CN109431493B - Wearable body surface potential acquisition device based on distance segmentation weighting algorithm - Google Patents

Wearable body surface potential acquisition device based on distance segmentation weighting algorithm Download PDF

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CN109431493B
CN109431493B CN201811494155.XA CN201811494155A CN109431493B CN 109431493 B CN109431493 B CN 109431493B CN 201811494155 A CN201811494155 A CN 201811494155A CN 109431493 B CN109431493 B CN 109431493B
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waveform
electrocardiogram
template
wave
point
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CN109431493A (en
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李建清
朱松盛
方文胜
刘宾
王惠琳
徐静怡
孟诚
王保民
钱明泽
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Nanjing Medical University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
    • A61B5/282—Holders for multiple electrodes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • A61B5/6805—Vests
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

Abstract

The invention discloses a wearable body surface potential acquisition device and method based on a distance segmentation weighting algorithm. Wherein, the signals received by part of dry electrodes are used as electrocardiosignals for synthesizing electrocardiogram. If the corresponding lead signal is good, the lead signal is directly adopted; if the corresponding lead signals are not good, four different multi-lead algorithms are adopted to obtain the corresponding electrocardiograms. And selecting the algorithm which can represent the electrocardiogram of the user most from the four algorithms by adopting a distance-based segmented weighting algorithm according to the similarity. The health condition of the user is analyzed based on the derived electrocardiogram and the distribution position of the lesion may be determined based on a Body Surface Potential Mapping (BSPM) at the same time to guide the subsequent treatment.

Description

Wearable body surface potential acquisition device based on distance segmentation weighting algorithm
Technical Field
The invention relates to a body surface potential acquisition technology, in particular to a wearable body surface potential acquisition device and method based on a distance segmentation weighting algorithm.
Background
With the improvement of living standard and the change of life style of people, the incidence of cardiovascular diseases also rises year by year, and the high disability rate, the high re-hospitalization rate and the high fatality rate of the cardiovascular diseases cause huge burden to the life quality of patients and the social development. At present, 2.9 hundred million cardiovascular disease patients in China are estimated, the fatality rate is 2.5 to 3.0 percent, and the fatality rate accounts for more than 40 percent of the fatalities of residents in China. The high mortality rate, high disability rate, high medical risk, high medical burden and the like of cardiovascular and cerebrovascular diseases make the cardiovascular and cerebrovascular diseases become important social problems. The monitoring of various physical signs such as electrocardio and the like is an effective means for early detecting cardiovascular and cerebrovascular diseases. If the dynamic monitoring of various body signs of patients with cardiovascular and cerebrovascular diseases, sub-health and even healthy people can be implemented on a large scale, abnormal conditions are fed back to doctors in time, and the early intervention is carried out on the causes of diseases possibly causing serious consequences, so that the fatality and disability rate of the cardiovascular and cerebrovascular diseases can be obviously reduced, and the social and economic losses are greatly reduced.
Although the invention with application number of CN201710262618.9 can monitor the electrocardio of a patient in real time, the electrocardio monitoring is not accurate enough and is greatly influenced by the motion of a user. Further analysis and diagnosis of the patient is not possible, nor is analysis of the location of the user's cardiac lesions possible.
The invention with the application number of CN201711309089.X monitors the health of a human body through borborygmus, heartbeat, respiration, body temperature, blood oxygen and the like of the human body, only carries out the most basic monitoring and is too general. But the prevention effect on the increasingly common cardiovascular and cerebrovascular diseases cannot be found in advance.
Disclosure of Invention
The invention discloses a wearable body surface potential acquisition device and method based on a distance segmentation weighting algorithm. Aims to monitor the body surface potential of people with high incidence of cardiovascular and cerebrovascular diseases so as to prevent and cure the cardiovascular and cerebrovascular diseases in time and facilitate subsequent treatment. The device comprises a vest, a dry electrode, a simulation front end, a data acquisition system and a PC end;
the dry electrode directly contacts a human body, the dry electrode transmits electrophysiological signals of the human body to an input port of the analog front end through a lead wire, the analog front end is subjected to filtering and amplification processing, output signals are connected to an input port of the data acquisition system, the analog signals are converted into digital signals after A \ D conversion of the data acquisition system, and the digital signals are stored in a computer and serve as data supports of the body surface potential mapping graph.
The dry electrodes are distributed on the vest;
the simulation front end is sewn between the right waist of the vest and is connected with the dry electrode through a lead;
the analog front-end (AFE), i.e. a hardware device for processing an analog signal sent by a signal source, has functions to be implemented, including a differential amplifier circuit, a high-pass filter circuit, a main amplifier circuit, and a low-pass filter circuit. The peripheral circuit is mainly formed by connecting a plurality of groups of resistor capacitors in series or in parallel, and the two input ports of the differential amplification circuit are connected with large resistors with proper resistance values, so that the impedance matching degree of the electrodes and the analog front end can be increased; by placing resistors (the amplifier is an amplifier of the chip of the analog front end, and the chip strictly speaking is an amplifier) with different values and capacitors around the amplifier, filter circuits with different pass bands can be formed;
the simulation front end processes the somatosensory potential signals collected by the dry electrode, and the steps comprise amplifying and filtering the signals;
the data acquisition system is a circuit system for converting the analog electrocardiosignals into digital signals. The whole acquisition system can be divided into an A \ D conversion part, a synchronous triggering part and a data transmission part. The A \ D conversion part is 8 cascaded data acquisition cards, each data acquisition card can synchronously sample 16 signals, each data acquisition card has 16-bit sampling precision, and the sampling frequency reaches 250kS/s (the data acquisition card is sold as a mature product). The 8 data acquisition cards are synchronously triggered by the same group of clock signals, so that the 8 data acquisition cards can start sampling at the same time. The data obtained by sampling is transmitted to an upper computer through a PCI-E interface, and the upper computer receives the data through Visual Studio and stores the data in real time;
the signal of the somatosensory potential acquired by the dry electrode is amplified and filtered by a peripheral circuit of the analog front end and then is accessed to a chip of the analog front end, and the signal is converted into a digital signal by a data acquisition system and sent to a PC (personal computer) end;
the PC end judges the acquired signals, if the Signal-to-noise ratio (Signal-to-noise ratio) reaches 90dB or more, the signals are synthesized into an electrocardiogram, otherwise, more than two multi-lead electrocardiogram algorithms are adopted to obtain corresponding electrocardiograms, and the electrocardiograms which best meet the user are screened out.
In the invention, four multi-lead electrocardio algorithms are adopted to obtain corresponding twelve-lead electrocardiograms, and the electrocardiograms which best meet the user are screened out. The four multi-lead electrocardiograph algorithms are as follows:
three-lead reconstruction twelve-lead algorithm: the electrocardiogram vector is a vector in a three-dimensional linear space, and when the vectors of three lead axes are not linearly related, the vectors form a base of the linear space, so that the projection coefficients of all leads on the three original leads can be obtained and used as reconstruction coefficients to obtain the electrocardiogram data of twelve leads.
Artificial Neural Network (ANN) method: and establishing the relation between the input layer and the output layer by continuously adjusting the connection relation between the nodes after training until the error of network training reaches the minimum value. The lead signals used for reconstruction are used as input neurons of the neural network, and the standard 12-lead ECG rest signals to be reconstructed are used as output neurons for training. After the training is finished, the relation between the leads used for reconstruction and the leads to be reconstructed is established by the trained neural network. When applied to practical situations, the rest lead signals to be reconstructed can be obtained only by knowing the lead signals for reconstruction and inputting the lead signals into the network.
Support Vector Machine (SVM) method: the classification is performed by first mapping input data into a high-dimensional space through a specific kernel function, then creating a separated maximum interval hyperplane for the feature space, and regression is to find the maximum interval hyperplane.
Linear lead reconstruction method: selecting a certain number of leads as initial leads for reconstruction, and then establishing a linear model between the initial leads and the standard twelve leads so as to realize the reconstruction of the leads.
The method specifically comprises the following steps:
step 1, use fourObtaining four corresponding electrocardiograms by different multi-lead electrocardio algorithms, and recording the corresponding electrocardiogram waveform of one cycle of the four electrocardiograms in the same time interval as A1，A2，A3，A4：
Figure BDA0001896438430000031
A＝(A1，A2，A3，A4)
n is the number of data points in an electrocardiographic waveform,
Figure BDA0001896438430000032
the method comprises the steps of representing the nth data point in the ith electrocardiogram waveform, A representing a set of four electrocardiograms in a corresponding cycle in the same period, and i taking the values of 1, 2, 3 and 4;
step 2, dividing four electrocardiogram waveforms into four sections according to the complete medical electrocardiogram waveforms including P wave, QRS wave, T wave and U wave, wherein the first section is a1～axThe second segment is ax～ayThe third segment is ay～azThe fourth stage is az～anWherein a is1The first point of a complete cycle, the peak of the P wave, axThe peak of the QRS wave, ayIs the peak of the T wave, azIs the peak of the U wave, anThe last point of a complete cycle is also the peak of the P-wave. Respectively calculating Hausdorff distances of four sections of electrocardiogram waveforms divided by each electrocardiogram waveform, wherein the Hausdorff distances are generally used for similarity measurement of binary images, the higher the similarity is, the smaller the Hausdorff distances are, and when the two images are the same, the Hausdorff distances are 0;
step 3, training through a BP Neural Network (Back Propagation Neural Network) to obtain weights P of four sections of electrocardiogram waveforms1，P2，P3，P4；
Step 4, respectively using A1，A2，A3，A4Is a template and is connected with the other three wavesSimilarity of shape calculation:
Figure BDA0001896438430000033
Figure BDA0001896438430000034
H(ai，as)＝max(h(As，Ai)，h(Ai，As))，
Figure BDA0001896438430000041
wherein A issIs a template waveform, AiAnother waveform, a, which is a waveform with a degree of similarity to the template waveformsIs a waveform AsA point ofiIs a waveform AiA point of (a), h (a)s，Ai)、h(Ai，As) For the calculated median value, H (a)i，as) Hausdorff distance, D (a), which is a waveform in an electrocardiogrami，as) Is the distance between each corresponding point in the two waveforms, t is the point at which a complete cycle waveform is sampled,
Figure BDA0001896438430000042
for the t-th point in the template waveform,
Figure BDA0001896438430000043
the t-th point of the waveform, H (A), to which the template waveform is comparedi，As) Is represented by AsAs a templateiAnd AsHausdorff distance of (H)yHausdorff distance, P, for the y-th waveformyAnd obtaining four groups of Hausdorff distances for the weight of the y-th section of waveform: with A1As a template: h (A)2，A1)、H(A3，A1)、H(A4，A1) (ii) a With A2As a template: h (A)1，A2)、H(A3，A2)、H(A4，A2) (ii) a With A3As a template: h (A)1，A3)、H(A2，A3)、H(A4，A3) (ii) a With A4As a template: h (A)1，A4)、H(A2，A4)、H(A3，A4)；
And 5, adding the Hausdorff distances of each group, comparing, selecting the template with the minimum sum value as an optimal result, wherein the electrocardiogram waveform corresponding to the optimal result is the electrocardiogram most suitable for the user.
The dry electrodes are button-shaped, and the number of the dry electrodes is N (generally 120).
The data acquisition system synchronously acquires the somatosensory potential signals acquired by the N dry electrodes,
the number of the dry electrodes is 120, the dry electrodes are roughly divided into eight rows and eighteen columns, wherein seven columns of the dry electrodes are close to the left chest part, six columns of the dry electrodes are uniformly distributed on the back of the vest, one column of the dry electrodes is respectively arranged at the positions of the left clavicle 1/3 and 2/3, and two columns of the dry electrodes are arranged on the left side surface of the vest.
The simulation front end is integrated in a box, a rechargeable lithium battery for providing a power supply is arranged in the box, and a switch and an electric quantity display are arranged on the outer surface of the box.
The vest is made of cloth with elasticity and air permeability.
The invention also provides a wearable body surface potential acquisition method based on the distance segmentation weighting algorithm, which is characterized by comprising the following steps:
step a1, after the wearable body surface potential acquisition device is fully charged, a user wears the vest and presses down the switch;
step a2, simultaneously acquiring body surface potentials of a user by using dry electrodes on the vest, sending the body surface potentials to a simulation front end, and synchronously acquiring and transmitting the body surface potentials to a PC (personal computer) end by using a data acquisition system;
step a3, the PC end judges the acquired signals, if appropriate, the signals are synthesized into an electrocardiogram, if not, more than two multi-lead electrocardio algorithms are adopted to obtain a corresponding electrocardiogram, and the electrocardiogram which best accords with the user is screened out;
step a3, if the electrocardiogram is abnormal, determining the distribution of the focus according to the body surface potential map.
Step a3 includes the following steps:
step a3-1, setting a normal myocardial model to obtain a corresponding body surface potential map, performing QRS period integration, and marking the obtained body surface potential integration map as SO;
step a3-2, dividing the heart into 53 partitions according to the anatomical position, and using the partitions as basic unit body surface potentials in operation;
setting a model of the n-th subarea ectopic pacing point, measuring a corresponding body surface potential map through a dry electrode, and performing integration in a QRS period, wherein the integral map of the body surface potential obtained by labeling is Sn, and the value of n is 0-53;
step a3-3, subtracting SO from Sn to obtain the difference of the integral map of the body surface potential, and recording the difference as Dn;
step a3-4, finding out a maximum value maxn from Dn, and recording the position of the maximum value maxn;
step a3-5, finding out a minimum value minn from Dn, and recording the position of the minimum value minn;
step a3-6, calculating a direction An from the minimum value to the maximum value;
step a3-7, analyzing all ans, and determining the location of ectopic pacing sites.
The beneficiary of the product is a person with the possibility of suffering from cardiovascular and cerebrovascular diseases, firstly, some heart diseases are shown in some special conditions, the long-time monitoring of the body surface potential can find the abnormality of the electrocardiogram in time, and some sudden situations can be prevented in advance; secondly, an alarm can be given out under the condition of sudden heart disease, so that the patient can be timely cured, and the death rate and disability rate of the patient are reduced; thirdly, the distribution position of the focus can be determined according to a body surface potential map obtained by the measured body surface potential for guiding the subsequent treatment. Therefore, the wearable body surface potential acquisition system based on the Hausdorff distance segmented weighting algorithm is provided.
The appearance of the device is similar to that of a common tight vest, and based on the right-hand habit of most people, a small box at the front end of the electrocardio simulation is arranged between the right waist of the vest, and a switch and electric quantity display are arranged on the box. And (3) turning on a switch, starting the body surface potential monitoring device to work, acquiring and processing the 120-path body surface potential, sending the acquired and processed body surface potential to a data acquisition system and a PC (personal computer) end, and synchronously acquiring, storing, analyzing and displaying the acquired and processed body surface potential at the PC end.
The invention has the beneficial effects that:
the adoption has good gas permeability and elastic cloth, can promote the contact of electrode and skin, reduces the frictional resistance that electrode and skin friction arouse, still helps improving people's wearing to experience, is fit for wearing for a long time for long-time body surface potential monitoring. Under the condition that corresponding lead signals are not good, 120-lead acquisition body surface potentials are adopted, corresponding electrocardiograms are obtained through four different algorithms, and finally, an electrocardiogram closest to the actual electrocardio condition of a user is selected through a Hausdorff distance-based segmented weighting algorithm, so that the interference caused by frictional impedance and the like is greatly reduced, and the accuracy of the measured electrocardiogram during exercise is improved. Can establish a complete heart potential distribution map, is helpful for doctors to diagnose fixed-point focus and guide subsequent treatment. The large amount of data acquisition of the user is helpful for helping the patient to establish a complete electrocardiogram data model, and is convenient for the analysis and diagnosis of the illness state of the user in the future.
Drawings
The foregoing and other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a diagram of the arrangement positions of 120 dry electrodes on the vest.
FIG. 2 is a main structural view of the apparatus of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
As shown in figure 2, the device comprises a vest body, 120 dry electrodes, a box-shaped electrocardio simulation front end, a data acquisition system and a PC end.
120 dry electrodes are made into button shape and sewed on the vest according to the position shown in figure 1. Roughly divided into eight rows and eighteen columns. Seven of the rows of dry electrodes are relatively tight and close to the left chest portion. The dry electrode is connected with the analog front end by a lead.
The box-shaped electrocardio-analog front end consists of a peripheral circuit and a chip. The body surface potential acquired by the dry electrode is amplified and filtered by a peripheral circuit and then is connected to a chip at the analog front end. The data are synchronously collected by a data collection system and then sent to a PC terminal.
The chip model is AD 8232.
The data acquisition system uses Shanghai simple instrument technology products, can realize 128-channel synchronous acquisition, and the sampling rate can reach 250 kS/s.
The data acquisition system is connected with a PCIE port of a PC end host case, and synchronous data acquisition, storage and display are realized by using C # in visual studio.
Signals of 120 somatosensory potentials are acquired by 120 dry electrodes respectively and transmitted to a peripheral circuit. The analog front end is accessed after the amplification and the filtering of the peripheral circuit. The data acquisition system realizes 120-path synchronous acquisition, and transmits the acquired signals to the PC terminal to obtain corresponding electrocardiograms by adopting four different multi-lead electrocardio algorithms. The electrocardiogram that best fits the user is selected using a piecewise weighting algorithm based on the Hausdorff distance. And finally, obtaining a conclusion according to the obtained QRS wave information and making a corresponding processing method.
The detailed steps of the algorithm are as follows:
the method comprises the following steps: the waveforms of the four electrocardiograms in a period corresponding to the same time are respectively recorded as A1，A2，A3， A4：
Figure BDA0001896438430000071
A＝(A1，A2，A3，A4)
n is the number of data points in an electrocardiographic waveform.
And dividing the electrocardiogram into four sections according to the standard P wave, QRS wave and T wave, and respectively calculating the Hausdorff distance. The first section is a1～axThe second segment is ax～ayThe third segment is ay～azThe fourth stage is az～an。
Step two: determining the weight of each segment through a neural training network, wherein the weight of each segment obtained through the BP neural training network is P ═ P1，P2，P3，P4}。
Step three: are respectively expressed as A1，A2，A3，A4For the template, similarity is calculated with the remaining three waveforms:
Figure BDA0001896438430000072
Figure BDA0001896438430000073
H(ai，as)＝max(h(As，Ai)，h(Ai，As))
wherein D (a)i，as) Is the distance of each point in the two waveforms.
Figure BDA0001896438430000074
Four sets of Hausdorff distances were obtained: with A1As a template: h (A)2，A1)、H(A3，A1)、H(A4，A1) (ii) a With A2As a template: h (A)1，A2)、H(A3，A2)、H(A4，A2) (ii) a With A3As a template: h (A)1，A3)、H(A2，A3)、H(A4，A3) (ii) a With A4As a template: h (A)1，A4)、H(A2，A4)、H(A3，A4)。
Step four: the Hausdorff distances of each group are added and compared, and the template of the group with the minimum sum value is selected as the optimal result.
If the electrocardiogram is abnormal, the following method is adopted to determine the focus of the heart:
setting a normal myocardial model to obtain a corresponding body surface potential map, and performing QRS period integration, wherein the marked integral map of the body surface potential is SO;
the heart is divided into 53 regions according to the anatomical position, and used as elementary unit body surface potentials in the operation.
Setting a model of an n (0-53) th partitioned ectopic pacing point, measuring a corresponding body surface potential map through a dry electrode, and performing integration in a QRS period, wherein an integral map of the body surface potential obtained by labeling is Sn;
subtracting SO from Sn to obtain the difference of the integral diagram of the body surface potential, and recording the difference as Dn;
finding out a maximum value maxn from Dn, and recording the position of the maximum value maxn;
finding out a minimum value minn from Dn, and recording the position of the minimum value minn;
calculating a direction An from the minimum value to the maximum value;
the location of ectopic pacing sites was determined for all An analyses. The determination method comprises the following steps:
individual differentiation matching is firstly carried out, so that different individuals can carry out disease discrimination on the body surface equipotential diagram under the same standard. To a large amount of actual dystopy paced patients, collect its body surface potential data earlier, through the clinical medicine imaging technology of intervention means, find the position of current patient dystopy pace-making point, a dystopy pace-making point position must correspond multiple body surface potential map, match body surface potential data and dystopy pace-making point position, set up the parameter of a matching degree (full value is 1), establish a heart dystopy pace-making point position and body surface potential map corresponding relation database based on this data. Matching the collected body surface potential diagram of the patient with the ectopic pacing point position in the database, and considering that the ectopic pacing point position of the patient is consistent with the preset ectopic pacing point position when the matching degree parameter reaches 0.8.
Examples
120 dry electrodes are made into button shape and sewed on the vest according to the position shown in figure 1. Roughly divided into eight rows and eighteen columns. Seven of the rows of dry electrodes are relatively tight and close to the left chest portion. The dry electrode is connected with the analog front end by a lead.
The box-shaped electrocardio-analog front end consists of a peripheral circuit and a chip. The body surface potential difference collected by the dry electrode is input, and the signal is amplified and filtered by a peripheral circuit and then is accessed to a chip at the analog front end. The data are synchronously collected by a data collection system and then sent to a PC terminal.
The model of the chip is AD8232, and the chip has low power consumption and small volume and is suitable for long-time carrying and measurement.
The data acquisition system uses Shanghai simple instrument technology products, can realize 128-channel synchronous acquisition, and the sampling rate can reach 250 kS/s.
The data acquisition system is connected with a PCIE port of a PC end host case, and synchronous data acquisition, storage and display are realized by using C # in visual studio.
The sampling rate was set to 360 Hz. From the medical point of view, at least 180 points are selected to represent a complete electrocardiogram, i.e. n is 180.
Dividing the electrocardiogram into four sections according to standard P wave, QRS wave and T wave, the first section is a1～a60The second segment is a61～a80The third segment is a81～a130The fourth stage is a131～a180。
Obtaining the weight value of each section as P ═ P through training the neural network1，P2，P3，P4}＝{0.2，0.05，0.4，0.35)。
Are respectively expressed as A1，A2，A3，A4For the template, similarity is calculated with the remaining three waveforms:
Figure BDA0001896438430000091
four groups of Hausdorff distances were obtained: h (A)2，A1)、H(A3，A1)、H(A4，A1)；H(A1，A2)、H(A3，A2)、 H(A4，A2)；H(A1，A3)、H(A2，A3)、H(A4，A3)；H(A1，A4)、H(A2，A4)、H(A3，A4). The Hausdorff distances of each group are added and compared, and the mode of the group with the minimum sum value is selected as the optimal result.
If the electrocardiogram is different, the distribution of the positions of the heart lesions is determined according to the analysis of the body surface potential map.
The invention provides a wearable body surface potential acquisition device and method based on a distance segmentation weighting algorithm, and a method and a way for implementing the technical scheme are many, the above description is only a preferred embodiment of the invention, and it should be noted that, for those skilled in the art, a plurality of improvements and embellishments can be made without departing from the principle of the invention, and these improvements and embellishments should also be regarded as the protection scope of the invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (6)

1. The wearable body surface potential acquisition device based on the distance segmentation weighting algorithm is characterized by comprising a vest, a dry electrode, a simulation front end, a data acquisition system and a PC (personal computer) end;
the dry electrodes are distributed on the vest;
the simulation front end is sewn between the right waist of the vest and is connected with the dry electrode through a lead;
the simulation front end processes the somatosensory potential signals collected by the dry electrode, and the steps comprise amplifying and filtering the signals;
after the somatosensory potential signals acquired by the dry electrode are amplified and filtered by the analog front end, the signals are converted into digital signals by the data acquisition system and are sent to the PC end;
the PC end judges the acquired signals, if the signal-to-noise ratio reaches 90dB or above, the signals are synthesized into an electrocardiogram, otherwise, the following steps are adopted:
step 1, four different methods are adoptedObtaining four corresponding electrocardiograms by a multi-lead electrocardio algorithm, and respectively recording the electrocardiogram waveforms of the four electrocardiograms in a corresponding period at the same time as A1，A2，A3，A4：
Figure FDA0002752990660000011
A＝(A1，A2，A3，A4)
n is the number of data points in an electrocardiographic waveform,
Figure FDA0002752990660000012
the method comprises the steps of representing the nth data point in the ith electrocardiogram waveform, A representing a set of four electrocardiograms in a corresponding cycle in the same period, and i taking the values of 1, 2, 3 and 4;
step 2, dividing four electrocardiogram wave forms into four sections according to standard P wave, QRS wave, T wave and U wave, wherein the first section is a1～axThe second segment is ax～ayThe third segment is ay～azThe fourth stage is az～an(ii) a Wherein a is1The first point of a complete cycle, the peak of the P wave, axThe peak of the QRS wave, ayIs the peak of the T wave, azIs the peak of the U wave, anThe last point of a complete cycle is also the peak of the P wave; the Hausdorff distances of the four electrocardiographic waveforms into which each electrocardiographic waveform is divided are calculated respectively,
step 3, obtaining the weights P of the four sections of electrocardiogram waveforms through BP neural network training1，P2，P3，P4；
Step 4, respectively using A1，A2，A3，A4For the template, similarity is calculated with the remaining three waveforms:
Figure FDA0002752990660000013
Figure FDA0002752990660000014
H(ai，as)＝max(h(As，Ai)，h(Ai，As))，
Figure FDA0002752990660000021
wherein A issIs a template waveform, AiAnother waveform, a, which is a waveform with a degree of similarity to the template waveformsIs a waveform AsA point ofiIs a waveform AiA point of (a), h (a)s，Ai)、h(Ai，As) For the calculated median value, H (a)i，as) Hausdorff distance, D (a), which is a waveform in an electrocardiogrami，as) Is the distance between each corresponding point in the two waveforms, t is the point at which a complete cycle waveform is sampled,
Figure FDA0002752990660000022
for the t-th point in the template waveform,
Figure FDA0002752990660000023
the t-th point of the waveform, H (A), to which the template waveform is comparedi，As) Is represented by AsAs a templateiAnd AsHausdorff distance of (H)yHausdorff distance, P, for the y-th waveformyAnd obtaining four groups of Hausdorff distances for the weight of the y-th section of waveform: with A1As a template: h (A)2，A1)、H(A3，A1)、H(A4，A1) (ii) a With A2As a template: h (A)1，A2)、H(A3，A2)、H(A4，A2) (ii) a With A3As a template: h (A)1，A3)、H(A2，A3)、H(A4，A3) (ii) a With A4As a template: h (A)1，A4)、H(A2，A4)、H(A3，A4)；
And 5, adding the Hausdorff distances of each group, comparing, selecting the template with the minimum sum value as an optimal result, wherein the electrocardiogram waveform corresponding to the optimal result is the electrocardiogram most suitable for the user.
2. The device of claim 1, wherein the dry electrode is button-shaped.
3. The apparatus of claim 2, wherein the data acquisition system synchronously acquires somatosensory potential signals acquired by the plurality of dry electrodes.
4. The device of claim 3, wherein said dry electrodes are 120, divided into eight rows and eighteen columns, wherein seven columns of dry electrodes are located adjacent to the left chest portion, six columns of dry electrodes are evenly distributed on the back of the vest, one column of dry electrodes is located at each of the left clavicles 1/3 and 2/3, and two columns of dry electrodes are located on the left side of the vest.
5. The apparatus of claim 4, wherein the analog front end is integrated into a box, a rechargeable potassium battery is provided in the box for providing power, and a switch and a power display are provided on the outer surface of the box.
6. The apparatus of claim 5, wherein said vest is made of a cloth having elasticity and breathability.
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