which comprises the following steps:
a. receiving a pulse wave signal;
b. optimizing the pulse wave signal to obtain an improved signal;
c. performing analog-to-digital conversion on the pulse wave signals subjected to optimization processing;
d. identifying an accompanying state when the pulse wave signals are acquired, entering a step f when the accompanying state is static, and entering a step e when the accompanying state is dynamic;
e. performing Fourier transform on the pulse wave signals subjected to analog-to-digital conversion;
f. the pulse wave data analysis is performed using a combination of a feature point method and an area map method to obtain a feature quantity of the pulse wave signal.
In an embodiment of the present invention, the step of performing optimization processing on the pulse wave signal includes amplifying, filtering, denoising, and shaping a part or all of them.
In an embodiment of the invention, the method further includes providing an acceleration signal reflecting the accompanying state.
In an embodiment of the present invention, the method includes: and acquiring each inflection point of the pulse wave signal as a characteristic point.
In an embodiment of the present invention, the area mapping method includes calculating the characteristic value K of the pulse wave signal according to the following formula:
wherein,
p (T) pulse wave signal, T is time interval;
Pgis the maximum value of the pulse wave signal P (T) in the time interval T, PdIs the minimum value of the pulse wave signal p (T) in the time interval T.
The invention also provides a pulse wave signal analysis device which comprises a pulse wave optimization unit and a microprocessor. The pulse wave optimizing unit receives the pulse wave signals and performs optimizing processing on the pulse wave signals to obtain improved signals. The microprocessor comprises an analog-to-digital conversion circuit, a signal identification circuit, a Fourier transform circuit and a data analysis circuit. The analog-to-digital conversion circuit performs analog-to-digital conversion on the pulse wave signals subjected to optimization processing and outputs the pulse wave signals to the Fourier transform circuit and the signal identification circuit. The signal identification circuit is connected with the analog-to-digital conversion circuit, identifies the accompanying state when the pulse wave signals are collected, instructs the Fourier transform circuit to carry out Fourier transform when the accompanying state is dynamic, and provides the pulse wave signals subjected to analog-to-digital conversion to the data analysis circuit when the accompanying state is static. The Fourier transform circuit is connected with the analog-to-digital conversion circuit and the signal identification circuit and used for carrying out Fourier transform on the pulse wave signals subjected to analog-to-digital conversion. The data analysis circuit is connected with the Fourier transform circuit and the signal identification circuit, and performs pulse wave data analysis by using a combination of a characteristic point method and an area graph method to obtain the characteristic quantity of the pulse wave signal.
In an embodiment of the invention, the apparatus further includes an acceleration sensor for detecting an acceleration signal reflecting a state accompanying the pulse wave signal when the pulse wave signal is collected.
In an embodiment of the invention, the apparatus further includes a signal amplifying circuit connected between the analog-to-digital conversion circuit and the signal identifying circuit.
In an embodiment of the invention, the apparatus further includes a data storage circuit connected to the data analysis circuit.
Compared with the prior art, the invention adopts the technical scheme that a series of processing is carried out on the pulse wave signals, and finally the characteristic quantity related to the cardiovascular index is obtained in a mode of being executable by the processor. The invention can be applied to cheap and easy-to-operate electronic equipment, and is suitable for non-medical technicians to be used for home medical care monitoring.
Drawings
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below, wherein:
fig. 1 shows an exemplary configuration of a pulse wave analysis device for implementing the present invention.
Fig. 2 shows an exemplary composition of the pulse wave signal optimizing unit shown in fig. 1.
FIG. 3 illustrates an example composition of the microprocessor shown in FIG. 1.
FIG. 4 is a flowchart illustrating a pulse wave analysis method according to an embodiment of the invention.
Fig. 5 shows a typical pulse waveform.
Fig. 6 is a schematic diagram showing the extraction of the pulse waveform feature value K.
Fig. 7 shows a flow chart of data analysis of pulse waves.
Fig. 8 illustrates a relationship between the degree of arteriosclerosis and the pulse wave characteristic value.
Detailed Description
Fig. 1 shows an exemplary configuration of a pulse wave analysis device for implementing the present invention. Referring to fig. 1, the analysis apparatus 100 may include a pulse wave signal optimization unit 110, an acceleration sensor 120, a microprocessor 130, and an output unit 140. The input of the pulse wave signal optimization unit 110 can introduce the pulse wave signal. For example, the pulse wave signal optimization unit 110 may be connected to a pulse wave sensor to obtain a pulse wave signal. The output terminal of the pulse wave signal optimizing unit 110 is connected to the first input terminal IN1 of the microprocessor 130. The acceleration sensor 120 is connected to a second input IN2 of the microprocessor 130. The microprocessor 130 has three output terminals, a first output terminal OUT1 is connected to the pulse wave signal optimizing unit 110, a second output terminal OUT2 is connected to the acceleration sensor 120, and a third output terminal is connected to the output unit 140.
The pulse wave signal optimization unit 110 is configured to perform optimization processing on the pulse wave signal. The optimized pulse wave signal has better quality, and the accuracy of subsequent analysis is improved. An exemplary circuit of the pulse wave signal optimizing unit 110 is shown in fig. 2, and includes a preamplification circuit 201, a baseline correction circuit 202, a filter 203, a wave trap 204, and a shaping circuit 205, which are connected in sequence. These circuits respectively amplify, filter, denoise, and shape the signal to obtain a signal with higher quality. The baseline correction circuit 202 is mainly used to eliminate baseline drift caused by muscle tremor, body tension, respiratory tremor, etc. The baseline correction circuit 202 may include a voltage follower and an in-phase adder.
The acceleration sensor 120 is used to identify an accompanying state when the pulse wave signal is acquired. The acceleration sensor 120 typically employs a three-dimensional acceleration sensor to measure the movement of the object in three directions.
In one embodiment of the invention, the acceleration sensor model may be ADXL 345.
The microprocessor 130 is a core component of the apparatus 100, and can control the pulse wave signal optimizing unit 110, the acceleration sensor 120, and analyze the optimized pulse wave signal to obtain a desired characteristic.
In embodiments of the present invention, the microprocessor 130 may employ a system-on-a-chip, or a combination of a separate microprocessor chip and peripheral circuits.
FIG. 3 illustrates an example composition of the microprocessor shown in FIG. 1. Referring to fig. 3, the microprocessor 130 has input terminals IN1, IN2, and output terminals OUT1, OUT2, and OUT 3. IN1 is the pulse wave signal input terminal, IN2 is the acceleration signal input terminal. OUT1 and OUT2 are optimized control outputs for pulse signals and acceleration signals, respectively. OUT3 is the data output.
The fourier transform circuit 303 is connected to the analog-to-digital conversion circuit 301, and can transform the pulse wave signal from the time domain to the frequency domain. Preferably, the transform performed by the fourier transform circuit 303 is a real-time fourier transform (RFT).
A signal recognition circuit 304 for providing the data analysis circuit 305 of the subsequent stage with the motion state about the carrier.
The data analysis circuit 305 is connected to the fourier transform circuit 303 and the signal recognition circuit 304, and can perform data analysis of the pulse wave by referring to the signals of the two circuits to obtain a desired characteristic quantity reflecting the cardiovascular physiological index. The intermediate data and final data generated by the data analysis circuit 305 may be stored in the data storage circuit 306. The data analysis circuit 305 is connected to the data output terminal OUT3 to provide the pulse wave signal and the analysis result to the outside.
The data analysis circuit 305 is also connected to an optimization control circuit 307. The optimization control circuit 307 is connected to the optimization control output terminals OUT1 and OUT2 of the pulse signal and the acceleration signal.
In one embodiment, the pulse wave analyzing apparatus shown in fig. 1 is embedded as an integral module in wearable products (such as wrist band and watch), mobile phones, tablet computers, notebook computers, personal care devices, etc. to provide a pulse wave analyzing function. When the embedded product is a small product such as a wrist band or a watch, the output unit 140 of the pulse wave analysis device is configured as a wireless transmission module to output the analyzed data based on a short-range communication protocol such as WIFI, 2.4GZigBee, bluetooth, or the like. When the embedded product is a tablet computer or a notebook computer, the output unit 140 may be an interface for the tablet computer or the notebook computer to access the pulse wave analysis device.
In another embodiment, the pulse wave analysis apparatus shown in fig. 1 may be physically split into two parts. The first part is a combination of the pulse wave signal optimization unit 110 and the acceleration sensor 120. The second part is the microprocessor 130 and the output unit 140. The pulse wave signal optimization unit 110 and the acceleration sensor 120 may be embedded in a wearable product. For example, the pulse wave signal optimizing unit 110 and the acceleration sensor 120 may be integrally installed with the pulse sensor. The pulse wave signal optimizing unit 110, the acceleration sensor 120 and the microprocessor 130 are connected wirelessly.
In actual implementation, an example model number of microprocessor 130 is CC 2530. The CC2530 is a system-on-chip, and integrates an analog-to-digital converter, an operational amplifier, a microprocessor, a memory, a radio frequency transmitter, and other modules.
The pulse wave analysis method according to an embodiment of the present invention can be implemented in the pulse wave analysis device 100 shown in fig. 1, but is not limited thereto. The pulse wave analysis method of the present embodiment may be implemented on any suitable device. FIG. 4 is a flowchart illustrating a pulse wave analysis method according to an embodiment of the invention. Referring to fig. 4, the method flow is as follows:
in step 401, a pulse wave signal will be received.
For example, the pulse wave signal optimization unit 110 will obtain a pulse wave signal from a pulse wave sensor connected thereto.
In step 402, an optimization process is performed on the pulse wave signal.
Such as amplification, filtering, denoising, shaping, etc., of the signal at the pulse wave signal optimization unit 110 to obtain an improved signal.
In step 403, the optimized pulse wave signal is analog-to-digital converted.
For example, the pulse wave signal is analog-to-digital converted by the analog-to-digital conversion circuit 301 in the microprocessor 130 to obtain the pulse wave signal in digital form.
At step 404, an attendant state at the time the pulse wave signal was acquired is identified. For example, the acquisition object is static or dynamic at the moment the signal is acquired. In the case of a portable device, the motion of the carrier may affect the pulse wave acquisition process, so the present embodiment reduces or eliminates this effect by detecting the accompanying state when the pulse wave signal is acquired.
For example, the acceleration sensor 120 may sense the user's motion and provide an acceleration signal to the signal recognition circuit 304 in the microprocessor 130. The signal identification circuit 304 can obtain the pulse wave signals from the signal amplification circuit 302 and identify the position of each pulse wave signal whether the user is dynamic or static. If the user is static, the pulse wave signal can be directly analyzed, and the process proceeds to step 406. On the contrary, if the user is dynamic, the pulse wave signal will be submerged in the interference signal, and it is necessary to extract the correct pulse wave signal from the interference signal. In the present embodiment, the extraction of the pulse wave signal is realized by performing time/frequency domain signal conversion using fourier transform. Flow proceeds to step 405.
In step 405, the pulse wave signal subjected to the analog-to-digital conversion is fourier-transformed.
For example, the fourier transform circuit 303 in the microprocessor 130 performs fourier transform on the pulse wave signal to transform the signal from the time domain to the frequency domain.
At step 406, pulse wave data analysis is performed. In the present embodiment, this analysis is achieved by a combination of a feature point method and an area map method. This will be described later.
For example, the pulse wave data analysis may be performed by the data analysis circuit 305 in the microprocessor 130.
The feature point method is to identify feature points of the pulse wave. The characteristic point of the pulse wave is essentially the pressure curve of the pulse wave
The inflection point of (c). The characteristic points are the transition points of the cardiac cycle from one mechanical process to another, and thus these inflection points have clear physiological significance. Fig. 5 shows a typical pulse waveform. As shown in the typical pulse waveform of FIG. 5, the fluctuation of the pulse pattern at A, B, C, D reflects different physiological and pathological changes of human body.
The area map method is an extraction method in which the change in the area of a pulse wave is converted into the value of the pulse wave waveform characteristic quantity K. Fig. 6 is a schematic diagram showing the extraction of the pulse waveform feature value K. Referring to FIG. 6, the foregoing description
The pulse wave signal p (T) is marked with a straight line in the waveform having an interval T. According to the following formula:
the value of the characteristic quantity K can be obtained. Wherein P isgIs the maximum value of the pulse wave signal P (T) in the interval T, PdIs the minimum value of the pulse wave signal p (T) in the interval T.
The corresponding change of the waveform and the area of the pulse diagram can be caused by the physiological and pathological changes of the cardiovascular system, can be reflected on the change of the K value, is regular and quite sensitive, and is an important physiological index for the clinical examination of the cardiovascular system.
In summary, the pulse wave data analysis method of the present embodiment, as shown in fig. 7, may include the following steps:
in step 701, pulse wave data p (t) is read in.
In step 702, feature points in the pulse wave data p (t) are searched. Such as feature point A, B, C, D in fig. 5. This can be obtained by calculating the inflection point of p (t).
In step 703, the current value of p (t) feature point is maintained as p (t)', i.e., the value of the feature point is updated.
In step 704, the feature quantity K value is calculated.
In step 705, the current K value is kept at K', i.e., the feature quantity is updated.
In step 706, the K value and the P (t) feature point are compared to obtain a variance value.
In step 707, it is determined whether to warn according to the comparison result of step 706.
Since the physiological significance of the feature point method is clear, the pulse wave data analysis of the embodiment extracts relevant feature quantities by identifying each feature point of the pulse wave, and determines the change trend of the feature quantities, and simultaneously, the analysis of the shape, the area, the intensity, the speed and the rhythm variation of the pulse wave can be supplemented and corrected by using the area graph method.
Based on the analysis result, when the change of the pulse wave is abnormal, according to the severity of the corresponding pathological feature development trend, an early warning signal can be generated.
Therefore, the pulse wave analysis method and the device of the embodiment of the invention have the advantages that the detection operation based on the pulse wave is very simple and relatively cheap, and the method and the device are more suitable for non-medical technicians to be used for home medical care monitoring.
Although the present invention has been described with reference to the present specific embodiments, it will be appreciated by those skilled in the art that the above embodiments are merely illustrative of the present invention, and various equivalent changes or substitutions may be made without departing from the spirit of the invention, and therefore, changes and modifications to the above embodiments within the spirit of the invention are intended to fall within the scope of the claims of the present application.
Claims (9)
1. A pulse wave signal analysis method includes the following steps:
a. receiving a pulse wave signal;
b. optimizing the pulse wave signal to obtain an improved signal;
c. performing analog-to-digital conversion on the pulse wave signals subjected to optimization processing;
d. identifying an accompanying state when the pulse wave signals are collected, entering a step f when the accompanying state is static, and entering a step e when the accompanying state is dynamic;
e. performing Fourier transform on the pulse wave signals subjected to analog-to-digital conversion;
f. the pulse wave data analysis is performed using a combination of a feature point method and an area map method to obtain a feature quantity of the pulse wave signal.
2. The pulse wave signal analysis method according to claim 1, wherein the step of performing optimization processing on the pulse wave signal includes some or all of amplification, filtering, denoising, and shaping.
3. The method for analyzing pulse wave signal according to claim 1, further comprising providing an acceleration signal reflecting the accompanying state.
4. The method according to claim 1, wherein the feature point method comprises: and acquiring each inflection point of the pulse wave signal as a characteristic point.
5. The method for analyzing pulse wave signal according to claim 1, wherein the area mapping method includes calculating the characteristic quantity K of the pulse wave signal according to the following formula:
wherein,
p (t) a pulse wave signal,t is a time interval;
Pgis the maximum value of the pulse wave signal P (T) in the time interval T, PdIs the minimum value of the pulse wave signal p (T) in the time interval T.
6. A pulse wave signal analysis device comprising:
the pulse wave optimizing unit is used for receiving the pulse wave signals and optimizing the pulse wave signals to obtain improved signals;
a microprocessor, comprising:
the analog-to-digital conversion circuit is used for performing analog-to-digital conversion on the pulse wave signals subjected to optimization processing and outputting the pulse wave signals to the Fourier transform circuit and the signal identification circuit;
the signal identification circuit is connected with the analog-to-digital conversion circuit, identifies the accompanying state when the pulse wave signals are collected, instructs the Fourier transform circuit to carry out Fourier transform when the accompanying state is dynamic, and provides the pulse wave signals subjected to analog-to-digital conversion to the data analysis circuit when the accompanying state is static;
the Fourier transform circuit is connected with the analog-digital conversion circuit and the signal identification circuit and is used for carrying out Fourier transform on the pulse wave signals subjected to analog-digital conversion;
and a data analysis circuit connected to the Fourier transform circuit and the signal identification circuit, for analyzing the pulse wave data by using a combination of a feature point method and an area map method to obtain a feature quantity of the pulse wave signal.
7. The pulse wave signal analysis device according to claim 6, further comprising:
and an acceleration sensor for detecting an acceleration signal reflecting a state accompanying the pulse wave signal when the pulse wave signal is acquired.
8. The pulse wave signal analysis device according to claim 6, further comprising:
and the signal amplifying circuit is connected between the analog-to-digital conversion circuit and the signal identification circuit.
9. The pulse wave signal analysis device according to claim 6, further comprising:
and the data storage circuit is connected with the data analysis circuit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310545727.3A CN103720462A (en) | 2013-11-06 | 2013-11-06 | Pulse wave signal analyzing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310545727.3A CN103720462A (en) | 2013-11-06 | 2013-11-06 | Pulse wave signal analyzing method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103720462A true CN103720462A (en) | 2014-04-16 |
Family
ID=50444945
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310545727.3A Pending CN103720462A (en) | 2013-11-06 | 2013-11-06 | Pulse wave signal analyzing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103720462A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104082920A (en) * | 2014-06-24 | 2014-10-08 | 吕志雪 | Smart bracelet |
| CN108720821A (en) * | 2018-05-28 | 2018-11-02 | 京东方科技集团股份有限公司 | A kind of blood pressure estimation device and blood pressure estimating system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0645117A1 (en) * | 1993-09-28 | 1995-03-29 | Seiko Epson Corporation | Blood pulse wave detecting apparatus and motion intensity measuring apparatus |
| US20040086060A1 (en) * | 2002-10-04 | 2004-05-06 | Keisuke Tsubata | Pulse wave detecting apparatus and fourier transform process apparatus |
| CN101785667A (en) * | 2010-01-15 | 2010-07-28 | 北京工业大学 | Waveform characteristic point based method for analyzing volume pulse wave pattern and detection device |
| CN201668380U (en) * | 2010-06-13 | 2010-12-15 | 南京信息工程大学 | Pulse signal collecting and processing system |
| CN201912069U (en) * | 2010-05-06 | 2011-08-03 | 桂林电子科技大学 | Portable pulse wave detector |
| US20110282224A1 (en) * | 2009-02-03 | 2011-11-17 | Omron Healthcare Co., Ltd. | Pulse wave analyzer and pulse wave analyzing method |
| CN102499669A (en) * | 2011-10-26 | 2012-06-20 | 中国科学院深圳先进技术研究院 | Heart parameter measuring method and device |
| CN103054562A (en) * | 2012-12-25 | 2013-04-24 | 中国科学院合肥物质科学研究院 | Cardiovascular function detection method based on multi-channel pulse wave form analysis and device thereof |
| JP2013085702A (en) * | 2011-10-18 | 2013-05-13 | Toyota Central R&D Labs Inc | Device and program for estimation of biosignals |
-
2013
- 2013-11-06 CN CN201310545727.3A patent/CN103720462A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0645117A1 (en) * | 1993-09-28 | 1995-03-29 | Seiko Epson Corporation | Blood pulse wave detecting apparatus and motion intensity measuring apparatus |
| US20040086060A1 (en) * | 2002-10-04 | 2004-05-06 | Keisuke Tsubata | Pulse wave detecting apparatus and fourier transform process apparatus |
| US20110282224A1 (en) * | 2009-02-03 | 2011-11-17 | Omron Healthcare Co., Ltd. | Pulse wave analyzer and pulse wave analyzing method |
| CN101785667A (en) * | 2010-01-15 | 2010-07-28 | 北京工业大学 | Waveform characteristic point based method for analyzing volume pulse wave pattern and detection device |
| CN201912069U (en) * | 2010-05-06 | 2011-08-03 | 桂林电子科技大学 | Portable pulse wave detector |
| CN201668380U (en) * | 2010-06-13 | 2010-12-15 | 南京信息工程大学 | Pulse signal collecting and processing system |
| JP2013085702A (en) * | 2011-10-18 | 2013-05-13 | Toyota Central R&D Labs Inc | Device and program for estimation of biosignals |
| CN102499669A (en) * | 2011-10-26 | 2012-06-20 | 中国科学院深圳先进技术研究院 | Heart parameter measuring method and device |
| CN103054562A (en) * | 2012-12-25 | 2013-04-24 | 中国科学院合肥物质科学研究院 | Cardiovascular function detection method based on multi-channel pulse wave form analysis and device thereof |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104082920A (en) * | 2014-06-24 | 2014-10-08 | 吕志雪 | Smart bracelet |
| CN108720821A (en) * | 2018-05-28 | 2018-11-02 | 京东方科技集团股份有限公司 | A kind of blood pressure estimation device and blood pressure estimating system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ren et al. | A novel cardiac auscultation monitoring system based on wireless sensing for healthcare | |
| Reddy et al. | On-device integrated PPG quality assessment and sensor disconnection/saturation detection system for IoT health monitoring | |
| Li et al. | Design of a continuous blood pressure measurement system based on pulse wave and ECG signals | |
| US10925495B2 (en) | Apparatus and method for determining a blood pressure of a subject | |
| Sharma et al. | Acoustic sensing as a novel wearable approach for cardiac monitoring at the wrist | |
| CN104042191A (en) | Wrist watch type multi-parameter biosensor | |
| EP3478166A1 (en) | On-demand heart rate estimation based on optical measurements | |
| Chen et al. | Algorithm for heart rate extraction in a novel wearable acoustic sensor | |
| CN101919704B (en) | Heart sound signal positioning and segmenting method | |
| CN105249925A (en) | Traditional Chinese medicine pulse manifestation acquisition apparatus, noise reduction system and noise reduction method | |
| CN109512395B (en) | Method, device and equipment for analyzing and processing biological signals | |
| CN109106345A (en) | Pulse signal characteristic detection method and device | |
| US11051765B2 (en) | Health status detecting system and method for detecting health status | |
| Jiang et al. | Recent research for unobtrusive atrial fibrillation detection methods based on cardiac dynamics signals: A survey | |
| CN113729653A (en) | Human body pulse wave signal acquisition method | |
| US8498696B2 (en) | Health monitoring device and human electric signal processing method | |
| Sonawani et al. | Biomedical signal processing for health monitoring applications: a review | |
| Gupta et al. | Pre-processing based ECG signal analysis using emerging tools | |
| Yen et al. | Blood Pressure and Heart Rate Measurements Using Photoplethysmography with Modified LRCN. | |
| CN105997095B (en) | Method and device for monitoring fetal movement in real time based on electrode array | |
| WO2018018570A1 (en) | Device and method for measuring electrocardiogram | |
| Lapsa et al. | Adaptive signal-to-noise ratio indicator for wearable bioimpedance monitoring | |
| CN103720462A (en) | Pulse wave signal analyzing method and device | |
| US20200330040A1 (en) | Method and system for detecting noise in vital sign signal | |
| KR20190103626A (en) | A portable ECG electrode and an ECG measurement system for small animals |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20140416 |