CN104605841A - Wearable electrocardiosignal monitoring device and method - Google Patents
Wearable electrocardiosignal monitoring device and method Download PDFInfo
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Abstract
The invention provides a wearable electrocardiosignal monitoring and processing method and device. Flexible fabric electrocardioelectrodes and a central control box are embedded into an elastic chest belt. The central controller comprises an electrocardiosignal collecting and conditioning unit, a signal processing unit, a data storage unit, a power unit and a wireless communication unit. The signal processing unit carries out filtering, feature extraction and analysis of the heart abnormal change state on the electrocardiosignal collected, amplified and filtered preliminarily by the collecting and conditioning unit. The signal waveform and the analysis result are sent to personal digital management equipment like a mobile phone of a user through the wireless communication unit and can be sent to a remote doctor through a network, and the doctor analyzes the illness state and feeds back medical suggestions and matters needing attentions to the user. The wearable electrocardiosignal monitoring device has the advantages of being comfortable to wear, light, small in size, low in cost and the like, long-term monitoring is facilitated under the conditions of daily life, study, activities and the like, and the intelligent diagnosis and feedback treatment of the heart state are achieved.
Description
Technical field
The present invention relates to wearable health medical treatment technical field, refer to a kind of wearable electrocardiosignal monitoring process method and device especially.
Background technology
In recent years, along with the prevalence of the chronic disease headed by cardiovascular disease in old people is more and more higher, and there is the cause of disease and state of an illness complexity, sick time is long, medical treatment cost is high, need the features such as long-period of management, more and more patient expect by health supervision pattern by centered by hospital to family-centered transformation, managed by personal health and suppress the zooming trend of senile chronic disease, thus reduce medical economy burden.Significant in the monitoring diagnosis and treatment of heart disease and other chronic diseases for very important electrocardiosignal, be that the mankind study and one of bioelectrical signals being applied to clinical medicine the earliest.Ambulatory electrocardiogram is the clinical analysis state of an illness, establishes the important objective basis of diagnosis, and comprise the electrocardiogram (ECG) data under the different situations such as rest, activity, work and study and sleep, it more intactly can record heart, for diagnosis provides foundation more accurately.Large-scale cardiac monitoring equipment volume in hospital is huge, at least needs 12 connecting lines to measure, brings sense of discomfort, add cost of seeking medical advice simultaneously to patient; The EGC sensor signal quality directly having influence on monitoring and the comfortableness worn on the other hand, especially for long-term Real-Time Monitoring, material and the structure of electrode are most important, current clinical electrocardioelectrode is the wet electrode with electrolytic gel, the signal attenuation that can cause skin allergy and cause due to skin dehydration, so be not suitable for long-term monitoring.
For above problem, domesticly propose the multiple electrocardio-monitor based on wearable textiles electrode, China Patent No. 200920089699.8 discloses a kind of wearable electrocardioelectrode vest with data recording equipment, electrode is formed by silver and nonwoven fiber complex, can gather and send the electrocardiosignal gathered; China Patent No. 201120506497.6 discloses a kind of wearable electrocardiosignal measuring device, and electrocardioelectrode can acquired signal to be gone forward side by side row relax and transmission, can obtain heart electric wave signal and heart rate value; The patent No. 201120303457.1 discloses a kind of wearing clothes of detecting electrocardio signal of human body, electrocardioelectrode is made on clothes by conductive fabric, ECG's data compression circuit, transmitter etc. are positioned in a housing, and data can be sufferer or doctor provides medical reference.
Although above patent is all have employed flexible fabric electrode based on wearable technology, successfully can reach the object of long-term monitoring electrocardiosignal, but textile electrode makes comparatively complicated, even need manufacturing equipment just can complete, also do not consider that textile electrode detects electrocardio and can bring very large motion artifacts interference when physical activity simultaneously.Disturb from other that to have specific frequency range different, it has dynamic frequency range, and amplitude is comparatively large, easily damages or floods bio signal, and it will cause the diagnosis of irrational process and mistake, brings huge challenge to monitoring.Motion artifacts can disturb the effectiveness of electrocardiosignal to a great extent, probably causes the warning of assessment to electrocardiosignal parameter error and trigger erroneous.Therefore, how effectively the motion artifacts in ambulatory ecg signal is suppressed to be the key issue necessarily solved in wearable health supervision.
Summary of the invention
For solving the problem, the invention provides a kind of wearable electrocardiosignal monitoring process method and device, can in the cardiomotility situation of the monitoring human whenever and wherever possible when not hindering daily routines.
Wearable electrocardiosignal monitoring device, is characterized in that: described device is made up of elastic chest bandage, flexible fabric electrocardioelectrode, central control box and intraconnections, and flexible fabric electrocardioelectrode and central control box embed in elastic chest bandage; Wherein
Described flexible fabric electrocardioelectrode is arranged at appropriate location inside pectoral girdle, and electrode is projection setting, ensures to contact completely with skin; Preferably, this flexible fabric electrocardioelectrode adopts the mode of printing or direct coated with conductive liquid to make; Shape for convenience of different pectoral girdles is arranged, and the shape of flexible fabric electrocardioelectrode can be arranged to the shapes such as circle, ellipse or polygon, and the modes such as the pattern that also can beautify arrange shape.
Preferably, adopt single channel to lead detection method, pectoral girdle at least configures three flexible fabric electrocardioelectrodes, wherein two electrode pairs should at left breast and right breast.
Described central control box at least comprises ecg signal acquiring conditioning unit, acceleration collecting unit, signal processing unit, radio communication unit; In addition, central control box can also comprise data storage cell, power subsystem, and described memory element is stored in the analytical data of data, feature and the heart mutation situation produced in electrocardiosignal observation process, and power subsystem provides power supply for whole device.
Described ecg signal acquiring conditioning unit, for gathering electrocardiosignal, carries out Hz noise, baseline drift to the electrocardiosignal collected and carries out noise pretreatment to myoelectricity interference, and the electrocardiosignal after process is carried out the processing and amplifying of gain;
Described acceleration collecting unit uses accelerometer to gather acceleration signal; Wherein, this acceleration signal is used for the suppression of motion artifacts interference.
The electrocardiosignal after treatment that described signal processing unit obtains based on described ecg signal acquiring conditioning unit and the acceleration signal that acceleration collecting unit gathers, carry out the analysis of the filtering of electrocardiosignal, feature extraction and heart mutation situation;
Preferably, this signal processing unit comprises:
Filtering of ECG Signal unit, be connected with ecg signal acquiring conditioning unit, acceleration collecting unit, adaptive filter method is adopted to suppress the motion artifacts interference noise that human body produces under moving situation, wherein using acceleration signal as the reference signal of sef-adapting filter.Signal characteristic abstraction unit, be connected with Filtering of ECG Signal unit, signal character detection algorithm is adopted to extract important signal characteristic from filtered electrocardiosignal, this signal characteristic at least comprises the characteristic index such as R ripple, heart rate of amplitude maximum, these indexs can be modified according to user's request, no longer limit and repeat at this.Heart mutation diagnosis unit unit, for detecting the parameter values of each feature of electrocardiosignal, these signal characteristics are carried out to the analysis of time domain and frequency domain, obtain the statistical indicator about electrocardiosignal, utilize machine learning classification algorithm to carry out Classification and Identification to the heart state of user.
Described radio communication unit is used for data to be after treatment wirelessly sent to receiving terminal, for Human Physiology and the observation of activity situation and the analyzing and diagnosing of the state of an illness; Preferably, this receiving terminal can be the equipment such as mobile terminal, fixing PC, personal digital assistant, notebook computer, panel computer.
Described intraconnections connects flexible fabric electrocardioelectrode and central control box; Preferably, this intraconnections, adopts wire or conductive fiber, and common yarn sewing is covered on intraconnections, connects track in formation.
In addition, present invention also offers a kind of electrocardiosignal monitoring method based on wearable electrocardiosignal monitoring device, described device at least comprises flexible fabric electrocardioelectrode, ecg signal acquiring conditioning unit, acceleration collecting unit, signal processing unit, radio communication unit and intraconnections, it is characterized in that:
1), signal collection modulation
Gather electrocardiosignal by ecg signal acquiring conditioning unit, and the process of the interference noises such as myoelectricity interference, Hz noise and baseline drift is tentatively eliminated to the electrocardiosignal collected; And gather acceleration signal by acceleration collecting unit;
2), motion artifacts suppresses
Pass through adaptive filter algorithm, for step 1) in the electrocardiosignal that obtains, sef-adapting filter is adopted to suppress the motion artifacts interference noise that human body produces under moving situation, in this programme, conventional sef-adapting filter method can be adopted, degree of will speed up signal as reference signal, thus adopts adaptive filter algorithm automatically to regulate self weights coefficient, to reach best filter effect.
Preferably, this programme can adopt following sef-adapting filter to realize, but the application is not limited to only realize with following methods: adaptive filter algorithm can be adopted automatically to regulate self weights coefficient W, to reach best filter effect, this sef-adapting filter has two-way input signal, one tunnel is the ECG signal d (k) with motion artifacts interference, and a road is reference signal x (k); Wherein, adopt a kind of normalization change step length least mean square error (Least Mean Squares, LMS) algorithm as adaptive filter algorithm, adopt acceleration signal as the reference signal of sef-adapting filter;
According to the structure of sef-adapting filter, the output of sef-adapting filter is the inner product of reference signal and weights coefficient, i.e. y=x
tw, then the output error of whole sef-adapting filter is the difference of input signal and output signal, i.e. e (k)=d (k)-x
tw.LMS algorithm is exactly make the mean-square value of above formula output error be minimum, to reach the suppression of noise signal.Can know that the more new formula of weights coefficient is according to LMS algorithm:
W(k+1)＝W(k)+μe(k)x(k)
Wherein, μ is step factor.The present invention uses a kind of Normalized LMS Algorithm of variable step, and have convergence rate and less steady-state error faster compared with traditional LMS algorithm, the step factor of this algorithm is expressed as:
Therefore the more new formula of weights coefficient becomes:
Utilize the acceleration signal of three axis of orientations (x, y, z) of accelerometer as reference signal in the present invention, i.e. x (k)=[Acc
x(k), Acc
y(k), Acc
z(k)], filter weights coefficient vector W=[w
1, w
2, w
3].
Thus, the step of whole adaptive-filtering is:
The first step: initialize weights coefficient vector W (0)=[0,0,0];
Second step: filter error e (k)=d (the k)-x estimating current time
tw;
3rd step: upgrade filter weights coefficient vector:
4th step: increased by time parameter k, to time next, repeats step above, until reach iterations.
3), signal characteristic abstraction
By step 2) the middle electrocardiosignal after motion artifacts suppresses obtained, important signal characteristic is extracted by signal character detection algorithm, this signal characteristic at least comprises the characteristic index such as R ripple, heart rate of amplitude maximum, these indexs can be modified according to user's request, no longer limit and repeat at this; By property data base local for those signal characteristics composition, for analysis and the assessment of heart rate mutation;
4), the analysis of cardiac conditions and assessment
By step 3) in the parameter values composition characteristic matrix of each feature of electrocardiosignal that obtains, these signal characteristics are carried out to the analysis of time domain and frequency domain, obtain the statistical indicator of electrocardiosignal, and according to this statistical indicator, Classification and Identification is carried out to the heart state of user.Preferably, in this step, machine learning classification algorithm can be adopted, according to the statistical indicator of electrocardiosignal, Classification and Identification be carried out to the heart state of user
In addition, this method can further include:
5), by the electrocardiosignal after signal processing and the heart state Classification and Identification result to user, by being wirelessly sent to doctor, and the suggestion of diagnostic result and the prevention state of an illness is fed back by doctor.
The present invention's wearable electrocardiosignal monitor device can when wearer carry out daily life, study and motion the monitoring of long-term cardiomotility is carried out to it.Suitable signal processing method is adopted to the electrocardiosignal gathered, break away from the restriction that can only gather electrocardiosignal in a stationary situation, feature extraction algorithm is adopted to extract signal characteristic composition characteristic data base, for follow-up intelligent diagnosis and treatment provide data to the electrocardiosignal after process.The beneficial effect of technique scheme is as follows: (1), utilize conductive material to be subject to electricity irritation and change electrical characteristic and make electrocardioelectrode, and materials are soft to the stingless excitation of skin, are convenient to user's worn for long periods, can repeated multiple timesly clean; (2) the Centroid quality, embedding pectoral girdle is light, low in energy consumption; (3), can detect the electrocardiosignal of user under kinestate in real time, stable signal transmission, ecg wave form is clear, and noise is less, is convenient to the feature detection of signal; (4), the signal after handling well can be produced diagnostic result by intellectual analysis, and wirelessly be real-time transmitted to cell phone, panel computer or personal management equipment, be convenient to the heart that user understands oneself in real time; (5), by signal and analysis result by Internet Transmission to diagnosis and treatment doctor, by doctor, diagnostic result is assessed, and provides the treatment of science to advise to sufferer.
Accompanying drawing explanation
Fig. 1 is wearable cardioelectric monitor pectoral girdle structural representation;
Fig. 2 is wearable electrocardioelectrode structural representation;
Fig. 3 is wearable electrocardiograph monitoring device theory diagram;
Fig. 4 is wearable electrocardiograph monitoring device signal processing module block diagram;
Fig. 5 is wearable electrocardio motion artifacts AF panel block diagram;
Fig. 6 is the FB(flow block) of wearable cardioelectric monitor method.
Wherein, 1-elastic chest bandage; 2-conductive fabric electrode; 3-central control box; 4-fabric; 5-textile electrode layer; 6-ecg signal acquiring conditioning unit; 7-acceleration collecting unit; 8-signal processing unit; 9-radio communication unit; 10-data storage cell; 11-power subsystem; 12-Filtering of ECG Signal unit; 13-signal characteristic abstraction unit; 14-heart mutation diagnosis unit.
Detailed description of the invention
For making the technical problem to be solved in the present invention, technical scheme and advantage clearly, be described in detail below in conjunction with the accompanying drawings and the specific embodiments.
The present invention is directed to the electrocardioelectrode complex manufacturing process of existing wearable electrocardiogram acquisition measuring device, need that large-scale manufacturing equipment, cost are higher, intelligence detects electrocardiosignal and lacks the problems such as intelligent diagnosis and treatment function under user's resting state, can both the wearable cardiac monitoring diagnosis and treatment method of life-time service and device when providing that a kind of manufacture process is simple, cost is lower and in user's daily life, study, activity and can sleep.
As shown in Figure 1, wearable cardioelectric monitor diagnosing and treating apparatus of the present invention comprises elastic chest bandage 1, conductive fabric electrode 2, central control box 3 and intraconnections composition.
Pectoral girdle adopts the good material of elasticity to make, and the junction button of pectoral girdle is designed to several different gears, can regulate the elasticity of pectoral girdle thus, is applicable to different fat or thin user and uses, wear conveniently, the fastening backward of user oneself both hands.Because the topmost object of this device breast identifies cardiac cycle and carries out heart rate analysis, the detection method so employing single channel leads, pectoral girdle at least configures three textile electrodes, and the shape of textile electrode can be circular, oval or polygon.When wearing pectoral girdle by wherein two textile electrodes are corresponding to left breast and right breast.Central control box is placed in the little pocket of pectoral girdle, along with pectoral girdle is around arriving user back.Central control box is connected with textile electrode by intraconnections, intraconnections adopts the wire or conductive fiber that elasticity is better, size is thinner, to rub and electric conductivity declines in order to avoid intraconnections and Body contact produce, therefore common yarn sewing is covered on intraconnections, form well-regulated interior even track.
As shown in Figure 2, textile electrode of the present invention adopts the mode of printing or direct coated with conductive liquid, and manufacturing process of comparing with the electrode of braiding is simple, does not need large-scale manufacturing equipment, so cost is lower.Utilize the elastic force of the good fabric of the elasticity such as sponge to guarantee that current-carrying part can contact completely with human body skin, utilized by the fabric 4 (as sponge) designing shape the sticky or profit of binding agent to sew with a needle and thread on pectoral girdle 1.The conductive liquid non-stimulated to skin is printed or be directly coated on fabric 4, forms textile electrode layer 5.
As shown in Figure 3, wearable electrocardiograph monitoring device comprises ecg signal acquiring conditioning unit 6, acceleration collecting unit 7, signal processing unit 8, radio communication unit 9, data storage cell 10 and power subsystem 11.Wherein ecg signal acquiring conditioning unit 6 is by electrocardiosignal through amplifying and filtering, selects suitable gain to reach the voltage of AD conversion, is tentatively eliminated the interference noises such as myoelectricity interference, Hz noise and baseline drift by filter circuit; Acceleration collecting unit 7 gathers the acceleration signal of human body, can provide reference signal for the filtering of motion artifacts; The built-in AD conversion module of signal processing unit 8, analogue signal is converted to digital signal processed by signal processing unit, mainly carry out noise filtering, the extraction of signal characteristic and the analysis of calculating and heart rate mutation situation, the data collected are stored the analysis carrying out disease for doctor by data storage cell 10 with portable storage medium; The data handled well send to the cell phone of user, panel computer or personal management equipment, for the heart of user's real-time monitored oneself wirelessly by radio communication unit 9; Power subsystem 11 is ecg signal acquiring conditioning unit 6, acceleration collecting unit 7, calculation processing unit 8 and radio communication unit 9 provide electric energy.
As shown in Figure 4, wearable electrocardiograph monitoring device signal processing unit comprises Filtering of ECG Signal unit 12, signal characteristic abstraction unit 13 and heart mutation diagnosis unit 14.
The noise of filtering completely filtering can not be carried out in Filtering of ECG Signal unit 12 pairs of hardware filtering methods, wherein, because motion artifacts interference belongs to nonstationary random signal, there is dynamic frequency range, and amplitude is larger, use hardware filtering or common software filtering method obviously can not reach the effect of filtering, need a kind of Filter and Filltering algorithm of good performance just can complete, sef-adapting filter is adopted to carry out filtering in the present invention, adopt the reference signal of acceleration signal as sef-adapting filter with motion artifacts signal with dependency, and adopt rational adaptive filter algorithm, realize user and carry out real-time cardioelectric monitor under kinestate,
Preferably, this programme can adopt following sef-adapting filter to realize, but the application is not limited to only realize with following methods: adaptive filter algorithm can be adopted automatically to regulate self weights coefficient W, to reach best filter effect, this sef-adapting filter has two-way input signal, one tunnel is the ECG signal d (k) with motion artifacts interference, and a road is reference signal x (k), and wherein k is time parameter; Wherein, adopt a kind of normalization change step length least mean square error (Least Mean Squares, LMS) algorithm as adaptive filter algorithm, adopt acceleration signal as the reference signal of sef-adapting filter;
According to the structure of sef-adapting filter, the output of sef-adapting filter is the inner product of reference signal and weights coefficient, i.e. y=x
tw, then the output error of whole sef-adapting filter is the difference of input signal and output signal, i.e. e (k)=d (k)-x
tw.LMS algorithm is exactly make the mean-square value of above formula output error be minimum, to reach the suppression of noise signal.Can know that the more new formula of weights coefficient is according to LMS algorithm:
W(k+1)＝W(k)+μe(k)x(k)
Wherein, μ is step factor.The present invention uses a kind of Normalized LMS Algorithm of variable step, and have convergence rate and less steady-state error faster compared with traditional LMS algorithm, the step factor of this algorithm is expressed as:
Therefore the more new formula of weights coefficient becomes:
Utilize the acceleration signal of three axis of orientations (x, y, z) of accelerometer as reference signal in the present invention, i.e. x (k)=[Acc
x(k), Acc
y(k), Acc
z(k)], filter weights coefficient vector W=[w
1, w
2, w
3].
Thus, the step of whole adaptive-filtering is:
The first step: initialize weights coefficient vector W (0)=[0,0,0];
Second step: filter error e (k)=d (the k)-x estimating current time
tw;
3rd step: upgrade filter weights coefficient vector:
4th step: increased by time parameter k, to time next, repeats step above, until reach iterations.
Signal characteristic abstraction unit 13 adopts signal character detection algorithm from filtered electrocardiosignal, to extract important signal characteristic, as the R ripple, heart rate etc. of amplitude maximum; Heart mutation diagnosis unit 14 pairs of signal characteristics carry out the analysis of time domain and frequency domain, obtain the statistical indicator about electrocardiosignal, machine learning classification algorithm is utilized to carry out Classification and Identification to the heart state of user, and doctor can be sent to by wireless communication module 9, the suggestion of diagnostic result and the prevention state of an illness is fed back by doctor.
As shown in Figure 5, the human body ambulatory ecg signal utilizing textile electrode to collect is as the input signal source 14 of wave filter, it is the composite signal of desirable electrocardiosignal and motion artifacts noise, using the 3-axis acceleration signal 15 that arrives with the ambulatory ecg signal synchronous acquisition reference signal as sef-adapting filter, then use sef-adapting filter 16 to carry out the suppression of motion artifacts, obtain comparatively pure electrocardiosignal.
As shown in Figure 6, the electrocardiosignal monitoring method based on wearable electrocardiosignal monitoring device in the present invention realizes mainly through following steps:
1), signal collection modulation
Gather electrocardiosignal by ecg signal acquiring conditioning unit, and the process of the interference noises such as myoelectricity interference, Hz noise and baseline drift is tentatively eliminated to the electrocardiosignal collected; And gather acceleration signal by acceleration collecting unit;
2), motion artifacts suppresses
By adaptive filter algorithm, for step 1) in the electrocardiosignal that obtains, the motion artifacts interference noise that human body produces under moving situation is suppressed; Wherein, adopt acceleration signal as the reference signal of sef-adapting filter;
3), signal characteristic abstraction
By step 2) the middle electrocardiosignal after motion artifacts suppresses obtained, extract important signal characteristic by signal character detection algorithm, this signal characteristic at least comprises R ripple, the heart rate of amplitude maximum; By property data base local for those signal characteristics composition, for analysis and the assessment of heart rate mutation;
4), the analysis of cardiac conditions and assessment
By step 3) in the parameter values composition characteristic matrix of each feature of electrocardiosignal that obtains, these signal characteristics are carried out to the analysis of time domain and frequency domain, obtain the statistical indicator of electrocardiosignal, and according to this statistical indicator, Classification and Identification is carried out to the heart state of user.
In step 4) in, adopt machine learning classification algorithm, the methods such as such as neutral net, according to the statistical indicator of electrocardiosignal, carry out Classification and Identification to the heart state of user.
5), by the electrocardiosignal after signal processing and the heart state Classification and Identification result to user, by being wirelessly sent to doctor, and the suggestion of diagnostic result and the prevention state of an illness is fed back by doctor.
Preferably, step 2) adaptive filter algorithm automatically regulate self weights coefficient W, to reach best filter effect, this sef-adapting filter adopts two-way input signal, one tunnel is the ECG signal d (k) with motion artifacts interference, one tunnel is reference signal x (k), and wherein k is time parameter.
Above-mentioned adaptive filter algorithm can adopt normalization change step length least mean square ERROR ALGORITHM,
The output of the sef-adapting filter of this adaptive filter algorithm is the inner product of reference signal x (k) and weights coefficient W, i.e. y=x
tw, the output error of sef-adapting filter is e (k)=d (k)-x
tw; The more new formula of weights coefficient is:
The step of above-mentioned adaptive filter algorithm is,
The first step: initialize weights coefficient vector W (0)=[0,0,0];
Second step: filter error e (k)=d (the k)-x estimating current time
tw;
3rd step: upgrade filter weights coefficient vector:
4th step: increased by time parameter k, to time next, repeats step above, until reach iterations.
Preferably, the acceleration signal of three axis of orientations (x, y, z) of accelerometer can be utilized as reference signal, i.e. x (k)=[Acc
x(k), Acc
y(k), Acc
z(k)], filter weights coefficient vector W=[w
1, w
2, w
3].
The above is the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from principle of the present invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (16)
1. a wearable electrocardiosignal monitoring device, is characterized in that: described device is made up of elastic chest bandage, flexible fabric electrocardioelectrode, central control box and intraconnections, wherein,
Described flexible fabric electrocardioelectrode is arranged at appropriate location inside pectoral girdle, and electrode is projection setting, thus ensures that electrode contacts completely with skin;
Described central control box at least comprises ecg signal acquiring conditioning unit, acceleration collecting unit, signal processing unit, radio communication unit;
Described ecg signal acquiring conditioning unit, for gathering electrocardiosignal, carries out Hz noise, baseline drift to the electrocardiosignal collected and carries out noise pretreatment to myoelectricity interference, and the electrocardiosignal after process is carried out the processing and amplifying of gain;
Described acceleration collecting unit is for gathering acceleration signal;
The electrocardiosignal after treatment that described signal processing unit obtains based on described ecg signal acquiring conditioning unit and the acceleration signal that acceleration collecting unit gathers, carry out the analysis of the filtering of electrocardiosignal, feature extraction and heart mutation situation;
Described radio communication unit is used for data to be after treatment wirelessly sent to receiving terminal;
Described intraconnections connects flexible fabric electrocardioelectrode and central control box.
2. device according to claim 1, is characterized in that:
Described monitoring device adopts single channel to lead detection method, and pectoral girdle at least configures three flexible fabric electrocardioelectrodes, wherein two electrode pairs should at left breast and right breast.
3. device according to claim 1, is characterized in that:
The suppression that the acceleration signal that described acceleration collecting unit collects disturbs for motion artifacts.
4. device according to claim 1, is characterized in that:
Described intraconnections, adopts wire or conductive fiber, and common yarn sewing is covered on intraconnections, connects track in formation.
5. device according to claim 1, is characterized in that:
Described flexible fabric electrocardioelectrode adopts the mode of printing or direct coated with conductive liquid to make.
6. require the device described in 1 according to profit, it is characterized in that:
Described central control box also comprises data storage cell, power subsystem; Described memory element is stored in the analytical data of data, feature and the heart mutation situation produced in electrocardiosignal observation process.
7. device according to claim 1, is characterized in that:
Described receiving terminal is mobile terminal, fixing PC, personal digital assistant, notebook computer, panel computer.
8. device according to claim 1, is characterized in that:
The shape of described flexible fabric electrocardioelectrode is circular, oval or polygon.
9. device according to claim 1, is characterized in that:
Described signal processing unit comprises:
Filtering of ECG Signal unit, adopts adaptive filter method to suppress the motion artifacts interference noise that human body produces under moving situation, wherein using acceleration signal as the reference signal of sef-adapting filter;
Signal characteristic abstraction unit, adopt signal character detection algorithm to extract important signal characteristic from filtered electrocardiosignal, this signal characteristic at least comprises R ripple, the heart rate of amplitude maximum;
Heart mutation diagnosis unit, for detecting the parameter values of each feature of electrocardiosignal, these signal characteristics are carried out to the analysis of time domain and frequency domain, obtain the statistical indicator about electrocardiosignal, utilize machine learning classification algorithm to carry out Classification and Identification to the heart state of user.
10. the electrocardiosignal monitoring method based on wearable electrocardiosignal monitoring device, described device at least comprises flexible fabric electrocardioelectrode, ecg signal acquiring conditioning unit, acceleration collecting unit, signal processing unit, radio communication unit and intraconnections, it is characterized in that:
1), signal collection modulation
Gather electrocardiosignal by ecg signal acquiring conditioning unit, and the process of the interference noises such as myoelectricity interference, Hz noise and baseline drift is tentatively eliminated to the electrocardiosignal collected; And gather acceleration signal by acceleration collecting unit;
2), motion artifacts suppresses
By adaptive filter algorithm, for step 1) in the electrocardiosignal that obtains, the motion artifacts interference noise that human body produces under moving situation is suppressed; Wherein, adopt acceleration signal as the reference signal of adaptive filter algorithm;
3), signal characteristic abstraction
By step 2) the middle electrocardiosignal after motion artifacts suppresses obtained, extract important signal characteristic by signal character detection algorithm, this signal characteristic at least comprises R ripple, the heart rate of amplitude maximum; By property data base local for those signal characteristics composition, for analysis and the assessment of heart rate mutation;
4), the analysis of cardiac conditions and assessment
By step 3) in the parameter values composition characteristic matrix of each feature of electrocardiosignal that obtains, these signal characteristics are carried out to the analysis of time domain and frequency domain, obtain the statistical indicator of electrocardiosignal, and according to this statistical indicator, Classification and Identification is carried out to the heart state of user.
11. methods according to claim 10, is characterized in that:
Described step 4) in, adopt machine learning classification algorithm, according to the statistical indicator of electrocardiosignal, Classification and Identification is carried out to the heart state of user.
12. methods according to claim 10, is characterized in that:
Also comprise step 5), by the electrocardiosignal after signal processing and the heart state Classification and Identification result to user, by being wirelessly sent to doctor, and by doctor feed back diagnostic result and prevention the state of an illness suggestion.
13. methods as claimed in claim 10, it is characterized in that: described step 2) adaptive filter algorithm automatically regulate self weights coefficient W, to reach best filter effect, this sef-adapting filter adopts two-way input signal, one tunnel is the ECG signal d (k) with motion artifacts interference, one tunnel is reference signal x (k), and wherein k is time parameter.
14. methods as claimed in claim 13, is characterized in that: described adaptive filter algorithm adopts normalization change step length least mean square ERROR ALGORITHM,
The output of the sef-adapting filter of this adaptive filter algorithm is the inner product of reference signal x (k) and weights coefficient W, i.e. y=x
tw, the output error of sef-adapting filter is e (k)=d (k)-x
tw; The more new formula of weights coefficient is:
15. methods as claimed in claim 14, is characterized in that: the step of described adaptive filter algorithm is,
The first step: initialize weights coefficient vector W (0)=[0,0,0];
Second step: filter error e (k)=d (the k)-x estimating current time
tw;
3rd step: upgrade filter weights coefficient vector:
4th step: increased by time parameter k, to time next, repeats step above, until reach iterations.
16. methods as claimed in claim 13, is characterized in that: utilize the acceleration signal of three axis of orientations (x, y, z) of accelerometer as reference signal, i.e. x (k)=[Acc
x(k), Acc
y(k), Acc
z(k)], filter weights coefficient vector W=[w
1, w
2, w
3].
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