CN103222864A - Self-adaption electrocardiograph (ECG) detection method and monitoring system thereof - Google Patents

Abstract

The invention discloses a self-adaption electrocardiograph (ECG) detection method and a monitoring system thereof. The detection method comprises the following steps: performing band-pass filtering, absolute value operation and low-pass filtering on an output ECG signal of an acquisition module; and detecting the R wave of the signal subjected to low-pass filtering by setting a threshold value. The monitoring system comprises an ECG acquisition module, intelligent equipment and a remote terminal, wherein the ECG acquisition module comprises an ECG pre-amplification circuit, a 50-Hz wave trap, a secondary amplification circuit, an anti-aliasing filter circuit, an analog/digital (A/D) conversion circuit, a microcontroller and a Bluetooth module; the microcontroller is connected with the intelligent equipment through the Bluetooth module; and the intelligent equipment loads the self-adaption ECG detection method and is connected with the remote terminal through a network. The method is simple and quick and suitable for a low-end microprocessor. When the method is used in combination with the monitoring system, a portable ECG monitoring system can be implemented and has the characteristics of multiple functions, low power consumption, low cost, small volume and the like.

Description

A kind of self adaptation electrocardio detection method and monitoring system thereof

Technical field

The present invention relates to the medical apparatus and instruments watch-dog, more specifically, relate to a kind of self adaptation electrocardio detection method and monitoring system thereof.

Background technology

Heart disease is the No.1 killer who threatens the human life always, is one of the highest disease of M ﹠ M.Along with the raising of living standard and health perception, people wish and can carry out health supervision and can carry out diagnosis and treatment timely under more critical situation heart at any time.So portable mobile wireless electrocardio Monitoring System Development has very important significance.Portable medical is a useful potential strong tool improving aspect the health care quality.

The at present both at home and abroad popular a variety of methods that electrocardiosignal is handled all have in various degree a bit and shortcoming.QRS detection, neural net method, sef-adapting filter, hidden Markov model, matched filter, genetic method, Hilbert transform, length and Conversion of energy based on wavelet transformation, these methods all have a common characteristic, that is exactly the complexity height of method, be unfavorable for real-time processing, microprocessor is had higher requirement digital signal.

Present domestic each big medical machinery manufacturer and R﹠D institution, all in the research and development of electrocardiograph, dropped into a large amount of resources, and all researched and developed the portable cardiac instrument product that differs from one another, as the HC-201 standard edition of beneficial body health (Beijing) Science and Technology Ltd. and the ECG Universal12-Lead PC-Basado Portable ECG of U.S. DRE company.But all do not obtain well popularizing, trace it to its cause, have the problem of the following aspects:

1. the electrocardio processing capacity that provides and the cardiac electrical information of record are limited, and the doctor is difficult to therefrom obtain comprehensive ecg information, has reduced doctor's the accuracy to medical diagnosis on disease.

2. generally all adopted the core devices of digital signal processor as the electrocardiogram (ECG) data analysis, and data communication, liquid crystal display, real-time clock, program storage etc. all need to extend out special functional device, thereby the structure more complicated, volume ratio is bigger, power consumption is also bigger simultaneously, the price comparison costliness, and general patient is difficult to bear.

3. supporting with it monitoring network and electrocardiogram (ECG) data processing center are not perfect, can not catch up with the paces of Internet of Things New Times.

4. also need the doctor to come the analytical judgment ecg wave form with this, be difficult to allow electrocardiograph step into ordinary various schools of thinkers.There is defective in the ecg analysis method of present stage, and the phenomenon of erroneous judgement heart rate exists.

Summary of the invention

Main purpose of the present invention is to propose a kind of self adaptation electrocardio detection method, and this method makes can be monitored a large amount of ecg signal datas when using the microprocessor of low side.

To achieve these goals, its technical scheme is:

A kind of self adaptation electrocardio detection method may further comprise the steps:

S1. the output ECG signal to acquisition module carries out bandpass filtering;

S2. the computing that takes absolute value of the signal after step S1 being handled;

S3. the signal after step S2 being handled carries out low-pass filtering;

S4. the R ripple in the signal after setting threshold detects step S3 and handles.

Preferably, adopt the band filter of 10～30Hz to carry out bandpass filtering among the described step S1, the transfer function of band filter is H _Bandpass(z), filtering is output as Y ₁(z), the ECG signal sequence of acquisition module output is x[n], it is carried out the z conversion X (z); Y is then arranged ₁(z)=H _Bandpass(z) X (z).

Described band filter is the Butterworth wave filter;

If the system function of Butterworth band filter is: $H_{\mathrm{band}}\left(s\right)=\frac{1}{B_n\left(\frac{s^2+{\mathrm{\ω}}_l{\mathrm{\ω}}_h}{s({\mathrm{\ω}}_h-{\mathrm{\ω}}_l)}\right)}$

Wherein ${\mathrm{\ω}}_l={2f}_s\tan \left(\frac{{\mathrm{\πf}}_l}{f_s}\right),$ ${\mathrm{\ω}}_h={2f}_s\tan \left(\frac{{\mathrm{\πf}}_h}{f_s}\right),$ f _lBe low cut-off frequency, f _hBe high cut-off frequency, f _sBe sample rate, n is filter order, wherein B _n(s) be defined as:

N is an even number, $B_n\left(s\right)=\underset{k=1}{\overset{\frac{n}{2}}{\mathrm{\Π}}}[s^2-2s\cos \left(\frac{2k+n-1}{2n}\mathrm{\π}\right)+1],$

N is an odd number, $B_n\left(s\right)=(s+1)\underset{k=1}{\overset{\frac{n-1}{2}}{\mathrm{\Π}}}[s^2-2s\cos \left(\frac{2k+n-1}{2n}\mathrm{\π}\right)+1],$

By to H _Band(s) carry out bilinear transformation, obtain the transfer function of Butterworth band filter, promptly $H_{\mathrm{bandpass}}\left(z\right)=H_{\mathrm{hand}}\left({2f}_s\frac{z-1}{z+1}\right).$

Preferably, described step S2 comprises that also the signal to step S1 output carries out discretization, obtains Y ₁(z) Dui Ying discrete series y ₁[n] is to discrete series y ₁[n] computing that takes absolute value, establishing output sequence is y ₂[n] then has y ₂[n]=| y ₁[n] |.

Preferably, adopting cut-off frequency among the described step S3 is the low pass filter of 5Hz, and establishing its transfer function is H _Lowpass(z), to the output signal y among the step S2 ₂[n] carries out the z conversion and obtains Y ₂(z), advanced low-pass filtering, and established filtering and be output as Y ₃(z), Y then ₃(z)=H _Lowpass(z) Y ₂(z).

Described low pass filter adopts the Butterworth low pass filter

If the system function of Butterworth low pass filter is

Wherein

F is a cut-off frequency, f _sBe sample rate, n is filter order, wherein B _n(s) be defined as:

N is an even number, $B_n\left(s\right)=\underset{k=1}{\overset{\frac{n}{2}}{\mathrm{\Π}}}[s^2-2s\cos \left(\frac{2k+n-1}{2n}\mathrm{\π}\right)+1]$

N is an odd number, $B_n\left(s\right)=(s+1)\underset{k=1}{\overset{\frac{n-1}{2}}{\mathrm{\Π}}}[s^2-2s\cos \left(\frac{2k+n-1}{2n}\mathrm{\π}\right)+1]$

By to H _Low(s) carry out bilinear transformation, obtain the transfer function of Butterworth low pass filter, promptly $H_{\mathrm{lowpass}}\left(z\right)=H_{\mathrm{low}}\left({2f}_s\frac{z-1}{z+1}\right).$

Preferably, the specific implementation of described step S4 is: to the Y of step S3 output ₃(z) carry out the z inverse transformation and obtain its corresponding discrete series y ₃[n], for the maximum point of filtering output sequence, establishing maximum point is i, wherein y ₃[i]〉y ₃[i-1] and y ₃[i]〉y ₃[i+1] establishes

The number of choosing maximum point is N, threshold value

A is used to be provided with the threshold value of meansigma methods proportion, detects the R ripple, to y ₃[i] is if y ₃[i]〉h, then i is filtered electrocardiosignal sequences y ₃R ripple place sequence number in [n].

Another purpose of the present invention is to propose a kind of monitoring system of using above-mentioned electrocardio detection method, and that this monitoring system has is multi-functional, low-power consumption, become low and characteristics such as volume is little.

Its technical scheme is:

A kind of monitoring system of application self-adapting electrocardio detection method comprises the electrocardiogram acquisition module that connects in turn, and smart machine and remote terminal are mounted with self adaptation electrocardio detection method on the described smart machine.

Preferably, described electrocardiogram acquisition module comprises and connects electrocardio pre-amplification circuit, 50Hz wave trap, second amplifying circuit, anti-aliasing filter circuit, A/D change-over circuit, microcontroller and bluetooth module in turn that described microcontroller is connected with smart machine by bluetooth module.

Preferably, described electrocardiogram acquisition module also comprises key-press module and display module, and key-press module is connected with microcontroller by data/address bus with display module.

Preferably, described microcontroller is a single-chip microcomputer.

Preferably, described smart machine is connected with remote terminal by network, and described network is GSM, WIFI, 3G or 4G network.

Preferably, described smart machine is smart mobile phone or the computer with bluetooth communication function.

Compared with prior art, beneficial effect of the present invention is: the electrocardio detection method that the present invention proposes is simply quick, the microprocessor that is fit to low side uses, with being used of its monitoring system under, can realize the portable cardiac monitoring system and have multi-functional, low-power consumption, low cost, characteristics such as volume is little.And monitoring patient's that can be at any time electrocardio situation, patient's safety is had great guarantee.

Description of drawings

Fig. 1 is the flow chart of self adaptation electrocardio detection method.

Fig. 2 is the structured flowchart of self adaptation electrocardio monitoring system.

Fig. 3 detects design sketch for this detection method to the R ripple with the interferential electrocardiosignal of myoelectricity.

Fig. 4 detects design sketch for this detection method to the R ripple with the interferential electrocardiosignal of power frequency.

Fig. 5 detects design sketch for this detection method to the R ripple of electrocardiosignal with baseline drift.

Fig. 6 detects design sketch for this detection method to the R ripple with the interferential electrocardiosignal of step saltus step.

Fig. 7 detects design sketch for this detection method to the R ripple with myoelectricity, power frequency, baseline drift, the interferential electrocardiosignal of step saltus step.

Fig. 8 is detection method of the present invention and the classic algorithm detection design sketch to clean electrocardiosignal.

Fig. 9 for detection method of the present invention and classic algorithm to being subjected to the detection design sketch of interferential electrocardiosignal.

Figure 10 is the detection design sketch of detection method to ecg-r wave in the conscience electricity consumption monitoring system.

(curve 1 is the original waveform that collects electrocardiosignal, and curve 2 is R ripple positions that this algorithm detects and marks, and curve 3 and curve 4 are demonstrations of this algorithmic procedure waveform)

The specific embodiment

Below in conjunction with accompanying drawing the present invention is described further, but embodiments of the present invention are not limited to this.

Fig. 1 is the flow chart of self adaptation electrocardio detection method, may further comprise the steps:

S1. the output ECG signal to acquisition module carries out bandpass filtering;

S2. the computing that takes absolute value of the signal after step S1 being handled;

S3. the signal after step S2 being handled carries out low-pass filtering;

S4. the R ripple in the signal after setting threshold detects step S3 and handles.

Present embodiment adopts the employing rate of 512Hz that electrocardiosignal is sampled, and band filter is selected for use among the step S1 is that the passband on 6 rank is the Butterworth wave filter of 10Hz～30Hz, low cut-off frequency f _l=10Hz, higher cut off frequency f _h=30Hz, sample rate f _s=512Hz, the substitution formula

Can calculate ω _lWith ω _h, substitution again $H_{\mathrm{band}}\left(s\right)=\frac{1}{B_n\left(\frac{s^2+{\mathrm{\ω}}_l{\mathrm{\ω}}_h}{s({\mathrm{\ω}}_h-{\mathrm{\ω}}_l)}\right)}$ Can get concrete system function.

By to H _Band(s) carry out bilinear transformation,

Obtain the transfer function H of Butterworth band filter _Bandpass(z) expression formula, further electrocardiosignal sequence x[n to collecting] carry out the z conversion X (z) is arranged, establish filtering and be output as Y ₁(z), Y is then arranged ₁(z)=H _Bandpass(z) X (z).

The computing that takes absolute value among the step S2 is for the filtering output Y of step S1 ₁(z) carry out the z inverse transformation its corresponding discrete series y is arranged ₁[n] is to discrete series y ₁[n] computing that takes absolute value, establishing output sequence is y ₂[n] then has y ₂[n]=| y ₁[n] |.

Adopt the Butterworth low pass filter of 6 rank cut-off frequency f=5Hz among the step S3, the substitution formula can be calculated the system function H of Butterworth low pass filter _Low(s), then it is carried out bilinear transformation, Obtain the transfer function H of Butterworth low pass filter _Lowpass(z), the signal y that step S3 is exported ₂[n] carries out the z conversion and obtains Y ₂(z).Through low-pass filtering, establish filtering and be output as Y ₃(z), Y then ₃(z)=H _Lowpass(z) Y ₂(z).

Y to S3 output ₃(z) carry out the z inverse transformation and obtain its corresponding discrete series y ₃[n], for the maximum point of filtering output sequence, establishing maximum point is i, wherein y ₃[i]〉y ₃[i-1] and y ₃[i]〉y ₃[i+1] establishes

The number of choosing maximum point is N, threshold value

A is another threshold value, detects the R ripple, to y ₃[i] is if y ₃[i]〉h, then i is filtered electrocardiosignal sequences y ₃R ripple place sequence number in [n].

As shown in Figure 2, a kind of monitoring system based on self adaptation electrocardio detection method, it comprises: the electrocardiogram acquisition module, smart machine and remote terminal, the electrocardiogram acquisition module is by the electrocardio pre-amplification circuit, the 50Hz wave trap, second amplifying circuit, the anti-aliasing filter circuit, the A/D change-over circuit, microcontroller and bluetooth module are formed, described electrocardio pre-amplification circuit once passes through the 50Hz wave trap gather electrocardiosignal from human body after, second amplifying circuit, the anti-aliasing filter circuit, the A/D change-over circuit, pass to microcontroller, described microcontroller is connected with smart machine by bluetooth module; Described smart machine is mounted with self adaptation electrocardio detection method and is connected by network with remote terminal.

Use the INA332 amplifying circuit of 3V running voltage that the electrocardiosignal of gathering is amplified 10 times.

Filter circuit adopts the adjustable active band elimination filter of asymmetric double T of Q-value, and Q-value is adjusted to suitable position, makes filter circuit have 50 hertz trap function.After the more powerful interfering signal of elimination, next adopt an in-phase amplification circuit that has the low-pass filtering function that the signal of telecommunication is amplified 100 times again, signal is reached more than the V level.

Adopt 8 tunnel 10 AD modular converter to change the electrocardiosignal dress into digital signal, be transferred in the master controller of MSP430.

Use the bluetooth module of specialty, this module size is little, and power consumption is little, adopts serial ports to communicate by letter with MSP430.MSP430 sends to the electrocardiogram (ECG) data that collects on the smart machine by bluetooth module.

Software utilization new method default on the smart machine is carried out the extraction of digital filtering and ecg-r wave with electrocardiogram (ECG) data, finally is presented on the device screen.Smart machine monitoring patient's at any time that so just can be by having Bluetooth function at one's side electrocardio.

The electrocardiosignal of human body is through the ECG preamplifier of high impedance, high common mode inhibition capacity, processing through the 50Hz trap circuit, the taking-up power frequency interference signals that obtains, this signal is again by the stronger two-stage amplifier of amplifying power, this moment, signal voltage reached the volt level, for the system that makes satisfies the sampling law, signal also must add frequency overlapped-resistable filter before entering AD.Data after the AD sampling are given main control chip MSP430, and data are delivered on the smart mobile phone by bluetooth module through the packing back by MSP430.The electrocardiosignal that on the mobile phone reception is collected utilizes this method to detect the R ripple, thereby calculate the beats of per minute, and can be selectively ecg information be sent on relatives' mobile phone or medical worker's the computer by GSM network or WIFI network mode.If patient's heart beating goes wrong, the smart mobile phone that the patient carries can be analyzed the electrocardiosignal estimate of situation, reports to the police or picture remote terminal transmission distress signal.

Above-described embodiments of the present invention do not constitute the qualification to protection domain of the present invention.Any modification of within spiritual principles of the present invention, having done, be equal to and replace and improvement etc., all should be included within the claim protection domain of the present invention.

Claims (10)

1. a self adaptation electrocardio detection method is characterized in that, may further comprise the steps:

S1. the output ECG signal to acquisition module carries out bandpass filtering;

S2. the computing that takes absolute value of the signal after step S1 being handled;

S3. the signal after step S2 being handled carries out low-pass filtering;

S4. the R ripple in the signal after setting threshold detects step S3 and handles.

2. self adaptation electrocardio detection method according to claim 1 is characterized in that, adopts the band filter of 10～30Hz to carry out bandpass filtering among the described step S1, and the transfer function of band filter is H _Bandpass(z), filtering is output as Y ₁(z), the ECG signal sequence of acquisition module output is x[n], it is carried out the z conversion X (z); Y is then arranged ₁(z)=H _Bandpass(z) X (z).

3. self adaptation cardioelectric monitor method according to claim 2 is characterized in that, described step S2 comprises that also the signal to step S1 output carries out discretization, obtains Y ₁(z) Dui Ying discrete series y ₁[n] is to discrete series y ₁[n] computing that takes absolute value, establishing output sequence is y ₂[n] then has y ₂[n]=| y ₁[n] |.

4. self adaptation cardioelectric monitor method according to claim 3 is characterized in that, adopting cut-off frequency among the described step S3 is the low pass filter of 5Hz, and establishing its transfer function is H _Lowpass(z), to the output signal y among the step S2 ₂[n] carries out the z conversion and obtains Y ₂(z), through low-pass filtering, establish filtering and be output as Y ₃(z), Y then ₃(z)=H _Lowpass(z) Y ₂(z).

5. self adaptation cardioelectric monitor method according to claim 4 is characterized in that the specific implementation of described step S4 is: to the Y of step S3 output ₃(z) carry out the z inverse transformation and obtain its corresponding discrete series y ₃[n], for the maximum point of filtering output sequence, establishing maximum point is i, wherein y ₃[i]〉y ₃[i-1] and y ₃[i]〉y ₃[i+1] establishes

The number of choosing maximum point is N, and threshold value h=a * dN, a are used to be provided with the threshold value of meansigma methods proportion, detects the R ripple, to y ₃[i] is if y ₃[i]〉h, then i is filtered electrocardiosignal sequences y ₃R ripple place sequence number in [n].

6. an application rights requires the monitoring system of the described self adaptation electrocardio of 1-5 detection method, comprise the electrocardiogram acquisition module that connects in turn, smart machine and remote terminal is characterized in that, are mounted with the described self adaptation electrocardio of claim 1-5 detection method on the described smart machine.

7. monitoring system according to claim 6, it is characterized in that, described electrocardiogram acquisition module comprises and connects electrocardio pre-amplification circuit, 50Hz wave trap, second amplifying circuit, anti-aliasing filter circuit, A/D change-over circuit, microcontroller and bluetooth module in turn that described microcontroller is connected with smart machine by bluetooth module.

8. monitoring system according to claim 6 is characterized in that, described electrocardiogram acquisition module also comprises key-press module and display module, and key-press module is connected with microcontroller by data/address bus with display module; Described microcontroller is a single-chip microcomputer.

9. monitoring system according to claim 6 is characterized in that described smart machine is connected with remote terminal by network, and described network is GSM, WIFI, 3G or 4G network.

10. monitoring system according to claim 6 is characterized in that, described smart machine is smart mobile phone or the computer with bluetooth communication function.

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