CN117899356B - Self-adaptive ear vagus nerve stimulation device - Google Patents
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Abstract
The present invention discloses an adaptive ear vagus nerve stimulation device, comprising: the device comprises an electroencephalogram acquisition module, an electrocardio acquisition module, a data processing module and an ear vagus nerve stimulation module; the brain electricity acquisition module acquires real-time brain electricity index signals of a patient; the electrocardio acquisition module acquires a real-time heart rate variability signal of a patient; the data processing module analyzes the real-time brain electrical index signal and the real-time heart rate variability signal in a preset time period according to a preset corresponding relation to obtain a self-adaptive stimulation current value; the ear vagus nerve stimulation module applies a target stimulation current to the ear vagus nerve of the patient, and adjusts the currently applied target stimulation current according to the adaptive stimulation current value every preset time period. The specific relation among the electrocardiosignal, the electroencephalogram and the stimulation effect of the vagus nerve is defined, the specific relation is used as the basis of the self-adaptive regulation of the closed-loop stimulation current of the vagus nerve, and the sensitivity and the accuracy of the self-adaptive regulation are improved.
Description
Technical Field
The invention discloses the technical field of medical information mining, and particularly relates to a self-adaptive ear vagus nerve stimulation device.
Background
At present, the stimulation of the vagus nerve of the ear is a novel physiotherapy which is developed at home and abroad in recent years, has been applied to the treatment of epilepsy, depression, migraine, insomnia and the like, has the advantages of definite curative effect, safety, economy, no side effect, simple operation and convenient popularization, and has wide application prospect.
In the prior art, the following main defects still exist in the design of the vagus nerve closed loop: (1) The main application range of the current vagus nerve stimulation system is neuropsychiatric diseases, key parts for the effect are positioned in the brain, and the current vagus nerve stimulation treatment process is not combined; (2) Because the clinical auditory vagus nerve stimulation treatment is often carried out by guiding the patient for multiple times, the patient can truly master the normative treatment and then automatically treat the patient at home, so that a real-time supervision process for whether the treatment is carried out or not and whether the parameter adjustment is correct or not exists, and the problems of lack of practical feasibility and accuracy are obviously existed only by means of telephone follow-up and video monitoring during the treatment, and the design does not consider the problems; (3) For epilepsy, the current method for predicting the seizure of the epilepsy is obviously insufficient in accuracy, and is used for detecting the seizure period of a epileptic, and the effect of early intervention is not achieved; (4) There is also a significant difference between the heart rate variability of the patient when seizures are not occurring and the normal heart rate variability.
Accordingly, those skilled in the art are required to develop a new solution to the above problems.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides an adaptive ear vagus nerve stimulation device, comprising: the device comprises an electroencephalogram acquisition module, an electrocardio acquisition module, a data processing module and an ear vagus nerve stimulation module, wherein the electroencephalogram acquisition module and the electrocardio acquisition module are respectively connected with the data processing module, and the data processing module is connected with the ear vagus nerve stimulation module;
the electroencephalogram acquisition module is used for acquiring real-time electroencephalogram index signals of a patient at intervals of a preset time period;
the electrocardio acquisition module is used for acquiring real-time heart rate variability signals of the patient at intervals of preset time periods in the process of performing the stimulation treatment of the vagus nerve of the ear of the patient;
The data processing module is used for analyzing the real-time brain electrical index signal and the real-time heart rate variability signal in the preset time period according to the corresponding relation among the preset self-adaptive stimulation current value, the brain electrical index signal and the heart rate variability signal, acquiring the self-adaptive stimulation current value and sending the self-adaptive stimulation current value to the vagus nerve stimulation module;
the ear vagus nerve stimulation module is used for applying target stimulation current to the ear vagus nerve of a patient, and receiving the self-adaptive current value sent by the data processing module in real time every preset time period so as to regulate the currently applied target stimulation current according to the self-adaptive stimulation current value.
Optionally, the apparatus further includes: the monitoring treatment module is respectively connected with the ear vagus nerve stimulation module, the cloud server and the remote mobile terminal;
The monitoring treatment module is used for sending the real-time brain electrical index signal, the real-time heart rate variability signal, the self-adaptive stimulation current value and the currently applied target stimulation current value of the patient which are acquired each time to the cloud server for storage, and sending the real-time brain electrical index signal, the real-time heart rate variability signal, the self-adaptive stimulation current value and the currently applied target stimulation current value of the patient to the remote mobile terminal, so that medical staff can monitor the treatment condition of the patient in real time through the remote mobile terminal.
Optionally, the real-time electroencephalogram index signal includes: the change amount of the auditory oddball normal event-related potential P300wave before and after a preset time period;
The real-time heart rate variability signal comprises: the change amount of the percentage PNN 50 of the time interval between each pair of adjacent normal heartbeats in the electrocardiogram accounting for all the normal heart beat logarithms before and after a preset time period and the change amount of the heart rate variability high-frequency band HF value before and after the preset time period are calculated according to the time interval of more than 50 ms;
The corresponding relation among the preset self-adaptive stimulation current, the electroencephalogram index signal and the heart rate variability signal is obtained after fitting through a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3), wherein Y is a self-adaptive stimulation current value, X 1 is the change quantity of the number of each pair of adjacent normal heartbeats in an electrocardiogram, the number of which exceeds 50ms, in percentage PNN 50 of the logarithm of all normal heartbeats in the preset time period, X 2 is the change quantity of the HF value of the heart rate variability high-frequency band before and after the preset time period, X 3 is the change quantity of the related potential P300 wave of auditory oddball normal event before and after the preset time period, and a 1、a2、a3、b1、b2 and b 3 are coefficients.
Optionally, the data processing module includes: a first processor;
The first processor is configured to obtain, in real time, a change amount of an auditory oddball normal event related potential P300 wave acquired by the electroencephalogram acquisition module before and after a preset time period, a change amount of a number of time intervals between each pair of adjacent normal heartbeats in the electrocardiogram acquired by the electrocardiograph acquisition module that exceeds 50ms in percentage PNN50 of all normal heart beat logarithms before and after the preset time period, and a change amount of a heart rate variability high-frequency band HF value acquired by the electrocardiograph acquisition module before and after the preset time period when the target stimulation current is applied to the auditory oddball normal event related potential P300 wave acquired by the electroencephalogram acquisition module for treatment, and calculate, according to a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3), an adaptive stimulation current value corresponding to the change amount of the auditory oddball normal event related potential P300 wave before and after the preset time period, the change amount of a number of time intervals between each pair of adjacent normal heartbeats in the electrocardiogram that exceeds 50ms in percentage PNN50 of all normal heart beat logarithms before and after the preset time period, and the change amount of heart rate variability high-frequency band HF value before and after the preset time period.
Optionally, the first processor is further configured to pre-process the real-time electroencephalogram indicator signal and the real-time heart rate variability signal, and the pre-processing process includes: the heart rate variability signals are subjected to digital processing, denoising and artifact removing processing, QRS wave automatic detection and rejection of ectopic pacing QRS to obtain a sinus NN interval sequence, NN interval data in a preset time period are calculated, PNN 50 and HF indexes of heart rate variability are extracted and calculated, and P300 indexes before stimulation and after stimulation in the preset time period are calculated.
Optionally, the data processing module further includes: the second processor is used for judging whether the epileptic patient is in an inter-seizure period or not through a preset epileptic monitoring strategy according to the real-time electroencephalogram index signals acquired by the electroencephalogram acquisition module so as to apply target stimulation current to the vagus nerve of the patient through the vagus nerve stimulation module when the epileptic patient is determined to be in the inter-seizure period, and treating the patient;
The epileptic monitoring strategy is as follows: judging whether abnormal waveform changes occur in waveform changes corresponding to the real-time brain electrical index signals, wherein the abnormal waveform changes comprise: spike, spike complex, spike slow wave and dysrhythmic wave; if the abnormal waveform changes occur, determining that the epileptic patient is in an epileptic seizure interval.
Optionally, the apparatus further includes: the current threshold setting module is connected with the ear vagus nerve stimulation module;
the current threshold setting module is used for setting the maximum threshold current of the self-adaptive adjusted stimulation before the treatment of the vagus nerve of the ear on the patient, and the maximum threshold current is the upper limit threshold of the self-adaptive adjusted stimulation current;
The process of setting the maximum threshold current is as follows: stopping the brain electrical collection module from collecting real-time brain electrical signals and stopping the electrocardio collection module from collecting real-time heart rate variability signals, gradually increasing the current value applied to the patient by the ear vagus nerve stimulation module from zero, determining the corresponding current when the patient just begins to feel pain as the maximum threshold current of the self-adaptive regulated stimulation, gradually reducing the stimulation current value applied to the patient by the ear vagus nerve stimulation module near the maximum threshold current value, and determining the stimulation current value applied to the patient by the ear vagus nerve stimulation module at the moment as the initial regulation current of the self-adaptive stimulation current value when the regular vascular pulsation dynamic occurs in the concha cavity of the patient and the patient has no obvious pain.
Optionally, the PNN 50 has a normal value in the range of 22.16% ± 13.14% in the 18-29 year old, 13.84% ± 9.94% in the 30-49 year old, and 7.95% ± 9.15% in the 50-69 year old;
The normal value range of the HF value in the age range of 18-29 years is 1537.54ms 2±1712.79ms2, the normal value range in the age range of 30-49 years is 836.40ms 2±828.29ms2, and the normal value range in the age range of 50-69 years is 266.46ms 2±614.53ms2;
the normal range of hearing oddball paradigm P300 is: 5-20uV;
and taking the normal value range of the PNN 50 and the HF value in different age groups and the normal value range of the P300 as the upper limit threshold priori limiting targets of the adaptive stimulation current value.
Optionally, the apparatus further includes: a linear fitting module;
The linear fitting module is used for fitting a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or according to the vagus nerve stimulating current, the brain electrical index signal and the heart rate variability signal Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3);
The fitting process is as follows: performing general linear model fitting according to the change amount of auditory oddball normal form event related potential P300 wave before and after a preset time period, the change amount of PNN 50 which is the percentage of the number of time intervals between each pair of adjacent normal heartbeats in an electrocardiogram and exceeds 50ms to all normal heartbeat logarithms, the change amount of heart rate variability high-frequency band HF value before and after the preset time period and the change amount of stimulation current value before and after the preset time period of a preset number of sample patients in a database in the treatment process of the auditory oddball normal form event related potential P300 wave before and after the preset time period, and obtaining a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or a general stimulation current value before and after the preset time period Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3).
Optionally, the ear vagus nerve stimulation module is configured to receive the adaptive current value sent by the data processing module in real time at intervals of a preset time period, subtract the adaptive current value from the currently applied target stimulation current, and obtain the adjusted target stimulation current value, so as to apply current stimulation treatment to the ear vagus nerve of the patient through the adjusted target stimulation current value.
In summary, through the technical scheme disclosed by the invention, the following beneficial effects can be brought:
(1) The gold standard of epileptic detection, namely scalp electroencephalogram, is adopted to realize more accurate detection of epileptic seizure intervals, and when the epileptic is determined to be in the seizure intervals, the treatment module is coupled for treatment, so that the curative effect can be improved, and the effect of treating the disease is achieved;
(2) Screening and adopting heart rate variability indexes (PNN 50 and HF) capable of sensitively representing the curative effect of the vagus nerve stimulation, determining that a specific linear correlation exists between the PNN50 and the HF and the curative effect of the vagus nerve stimulation, combining an electroencephalogram P300 index, taking the difference value between the normal values of the PNN50 and the HF of a patient and the vagus nerve stimulation treatment after 5 minutes and the difference value of the P300 before and after 5 minutes of stimulation as the basis of the adaptive regulation of the vagus nerve closed loop stimulation current, taking the feedback of the brain to the treatment into consideration, and improving the sensitivity and the accuracy of the adaptive regulation of the vagus nerve closed loop stimulation current;
(3) The highest current threshold value of the self-adaptive regulated stimulation is set, and the highest current threshold value and the range of the heart rate variability normal value are used as priori limiting indexes of the current range of the closed-loop self-adaptive regulation of the stimulation current, so that discomfort such as obvious pain and the like possibly caused by overlarge current stimulation in the treatment process can be avoided, and the safety of equipment is improved;
(4) The treatment information storage element and the Bluetooth sending element are added, so that the doctor can monitor the condition of the patient in the home treatment process in real time and correct the condition accurately under the permission of the patient, and the scientificity and the practicability are improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a schematic diagram illustrating a configuration of an adaptive ear vagus nerve stimulation device according to an exemplary embodiment;
FIG. 2 is a schematic structural view of another adaptive ear vagus nerve stimulation device according to the illustration of FIG. 1;
FIG. 3 is a schematic illustration of an electroencephalogram anomaly waveform according to the illustration of FIG. 1;
Fig. 4 is a schematic structural view of yet another adaptive ear vagus nerve stimulation device according to fig. 1.
Detailed Description
The following describes in detail the embodiments of the present disclosure with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
Fig. 1 is a schematic structural view of an adaptive ear vagus nerve stimulation device according to an exemplary embodiment, as shown in fig. 1, the device comprising: the device comprises an electroencephalogram acquisition module 110, an electrocardio acquisition module 120, a data processing module 130 and an ear vagus nerve stimulation module 140, wherein the electroencephalogram acquisition module 110 and the electrocardio acquisition module 120 are respectively connected with the data processing module 130, and the data processing module 130 is connected with the ear vagus nerve stimulation module 140; the electroencephalogram acquisition module 110 is used for acquiring real-time electroencephalogram index signals of a patient every preset time period; the electrocardiograph acquisition module 120 is used for acquiring real-time heart rate variability signals of the patient at intervals of preset time periods in the process of performing the stimulation treatment of the vagus nerve of the ear of the patient; the data processing module 130 is configured to analyze the real-time electroencephalogram index signal and the real-time heart rate variability signal in the preset time period according to a preset correspondence between the adaptive stimulation current value, the electroencephalogram index signal and the heart rate variability signal, obtain the adaptive stimulation current value, and send the adaptive stimulation current value to the vagus nerve stimulation module; the vagus nerve stimulating module 140 is configured to apply a target stimulating current to the vagus nerve of the patient, and receive the adaptive current value sent by the data processing module in real time at intervals of a preset time period, so as to adjust the currently applied target stimulating current according to the adaptive stimulating current value.
The device for stimulating the auricular vagus nerve in the disclosed embodiment of the invention is mainly used for applying target stimulating current to the auricular vagus nerve of a patient for treatment and generating self-adaptive stimulating current according to a real-time heart rate variability signal and a real-time brain electrical index signal fed back in the treatment process of the patient so as to adaptively adjust the target stimulating current, and is formed by an electroencephalogram acquisition module 110, an electrocardio acquisition module 120, a data processing module 130 and an auricular vagus nerve stimulation module 140. The brain electricity acquisition module is usually a head-wearing helmet and is used for acquiring scalp brain electricity index signals of a patient, and the brain electricity acquisition module can be used in the treatment and non-treatment processes of the auditory vagus nerve stimulation. The electrocardio acquisition module is mainly used in the treatment process of the vagus nerve stimulation of the ear and is mainly used for acquiring the variation of heart rate variability indexes before and after a preset time period in the treatment process according to the electrocardiogram data of a patient. The data processing module is used for calculating the self-adaptive stimulation current value of the patient according to the real-time brain electrical index signal and the real-time heart rate variability signal acquired by the brain electrical acquisition module and the electrocardio acquisition module. The ear vagus nerve stimulation module firstly applies target stimulation current to the ear vagus nerve of the patient according to a conventional treatment mode, and adjusts the target stimulation current according to the self-adaptive stimulation current value obtained by the data analysis module.
Specifically, the ear vagus nerve stimulating module is configured to receive the adaptive current value sent by the data processing module in real time at intervals of a preset time period, subtract the adaptive current value from the currently applied target stimulating current, and obtain the adjusted target stimulating current value, so as to apply current stimulating treatment to the ear vagus nerve of the patient through the adjusted target stimulating current value.
In an exemplary embodiment, in the process of applying a stimulation current to the vagus nerve of the patient for treatment, a real-time brain electrical index signal and a real-time heart rate variability signal are acquired every 5 minutes, an adaptive stimulation current value is calculated, a current applied target stimulation current is subtracted from the adaptive stimulation current value, an adjusted target stimulation current value is obtained, and the adjusted target stimulation current value is used for applying the stimulation current to the vagus nerve of the patient for treatment. Similarly, after 5 minutes, the target stimulation current after 5 minutes is adjusted by the self-adaptive current value, so that the real-time self-adaptive adjustment of the current in the treatment process of the vagus nerve stimulation is realized.
Specifically, the real-time electroencephalogram index signal includes: the change amount of the auditory oddball normal event-related potential P300wave before and after a preset time period; the real-time heart rate variability signal comprises: the change amount of the percentage PNN 50 of the time interval between each pair of adjacent normal heartbeats in the electrocardiogram accounting for all the normal heart beat logarithms before and after a preset time period and the change amount of the heart rate variability high-frequency band HF value before and after the preset time period.
Illustratively, P300 refers to abnormal waves of varying magnitude that occur in 300ms at event-related potentials (including vagal nerve stimulation), and in many studies it has been demonstrated that the magnitude of P300 reflects the activity of the brain stem blue-patch core-noradrenal loop, which is also a very important mechanism for the treatment of neuropsychiatric disorders by the vagus nerve. After the HF value heart rate variability frequency domain analysis is actually acquired from the high frequency part value of the heart rate variability data, time domain analysis can be performed, the index is SDNN, SDANN, RMSSD, PNN50, etc., and frequency domain analysis can also be performed, and the index is: LF, HF, LF/HF, etc., whereas PNN50, HF reflects mainly parasympathetic (vagal) activity, and our previously published papers have demonstrated that among the many indicators of heart rate variability, these two indicators are best used to reflect vagal tone, so we have chosen these two indicators.
In addition, the preset correspondence between the adaptive stimulation current, the electroencephalogram index signal and the heart rate variability signal is obtained after fitting through a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3), wherein Y is an adaptive stimulation current value, X 1 is a variable quantity of a percentage PNN 50 of a time interval between each pair of adjacent normal heartbeats exceeding 50ms in an electrocardiogram to the logarithm of all normal heartbeats before and after a preset time period, X 2 is a variable quantity of a heart rate variability high-frequency band HF value before and after the preset time period, X 3 is a variable quantity of an event related potential P300wave before and after the preset time period, and a 1、a2、a3、b1、b2 and b 3 are coefficients.
For example, in the disclosed embodiment of the invention, two general linear models Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3) are adopted for fitting to obtain a corresponding relationship among a preset adaptive stimulation current, an electroencephalogram index signal and a heart rate variability signal.
Still further, the apparatus further comprises: a linear fitting module; the linear fitting module is used for fitting a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or a heart rate variability signal according to the vagus nerve stimulating current of the ear, the brain electrical index signal and the heart rate variability signal Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3).
The fitting process is as follows: performing general linear model fitting according to the change amount of auditory oddball normal event related potential P300 wave before and after a preset time period, the change amount of PNN 50, the change amount of heart rate variability high-frequency band HF value before and after the preset time period, the change amount of stimulation current value before and after the preset time period, and the change amount of the stimulation current value before and after the preset time period of a preset number of sample patients in a database, wherein the change amount of the preset number of sample patients before and after the preset time period, the change amount of PNN 50, the change amount of heart rate variability high-frequency band HF value before and after the preset time period, and the change amount of each pair of adjacent normal heartbeats in an electrocardiogram are more than 50ms, so as to obtain a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3).
By way of example, it is understood that the database in the disclosed embodiments of the present invention refers to a medical database of hospitals in which historical hospital data for individual patients is stored. Medical information of a preset number of patients (sample patients) is selected from a database (the medical information comprises the change amount of auditory oddball normal event related potential P300wave before and after a preset time period, the change amount of PNN 50 which is the percentage of the time interval between each pair of adjacent normal heartbeats in an electrocardiogram to the logarithm of all normal heartbeats and is more than 50ms, the change amount of heart rate variability high-frequency band HF value before and after the preset time period and the change amount of stimulation current value before and after the preset time period), and a general linear model is fitted according to the medical information of the preset number of (4 minimum) sample patients.
In addition, it should be noted that the change amount of the auditory oddball normal event-related potential P300 wave before and after the preset time period, the change amount of the percentage PNN 50 of the number of the time interval exceeding 50ms between each pair of adjacent normal heartbeats in the electrocardiogram to all the normal heart beat logarithms before and after the preset time period, the change amount of the heart rate variability high-frequency band HF value before and after the preset time period, and the adaptive stimulation current value are all positive correlations.
Furthermore, in the disclosed embodiment of the invention, general linear models of different age groups can be set, namely, 18-29 years old is an age group, 30-49 years old is an age group, 50-69 years old is an age group, sample patients are divided according to the different age groups, and the general linear models are fitted according to medical data of the sample patients of each age group to obtain the corresponding relation among the adaptive stimulation current value, the electroencephalogram index signal and the heart rate variability signal. In addition, after the corresponding relations of the different age groups are obtained, in the process of performing adaptive ear vagal nerve stimulation treatment on the patient, the corresponding relations among the adaptive stimulation current value, the brain electrical index signal and the heart rate variability signal of the corresponding age group are selected according to the age of the patient, and the adaptive stimulation current value is calculated according to the corresponding relations among the adaptive stimulation current value, the brain electrical index signal and the heart rate variability signal corresponding to the age group of the patient.
Fig. 2 is a schematic structural view of another adaptive ear vagus nerve stimulation device according to fig. 1, and the data processing module shown in fig. 2 includes: a first processor 131; the first processor is configured to obtain, in real time, a change amount of an auditory oddball-normal-style event-related potential P300 wave acquired by the electroencephalogram acquisition module before and after a preset time period, a change amount of a number of each pair of adjacent normal heartbeats in an electrocardiogram acquired by the electrocardiograph acquisition module, which is more than 50ms, in percentage PNN50 of all normal heart beat logarithms before and after the preset time period, and a change amount of a heart rate variability high-frequency-band HF value acquired by the electrocardiograph acquisition module before and after the preset time period, and calculate, according to a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3), an adaptive stimulation current value corresponding to the change amount of the event-related potential P300 wave before and after the preset time period, the change amount of the number of each pair of adjacent normal heartbeats in the electrocardiogram, which is more than 50ms, in percentage PNN50 of all normal heart beat logarithms before and after the preset time period, and the change amount of the heart rate variability high-frequency-band HF value before and after the preset time period.
The data processing module is used for analyzing the real-time electroencephalogram index signal and the real-time heart rate variability signal to obtain the self-adaptive stimulation current value after the electroencephalogram index signal is acquired by the electroencephalogram acquisition module and the real-time heart rate variability signal is acquired by the electrocardio acquisition module. In addition, the data processing module can select a corresponding relation among a preset self-adaptive stimulation current value, an electroencephalogram index signal and a heart rate variability signal in a corresponding age range according to the age of a patient, and calculate the self-adaptive stimulation current value according to the corresponding relation.
It can be understood that, before the data processing module analyzes the real-time electroencephalogram index signal and the real-time heart rate variability signal, the data processing module also needs to perform data preprocessing on the real-time electroencephalogram index signal and the real-time heart rate variability signal, so as to extract the change amount of the auditory oddball normal event related potential P300 wave before and after a preset time period, the change amount of the percentage PNN50 of each pair of adjacent normal heartbeats in the electrocardiogram acquired by the electrocardio acquisition module, which is more than 50ms, of the number of the adjacent normal heartbeats in the heart rate variability high-frequency band HF value acquired by the electrocardio acquisition module before and after the preset time period.
Specifically, the first processor 131 is further configured to perform preprocessing on the real-time electroencephalogram indicator signal and the real-time heart rate variability signal, where the preprocessing process includes: the heart rate variability signals are subjected to digital processing, denoising and artifact removing processing, QRS wave automatic detection and rejection of ectopic pacing QRS to obtain a sinus NN interval sequence, NN interval data in a preset time period are calculated, PNN 50 and HF indexes of heart rate variability are extracted and calculated, and P3 indexes before stimulation and after stimulation in the preset time period are calculated.
In addition, as shown in fig. 2, the data processing module further includes: the second processor 132 is configured to determine whether the epileptic patient is in an epileptic seizure interval according to a real-time electroencephalogram index signal acquired by the electroencephalogram acquisition module through a preset epileptic monitoring policy, so as to apply a target stimulation current to an ear vagus nerve of the patient through the ear vagus nerve stimulation module to treat the patient when the epileptic patient is determined to be in the epileptic seizure interval; the epileptic monitoring strategy is as follows: judging whether abnormal waveform change occurs in waveform change corresponding to the real-time brain electrical index signal, wherein the abnormal waveform change comprises the following steps: spike, spike complex, spike slow wave and dysrhythmic wave; if the abnormal waveform changes occur, the epileptic patient is determined to be in an inter-seizure phase.
For example, in the disclosed embodiment of the present invention, the second processor 132 is configured to detect an epileptic seizure interval, and for an epileptic patient, the bluetooth signal control switch of the second processor (usually a chip) in the data processing module is turned on to perform accurate detection of an epileptic signal and tracing and positioning of a epileptic brain area, and is coupled and controlled with adaptive adjustment of an electric current for closed loop stimulation of an auditory vagus nerve, so that the epileptic detection and the implementation of closed loop stimulation of the auditory vagus nerve and the adaptive adjustment of the electric current are performed simultaneously.
In general, whether the epileptic is in an inter-seizure period is judged by a real-time electroencephalogram index signal, and electroencephalogram abnormalities of epileptic can be divided into an inter-seizure period and a seizure period. As shown in fig. 3, the spike, spike slow wave emission or abnormality of various rhythms is mainly seen in the inter-seizure period. For example: the spike wave is waveform change caused by abnormal discharge of cerebral cortex nerve cells in an electroencephalogram signal, is expressed by negative surface deflection, steep ascending branch and descending branch caused by surface orientation of the cortex, has an overall shape like spike of thorn, and is one of epileptic characteristics of epileptic discharge in the electroencephalogram signal. The period of the spike is usually 20-70ms, which stands out from the background signal, and the amplitude is more than 20uV and more than 1.5 times of the background signal. During the period of onset, an abnormal onset electroencephalogram event with evolution process from beginning to end can be seen, namely epileptiform discharge such as spike, spike slow wave, spike slow compound wave and the like can be found in the electroencephalogram, and the method has important value for diagnosis of epilepsy, and seizure typing and prognosis. The disclosed embodiment of the invention adopts the inter-seizure period, and treats the epilepsy when the patient does not have formal seizure, so that the seizure of the epilepsy of the patient can be intervened in advance, and the effect of no disease healing is achieved.
In addition, fig. 4 is a schematic structural view of still another adaptive ear vagus nerve stimulation device according to fig. 1, as shown in fig. 4, the device further comprising: the monitoring treatment module 150 is respectively connected with the ear vagus nerve stimulation module 140, the cloud server and the remote mobile terminal; the monitoring and treating module 150 is configured to send the real-time electroencephalogram index signal, the real-time heart rate variability signal, the adaptive stimulation current value and the currently applied target stimulation current value of the patient acquired each time to the cloud server for storage, and send the real-time electroencephalogram index signal, the real-time heart rate variability signal, the adaptive stimulation current value and the currently applied target stimulation current value of the patient to the remote mobile terminal, so that a medical staff can monitor the treatment condition of the patient in real time through the remote mobile terminal.
By way of example, in the embodiment of the disclosure, the detailed information of the treatment of the patient every 5 minutes is stored by the treatment information storage device and sent to the monitoring treatment module through bluetooth, and on the premise of following the permission of the patient, the detailed information is uploaded to the cloud server to save the original data, and on the other hand, the detailed information is sent to the mobile phone (remote mobile terminal) of the doctor in real time, so that the doctor can instruct and correct the real-time treatment, the safety and effectiveness of the treatment are ensured, and the better supervision and the treatment quality control of the actual conditions such as whether the patient is treated at home, whether the stimulation position is correct, whether the parameters are correct or not are also ensured.
In addition, the device also comprises: a current threshold setting module (not shown) connected to the auditory vagus nerve stimulation module; the current threshold setting module is used for setting the maximum threshold current of the self-adaptive adjusted stimulation before the treatment of the vagus nerve of the ear on the patient, wherein the maximum threshold current is the upper limit threshold of the current value of the self-adaptive adjusted stimulation; the process of setting the maximum threshold current is as follows: the method comprises the steps of stopping the brain electrical acquisition module from acquiring real-time brain electrical signals and stopping the electrocardio acquisition module from acquiring real-time heart rate variability signals, gradually increasing the current value applied to a patient by the vagus nerve stimulation module from zero, determining the corresponding current when the patient just begins to feel pain as a maximum threshold current value, gradually reducing the stimulation current value applied to the patient by the vagus nerve stimulation module near the maximum threshold current value, and determining the stimulation current value applied to the patient by the vagus nerve stimulation module at the moment as the initial regulation current of the self-adaptive stimulation current value when the regular vascular pacing motion of the concha cavity of the patient occurs and the patient has no obvious pain feeling, namely, the self-adaptive current is regulated within a certain range of the initial regulation current. .
Illustratively, subthreshold stimulation current is firstly adopted, then in the treatment process, according to the difference between the instant PNN 50, the HF value and the P300 and the target value, the self-adaptive value of the current is obtained according to a fitting formula, the current-applied target stimulation current is subtracted by the self-adaptive stimulation current value, the adjusted target stimulation current value is obtained, so that current stimulation treatment (not exceeding the maximum value defined before and protecting the patient) is applied to the auricular vagus nerve of the patient through the adjusted target stimulation current value, and thus, the self-adjustment is repeatedly performed dynamically in the whole treatment process, and the aim of individual accurate treatment is achieved.
The PNN 50 has a normal value range of 22.16% + -13.14% at 18-29 years old, 13.84% + -9.94% at 30-49 years old, and 7.95% + -9.15% at 50-69 years old; the normal range of the HF value at 18-29 years old is 1537.54ms 2±1712.79ms2, the normal range at 30-49 years old is 836.40ms 2±828.29ms2, and the normal range at 50-69 years old is 266.46ms 2±614.53ms2; the normal value range of the P300 is as follows: 5-20uV; and taking the normal value range and the P300 normal value range of the PNN 50 and the HF values in different age groups as the upper limit threshold priori limiting targets of the adaptive stimulation current values.
In summary, the present disclosure relates to an adaptive ear vagus nerve stimulation device, comprising: the device comprises an electroencephalogram acquisition module, an electrocardio acquisition module, a data processing module and an ear vagus nerve stimulation module; the electroencephalogram acquisition module is used for acquiring real-time electroencephalogram index signals of a patient every preset time period; the electrocardio acquisition module is used for acquiring real-time heart rate variability signals of the patient at intervals of a preset time period in the process of performing the stimulation treatment of the vagus nerve of the ear of the patient; the data processing module is used for analyzing the real-time brain electrical index signal and the real-time heart rate variability signal in a preset time period according to the corresponding relation among the preset self-adaptive stimulation current value, the brain electrical index signal and the heart rate variability signal, acquiring the self-adaptive stimulation current value and sending the self-adaptive stimulation current value to the ear vagus nerve stimulation module; the ear vagus nerve stimulation module is used for applying target stimulation current to the ear vagus nerve of a patient, and receiving the self-adaptive current value sent by the data processing module in real time every preset time period so as to adjust the currently applied target stimulation current according to the self-adaptive stimulation current value. The specific relation among the electrocardiosignal, the electroencephalogram and the stimulation effect of the vagus nerve is defined, the specific relation is used as the basis of the self-adaptive regulation of the closed-loop stimulation current of the vagus nerve, and the sensitivity and the accuracy of the self-adaptive regulation are improved.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the embodiments described above, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (7)
1. An adaptive ear vagus nerve stimulation device, the device comprising: the device comprises an electroencephalogram acquisition module, an electrocardio acquisition module, a data processing module and an ear vagus nerve stimulation module, wherein the electroencephalogram acquisition module and the electrocardio acquisition module are respectively connected with the data processing module, and the data processing module is connected with the ear vagus nerve stimulation module;
the electroencephalogram acquisition module is used for acquiring real-time electroencephalogram index signals of a patient at intervals of a preset time period;
the electrocardio acquisition module is used for acquiring real-time heart rate variability signals of the patient at intervals of preset time periods in the process of performing the stimulation treatment of the vagus nerve of the ear of the patient;
The data processing module is used for analyzing the real-time brain electrical index signal and the real-time heart rate variability signal in the preset time period according to the corresponding relation among the preset self-adaptive stimulation current value, the brain electrical index signal and the heart rate variability signal, acquiring the self-adaptive stimulation current value and sending the self-adaptive stimulation current value to the vagus nerve stimulation module;
The ear vagus nerve stimulation module is used for applying target stimulation current to the ear vagus nerve of a patient, and receiving the self-adaptive stimulation current value sent by the data processing module in real time every preset time period so as to adjust the currently applied target stimulation current according to the self-adaptive stimulation current value;
the real-time electroencephalogram index signal comprises: the change amount of the auditory oddball normal event-related potential P300 wave before and after a preset time period;
The real-time heart rate variability signal comprises: the change quantity of the percentage PNN50 of the time interval between each pair of adjacent normal heartbeats in the electrocardiogram, which is more than 50ms, accounting for the logarithm of all normal heartbeats before and after a preset time period, and the change quantity of the heart rate variability high-frequency band HF value before and after the preset time period;
The corresponding relation among the preset self-adaptive stimulation current, the electroencephalogram index signal and the heart rate variability signal is obtained by fitting through a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3), wherein Y is a self-adaptive stimulation current value, X1 is the change quantity of the number of time intervals between each pair of adjacent normal heartbeats exceeding 50ms in the electrocardiogram, which is the percentage PNN50 of the logarithm of all normal heartbeats, before and after a preset time period, X2 is the change quantity of the heart rate variability high-frequency band HF value before and after the preset time period, X3 is the change quantity of an event-related potential P300wave before and after the preset time period, and a1, a2, a3, b1, b2 and b3 are coefficients;
The apparatus further comprises: a linear fitting module;
The linear fitting module is used for fitting a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or according to the vagus nerve stimulating current, the brain electrical index signal and the heart rate variability signal Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3);
The fitting process is as follows: performing general linear model fitting according to the change amount of auditory oddball normal event related potential P300 wave before and after a preset time period, the change amount of PNN50 which is the percentage of the number of time intervals between each pair of adjacent normal heartbeats in an electrocardiogram and accounts for all normal heartbeat logarithms and is more than 50ms in the preset time period, the change amount of heart rate variability high-frequency band HF value before and after the preset time period and the change amount of stimulation current value before and after the preset time period of a preset number of sample patients in a database in the treatment process of the auditory oddball normal event related potential P300 wave during the treatment of the auditory vagus nerve, and obtaining a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or a general linear model Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3);
The apparatus further comprises: the current threshold setting module is connected with the ear vagus nerve stimulation module;
the current threshold setting module is used for setting the maximum threshold current of the self-adaptive adjusted stimulation before the treatment of the vagus nerve of the ear on the patient, and the maximum threshold current is the upper limit threshold of the self-adaptive adjusted stimulation current;
The process of setting the maximum threshold current is as follows: stopping the brain electrical acquisition module from acquiring real-time brain electrical signals and stopping the electrocardio acquisition module from acquiring real-time heart rate variability signals, gradually increasing the current value applied to a patient by the ear vagus nerve stimulation module from zero, determining the corresponding current when the patient just begins to feel pain as the maximum threshold current of the self-adaptive regulated stimulation, gradually reducing the stimulation current value applied to the patient by the ear vagus nerve stimulation module near the maximum threshold current value, and determining the stimulation current value applied to the patient by the ear vagus nerve stimulation module at the moment as the initial regulation current of the self-adaptive stimulation current value when the regular vascular pulsation dynamic occurs in the concha cavity of the patient and the patient has no obvious pain;
The device also comprises Bluetooth, which is used for sending the detailed information of the treatment of the patient to the mobile phone of the doctor under the permission of the patient so as to lead the doctor to guide and correct the treatment in real time.
2. The adaptive ear vagus nerve stimulation device of claim 1, further comprising: the monitoring treatment module is respectively connected with the ear vagus nerve stimulation module, the cloud server and the remote mobile terminal;
The monitoring treatment module is used for sending the real-time brain electrical index signal, the real-time heart rate variability signal, the self-adaptive stimulation current value and the currently applied target stimulation current value of the patient which are acquired each time to the cloud server for storage, and sending the real-time brain electrical index signal, the real-time heart rate variability signal, the self-adaptive stimulation current value and the currently applied target stimulation current value of the patient to the remote mobile terminal, so that medical staff can monitor the treatment condition of the patient in real time through the remote mobile terminal.
3. The adaptive ear vagus nerve stimulation device of claim 1, wherein the data processing module comprises: a first processor;
The first processor is used for acquiring the change quantity of the auditory oddball normal form event related potential P300 wave acquired by the electroencephalogram acquisition module before and after a preset time period in real time and the time interval between each pair of adjacent normal heartbeats in the electrocardiogram acquired by the electrocardiograph acquisition module is more than 50ms when the auricular vagus nerve of the patient is treated by applying the target stimulation current
And calculating the change quantity of the electric potential P300 wave related to the auditory oddball normal event before and after the preset time period, the change quantity of the number of the adjacent normal heart beats in the electrocardiogram, which exceeds 50ms, of the percentage PNN50 of the normal heart beat logarithm before and after the preset time period and the self-adaptive stimulation current value corresponding to the change quantity of the heart rate variability high frequency band HF value before and after the preset time period according to a general linear model ：Y＝(a1X1+b1)+(a2X2+b2)+(a3X3+b3), or Y＝(a1X1 2+b1)+(a2X2 2+b2)+(a3X3 2+b3).
4. The adaptive ear vagus nerve stimulation device of claim 3, wherein the first processor is further configured to pre-process the real-time electroencephalographic index signal and the real-time heart rate variability signal, the pre-processing procedure comprising: the heart rate variability signals are subjected to digital processing, denoising and artifact removing processing, QRS wave automatic detection and rejection of ectopic pacing QRS to obtain a sinus NN interval sequence, NN interval data in a preset time period are calculated, PNN50 and HF indexes of heart rate variability are extracted and calculated, and auditory oddball normal-form event related potential P300 before stimulation and after stimulation in the preset time period is calculated.
5. The adaptive ear vagus nerve stimulation device of claim 3, wherein the data processing module further comprises: the second processor is used for judging whether an epileptic patient is in an epileptic seizure interval or not through a preset epileptic monitoring strategy according to the real-time electroencephalogram index signals acquired by the electroencephalogram acquisition module, so that when the epileptic patient is determined to be in the epileptic seizure interval, the target stimulation current is applied to the auricular vagus nerve of the patient through the auricular vagus nerve stimulation module to treat the patient;
The epileptic monitoring strategy is as follows: judging whether abnormal waveform changes occur in waveform changes corresponding to the real-time brain electrical index signals, wherein the abnormal waveform changes comprise: spike, spike complex, spike slow wave and dysrhythmic wave; if the abnormal waveform changes occur, determining that the epileptic patient is in an epileptic seizure interval.
6. The adaptive ear vagus nerve stimulation device of claim 1, wherein the PNN 50 has a normal value ranging from 22.16% ± 13.14% at 18-29 years old, a normal value ranging from 13.84% ± 9.94% at 30-49 years old, and a normal value ranging from 7.95% ± 9.15% at 50-69 years old;
The normal value range of the HF value in the age range of 18-29 years is 1537.54ms 2±1712.79ms2, the normal value range in the age range of 30-49 years is 836.40ms 2±828.29ms2, and the normal value range in the age range of 50-69 years is 266.46ms 2±614.53ms2;
the normal range of hearing oddball paradigm P300 is: 5-20uV;
and taking the normal value range of the PNN 50 and the HF value in different age groups and the normal value range of the P300 as the upper limit threshold priori limiting targets of the adaptive stimulation current value.
7. The adaptive ear vagus nerve stimulation device of claim 1, wherein the ear vagus nerve stimulation module is configured to receive the adaptive stimulation current value sent by the data processing module in real time every a preset period of time, subtract the adaptive stimulation current value from a currently applied target stimulation current, and obtain the adjusted target stimulation current value, so as to apply the current stimulation therapy to the ear vagus nerve of the patient through the adjusted target stimulation current value.
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