CN103110422A - Breath and heartbeat real-time separating method based on biological radar detection - Google Patents
Breath and heartbeat real-time separating method based on biological radar detection Download PDFInfo
- Publication number
- CN103110422A CN103110422A CN2012105523514A CN201210552351A CN103110422A CN 103110422 A CN103110422 A CN 103110422A CN 2012105523514 A CN2012105523514 A CN 2012105523514A CN 201210552351 A CN201210552351 A CN 201210552351A CN 103110422 A CN103110422 A CN 103110422A
- Authority
- CN
- China
- Prior art keywords
- signal
- breath
- breath signal
- harmonic
- breathing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a breath and heartbeat real-time separating method based on biological radar detection. Breath and heartbeat signals can be separated from body moving signals in real time after the breath and body moving signals which are detected by a biological radar are pre-processed, harmonic wave of the breath signals is detected, self-adaptive harmonic wave is counteracted and the like, non-contacting and real-time detection on the breath and heartbeat signals are achieved and thereby requirements of biological signal real-time monitoring on clinical patients (burning or infection), senior person in families and other patients of chronic diseases are met.
Description
Technical field
The present invention relates to a kind of bioradar detection technique of utilizing in the physiology signal non-contact detecting, carry out real-time method of separating with breathing with heart beating.
Background technology
Along with the needs of biomedical engineering, radar, electronics, computer technology and military affairs, medical science, social development, many Chinese scholars propose the new concept of a kind of physiological signal Detection Techniques---bioradar (Bioradar).This technological incorporation Radar Technology, biomedical engineering technology are in one, penetrable nonmetal medium (clothes, hospital gauze etc.), without any need for electrode or sensor contact life entity, can detect the physiological signal (as breathing, body is moving etc.) of human body in larger distance, realize the non-contact detecting of physiological signal.Therefore, this technology can be widely used in the occasions such as clinical monitoring, home health care.
Bioradar generally comprises front end, antenna, signal condition hardware and back end signal processing software part, under the prerequisite of the hardware performance index optimums such as front end, signal conditioner, how can separate in real time in back end signal is processed and to breathe and heartbeat signal, bioradar applying in clinical physiological signal or home health care had conclusive effect, therefore be subject to the attention of countries in the world height, research worker has proposed diverse ways and has separated with the real-time of heart beating for bioradar detection breathing.Mainly can be divided into according to the method that adopts: (1) time-domain digital filter method; (2) frequency domain filtering method; (3) wavelet decomposition and Reconstruction Method.Wherein, time-domain filtering method is to adopt the FIR(finite impulse response) digital filter directly arranges two digital filters with the moving signal of body, the cut-off frequency of the wave digital lowpass filter that respiration channel is corresponding is 0.5Hz, and the digital band-pass filter lower-cut-off frequency that the heart beating passage is corresponding is that 0.6Hz and upper cut-off frequency are 3.3Hz.The advantage of the method be algorithm simple, be easy to realize, but have two defectives: the firstth, instantaneous heartbeat signal can not obtain in real time, main cause is that the FIR wave filter exists certain signal time delay when design; The secondth, will cause the partial information of heartbeat signal lose, main cause be near the heartbeat signal of filter cutoff frequency also will be by filtering; Above two defectives make the isolated breathing of FIR digital filtering method and heartbeat signal, can not be applied to the requirement (requiring physiological parameter real-time and accuracy) of clinical physiological signal monitoring.The frequency domain filtering method is that breathing and the moving signal of body that bioradar detects are carried out the FFT(fast Fourier transform) calculate, then according to the spectrum width of breathing, frequency domain filter is set the frequency spectrum that body moves signal is carried out filtering, therefrom isolate the frequency spectrum of heartbeat signal, the last time domain waveform that therefrom extracts again heartbeat signal through contrary FFT conversion.The advantage of the method is to isolate heartbeat signal exactly from the moving signal of body, and directly obtains the instantaneous heart rate value.But also there are two problems: the first, be to satisfy and to calculate fast FFT direct transform and inverse transformation, to need the people be zero padding or reduce data length, easily produces data redundancy and increase operand, breathes the real-time of separating with heartbeat signal thereby reduce; Second: when the fundamental frequency of the higher hamonic wave of breathing and heartbeat signal is overlapping, can't move effective filtering breath signal frequency spectrum from body, this situation is very many in the clinical cardiovascular patient, so the method can not satisfy the monitoring requirement of clinical physiological signal.Wavelet method is the frequency band according to breath signal, selects suitable wavelet packet to carry out Time Domain Decomposition the moving signal of body, then will breathe the signal zero setting in frequency band, and will remain other signal components and carry out wavelet reconstruction, finally synthesizes heartbeat signal.The advantage of the method is that capacity of resisting disturbance is strong, and can be with all information completely ground reconstruct of heartbeat signal, but is difficult to select wavelet packet during wavelet decomposition, and algorithm is complicated, operand is large, can not realize breathing with the real-time of heartbeat signal and separate.Therefore, the method does not satisfy the monitoring requirement of clinical physiological signal yet.
Summary of the invention
The objective of the invention is in the physiology signal non-contact detecting, particularly utilize in the process of bioradar detection, the real-time separation method of the simple breathing of a kind of algorithm and heart beating is provided, can isolate in real time breathing and heartbeat signal from the moving signal of body, thereby satisfy the requirement of clinical patient (burn, infection), the old man of family and other chronics' physiological signal Real-Time Monitoring.
For reaching above purpose, the present invention takes following technical scheme to be achieved:
A kind of breathing and heart beating real-time separation method that detects based on bioradar is characterized in that, comprises the steps:
(1) the moving signal of the breath signal that bioradar is detected and body carries out respectively pretreatment, breathes and the frequency range of the moving signal of body is limited in 5Hz, the amplitude of signal is limited to-1V extremely+1V, pretreatment comprises digital filtering and normalized;
(2) pretreated breath signal is through breathing the harmonic wave detection module, therefrom extract the above higher hamonic wave of secondary of breath signal, comprise that the breath signal first-harmonic is estimated and the breath signal harmonic wave is synthetic, wherein, the breath signal first-harmonic estimates to adopt auto-correlation algorithm to find the solution fundamental frequency; The synthetic Gauss-Newton algorithm that adopts of breath signal harmonic wave;
(3) output that will breathe the harmonic wave detection module is sent into self adaptation harmonic cancellation module and as its reference-input signal; The moving signal of pretreated body is sent into self adaptation harmonic cancellation module and as its original input signal, constantly adjust and upgrade the parameter of sef-adapting filter by adaptive algorithm, respiratory component in the moving signal of body and the difference of the harmonic components of the breath signal of reference input square hour, the output of sef-adapting filter is exactly heartbeat signal at this moment.
In said method, the described digital filtering of step (1) adopts the Butterworth iir filter; Normalized is carried out as follows: y=(x-Min)/(Max-Min), and wherein x is the breath signal after digital filtering, and y is signal after normalization, and Max and Min represent respectively to breathe after filtering and body moves maximum and the minima of signal.
The idiographic flow that the described breathing harmonic wave of step (2) detects is as follows:
The first step: the fundamental frequency of finding the solution breath signal
Choose suitable window function breath signal is carried out segment processing, to asking auto-correlation function in each data segment, then auto-correlation function is carried out power Spectral Estimation, the corresponding Frequency point of the maximum of power spectrum energy is the fundamental frequency f of breath signal
0, then from front to back, successively breath signal is carried out windowing, self correlation, asks the processing such as power spectrum, to f
0Constantly upgrade;
Second step: build breath signal higher hamonic wave mathematical model
The model definition of the movement of thorax that respiratory movement is caused is:
In formula: a represents breath signal, and n represents sampling instant, ω
0Be the first-harmonic angular frequency of breath signal,
f
0The fundamental frequency of expression breath signal, f
sThe expression sample frequency, l ω
0Be the frequency of l subharmonic, a
iBe [a
1, a
2... .a
2l+1]
TVector, a
2lRepresent the phase place of l subharmonic, a
2l-1Represent the amplitude of l subharmonic, a
2l+1Represent the DC component of l subharmonic;
The 3rd step: utilize Gauss-Newton algorithm to find the solution a in the second step mathematical model
iValue, the f that constantly will upgrade
0Value is carried out interative computation, when satisfying the optimal solution of Gauss-Newton, and the higher hamonic wave function of the breath signal that obtains synthesizing.
The described adaptive algorithm of step (3) adopts the LMS algorithm, step size mu=0.00002, and it is as follows that the exponent number of wave filter is got 20. concrete calculation procedures:
The filter factor vector of definition time n=0 is initial value W (0), then carries out iteration:
A, by the estimated value of the filter filtering coefficient vector of current time n, input breath signal harmonic vector x (n) and body move signal d (n), error signal:
B, the renewal valuation by recurrence method calculating filter coefficient vector again:
C, time index n is increased by 1, then begin step a, repeat above-mentioned calculating, arrive steady-state algorithm always and stop.
Compare with the heart beating separation method with the breathing of existing non-contact detecting, the present invention has the following advantages:
1, real-time is good: in the separation process of breathing and heartbeat signal, there is no the time delay of signal, the real-time of breathing and heart beating all is better than frequency domain filtering and wavelet decomposition reconstructing method.
2, the suitability is strong: no matter body moves whether the heartbeat signal frequency that comprises in signal is the stack of breath signal higher hamonic wave, all can effectively isolate heartbeat signal from the moving signal of body, the suitability all is better than time-domain filtering, frequency domain filtering and wavelet decomposition reconstructing method.
Description of drawings
The present invention is described in further detail below in conjunction with accompanying drawing and the specific embodiment.
Fig. 1 breathes during bioradar of the present invention detects and the functional block diagram of heart beating real-time separation method.In figure: input signal is breath signal and the moving signal of body that biological detections of radar arrives human body, and output signal is isolated heartbeat signal from the moving signal of body.
Fig. 2 is the functional structure chart of Fig. 1 self adaptation harmonic cancellation module.
Fig. 3 is the human body heartbeat signal that adopts the inventive method and extract and electrocardiosignal by comparison.Wherein (a) figure is breath signal, (b) figure is the moving signal of body, (c) figure is isolated heartbeat signal, and (d) figure is the electrocardiosignal of monitoring simultaneously, (e)-(h) is respectively breath signal, body moving signal, isolated heartbeat signal and the corresponding frequency spectrum of electrocardiosignal.
Fig. 4 is the heart rate comparison of computational results of 16 experimental subjecies.Wherein, dotted line represents from the heart rate of the human body heartbeat signal calculating of the inventive method extraction; Solid line represents from the heart rate of the electrocardiosignal calculating of monitoring simultaneously.
The specific embodiment
As shown in Figure 1, a kind of breathing of bioradar detection and real-time separation method of heart beating of utilizing comprises two-way pretreatment module (breath signal pretreatment, the moving Signal Pretreatment of body), a breathing harmonic wave detection module and a self adaptation harmonic cancellation module.Pretreatment module with the bioradar non-contact detecting to breathing and the moving signal of body carry out pretreatment; Pretreated breath signal therefrom extracts the above harmonic wave of secondary of breath signal through breathing the harmonic wave detection module, directly sends into self adaptation harmonic cancellation module and as its reference-input signal; The moving signal of pretreated body directly sends into self adaptation harmonic cancellation module and as its original input signal, its output signal is heartbeat signal, realizes isolating breath signal from the moving signal of body.
Wherein, pretreatment module comprises digital filtering and normalized, can will breathe and the frequency range of the moving signal of body is limited in 5Hz, the amplitude of signal is limited to-1V extremely+1V.Digital filtering adopts the Butterworth iir filter, and the design parameter of wave filter adopts Matlab software to calculate; Normalized will be breathed and the amplitude of the moving signal of body is scaled to [1,1] interval, normalized is carried out according to following formula: y=(x-Min)/(Max-Min), wherein x is the breath signal after digital filtering, y is signal after normalization, and Max and Min represent respectively to breathe after filtering and body moves maximum and the minima of signal.
Breathing harmonic wave detection module is synthesized by the estimation of breath signal first-harmonic and breath signal harmonic wave and forms, and the breath signal first-harmonic estimates to adopt autocorrelation technique, and the breath signal harmonic wave synthesizes the employing gauss-newton method.The process that the breath signal harmonic wave detects is as follows:
The first step: the fundamental frequency of finding the solution breath signal.
Choose suitable window function breath signal is carried out segment processing, to asking auto-correlation function in each data segment, then auto-correlation function is carried out power Spectral Estimation, the corresponding Frequency point of the maximum of power spectrum energy is the fundamental frequency f of breath signal
0Then from front to back, successively breath signal is carried out windowing, self correlation, asks the processing such as power spectrum, to f
0Constantly upgrade.
Second step: the mathematical model that builds the breath signal higher hamonic wave.
The model definition of the movement of thorax that respiratory movement is caused is:
In formula: a represents breath signal, and n represents sampling instant, ω
0For the first-harmonic angular frequency of breath signal (
f
0The fundamental frequency of expression breath signal, f
sThe expression sample frequency), l ω
0Be the frequency of l subharmonic, a
iBe [a
1, a
2... .a
2l+1]
TVector, a
2lRepresent the phase place of l subharmonic, a
2l-1Represent the amplitude of l subharmonic, a
2l+1Represent the DC component of l subharmonic.
The 3rd step: utilize gauss-newton method to synthesize the breath signal higher hamonic wave.
Adopt gauss-newton method to find the solution a in above-mentioned mathematical model
iValue, the f that constantly will upgrade
0Value is carried out interative computation, when satisfying the optimal solution of Gauss-Newton, and the higher hamonic wave function of the breath signal that obtains synthesizing.
With reference to figure 2, self adaptation harmonic cancellation module will be breathed the output of harmonic wave detection module as the reference input of sef-adapting filter (adaptive noise canceller), with the original input of the moving signal of pretreated body as sef-adapting filter, constantly adjust and upgrade the parameter of sef-adapting filter by adaptive algorithm, but the respiratory component in the moving signal of body and the difference of the harmonic components of the breath signal of reference input square hour, the output of sef-adapting filter is exactly heartbeat signal at this moment.Adaptive algorithm adopts simple LMS(least mean-square error) algorithm, step size mu=0.00002, it is as follows that the exponent number of wave filter is got 20. its calculation procedures: the filter factor vector of definition time n=0 is initial value W (0), then carries out iteration:
(1) by the estimated value of the filter filtering coefficient vector of current time n, input breath signal harmonic vector x (n) and body move signal d (n), error signal:
In formula:
The weight coefficient of expression wave filter, T represent the matrix that signal forms is carried out the transposition computing.
(2) pass through again the renewal valuation of recurrence method calculating filter coefficient vector:
In formula: μ represents the time step of adaptive updates wave filter weight coefficient.
(3) time index n is increased by 1, then begin step (1), repeat above-mentioned calculating, arrive steady-state algorithm always and stop.
As shown in Figure 3, can find out from frequency spectrum, crest frequency corresponding in the breath signal power spectrum is 0.2188Hz, and this moment, corresponding breathing rate was 13 beats/mins; Crest frequency corresponding in the heartbeat signal power spectrum is 1.0Hz, and this moment, corresponding heart rate was 60 beats/mins; Crest frequency corresponding in the electrocardiosignal power spectrum is 1.02Hz, and this moment, corresponding heart rate was 61 beats/mins.Can find out from result, it is very little with the heart rate error that obtains from electrocardiosignal that separation method provided by the invention obtains heart rate.
As shown in Figure 4, as can be seen from the figure the heart rate of 16 experimental subjecies is closely similar.Experimental result shows, breathing provided by the invention and heart beating real-time separation method are effective, feasible fully.
Claims (4)
1. breathing and a heart beating real-time separation method that detects based on bioradar, is characterized in that, comprises the steps:
(1) the moving signal of the breath signal that bioradar is detected and body carries out respectively pretreatment, breathes and the frequency range of the moving signal of body is limited in 5Hz, the amplitude of signal is limited to-1V extremely+1V, pretreatment comprises digital filtering and normalized;
(2) pretreated breath signal is through breathing the harmonic wave detection module, therefrom extract the above higher hamonic wave of secondary of breath signal, comprise that the breath signal first-harmonic is estimated and the breath signal harmonic wave is synthetic, wherein, the breath signal first-harmonic estimates to adopt auto-correlation algorithm to find the solution fundamental frequency; The synthetic Gauss-Newton algorithm that adopts of breath signal harmonic wave;
(3) output that will breathe the harmonic wave detection module is sent into self adaptation harmonic cancellation module and as its reference-input signal; The moving signal of pretreated body is sent into self adaptation harmonic cancellation module and as its original input signal, constantly adjust and upgrade the parameter of sef-adapting filter by adaptive algorithm, respiratory component in the moving signal of body and the difference of the harmonic components of the breath signal of reference input square hour, the output of sef-adapting filter is exactly heartbeat signal at this moment.
2. breathing and the heart beating real-time separation method that detects based on bioradar as claimed in claim 1, it is characterized in that, the described normalized of step (1) is carried out as follows: y=(x-Min)/(Max-Min), wherein x is the breath signal after digital filtering, y is signal after normalization, and Max and Min represent respectively to breathe after filtering and body moves maximum and the minima of signal.
3. breathing and the heart beating real-time separation method that detects based on bioradar as claimed in claim 1, is characterized in that, the idiographic flow that the described breathing harmonic wave of step (2) detects is as follows:
The first step: the fundamental frequency of finding the solution breath signal
Choose suitable window function breath signal is carried out segment processing, to asking auto-correlation function in each data segment, then auto-correlation function is carried out power Spectral Estimation, the corresponding Frequency point of the maximum of power spectrum energy is the fundamental frequency f of breath signal
0, then from front to back, successively breath signal is carried out windowing, self correlation, asks the processing such as power spectrum, to f
0Constantly upgrade;
Second step: build breath signal higher hamonic wave mathematical model
The model definition of the movement of thorax that respiratory movement is caused is:
In formula: a represents breath signal, and n represents sampling instant, ω
0Be the first-harmonic angular frequency of breath signal,
f
0The fundamental frequency of expression breath signal, f
sThe expression sample frequency, l ω
0Be the frequency of l subharmonic, a
iBe [a
1, a
2... .a
2l+1]
TVector, a
2lRepresent the phase place of l subharmonic, a
2l-1Represent the amplitude of l subharmonic, a
2l+1Represent the DC component of l subharmonic;
The 3rd step: utilize Gauss-Newton algorithm to find the solution a in the second step mathematical model
iValue, the f that constantly will upgrade
0Value is carried out interative computation, when satisfying the optimal solution of Gauss-Newton, and the higher hamonic wave function of the breath signal that obtains synthesizing.
4. breathing and the heart beating real-time separation method that detects based on bioradar as claimed in claim 1, is characterized in that, the described adaptive algorithm of step (3) adopts the LMS algorithm, step size mu=0.00002, and it is as follows that the exponent number of wave filter is got 20. concrete calculation procedures:
The filter factor vector of definition time n=0 is initial value W (0), then carries out iteration:
A, by the estimated value of the filter filtering coefficient vector of current time n, input breath signal harmonic vector x (n) and body move signal d (n), error signal:
B, the renewal valuation by recurrence method calculating filter coefficient vector again:
C, time index n is increased by 1, then begin step a, repeat above-mentioned calculating, arrive steady-state algorithm always and stop.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210552351.4A CN103110422B (en) | 2012-12-18 | 2012-12-18 | Breath and heartbeat real-time separating method based on biological radar detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210552351.4A CN103110422B (en) | 2012-12-18 | 2012-12-18 | Breath and heartbeat real-time separating method based on biological radar detection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103110422A true CN103110422A (en) | 2013-05-22 |
| CN103110422B CN103110422B (en) | 2014-10-15 |
Family
ID=48408822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210552351.4A Expired - Fee Related CN103110422B (en) | 2012-12-18 | 2012-12-18 | Breath and heartbeat real-time separating method based on biological radar detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103110422B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104644143A (en) * | 2015-03-09 | 2015-05-27 | 耿希华 | Non-contact life sign monitoring system |
| CN105852850A (en) * | 2016-04-28 | 2016-08-17 | 深圳竹信科技有限公司 | Method and related device for extracting respiratory signals from ECG (electrocardiogram) signals |
| CN105919568A (en) * | 2016-05-24 | 2016-09-07 | 北京千安哲信息技术有限公司 | Gabor transformation based method and device for extracting and analyzing breathing and heartbeat signals |
| CN105962914A (en) * | 2016-05-24 | 2016-09-28 | 北京千安哲信息技术有限公司 | Respiration and heartbeat signal separation method and device based on blind source separation |
| CN105997083A (en) * | 2016-04-27 | 2016-10-12 | 深圳市前海万象智慧科技有限公司 | Detection device for human body breathing and detection method for same |
| CN106175731A (en) * | 2016-08-10 | 2016-12-07 | 上海交通大学 | The signal processing system of non-contact vital sign monitoring |
| CN106821347A (en) * | 2016-12-20 | 2017-06-13 | 中国人民解放军第三军医大学 | A kind of life detection radar breathing of FMCW broadbands and heartbeat signal extraction algorithm |
| CN106999105A (en) * | 2014-11-28 | 2017-08-01 | 夏普株式会社 | High-frequency device |
| CN107167802A (en) * | 2017-05-24 | 2017-09-15 | 北京大学 | A kind of breath signal detection algorithm based on ULTRA-WIDEBAND RADAR |
| CN108697352A (en) * | 2017-06-29 | 2018-10-23 | 深圳和而泰智能控制股份有限公司 | Physiologic information measurement method and physiologic information monitoring device, equipment |
| CN108852327A (en) * | 2018-04-16 | 2018-11-23 | 浙江大学 | A method of the faint life signal of non-contact detecting from motion artifacts |
| CN109480787A (en) * | 2018-12-29 | 2019-03-19 | 中国科学院合肥物质科学研究院 | A kind of contactless sleep monitor equipment and sleep stage method based on ULTRA-WIDEBAND RADAR |
| CN109497968A (en) * | 2018-10-22 | 2019-03-22 | 中国人民解放军第四军医大学 | A kind of life signal synchronized measurement system and measurement method for bioradar detection |
| CN109875529A (en) * | 2019-01-23 | 2019-06-14 | 北京邮电大学 | A kind of vital sign detection method and system based on ULTRA-WIDEBAND RADAR |
| CN110327029A (en) * | 2019-07-03 | 2019-10-15 | 上海交通大学 | A kind of heart rate and heart rate variability monitoring method based on microwave perception |
| CN112914554A (en) * | 2021-03-24 | 2021-06-08 | 湖南万脉医疗科技有限公司 | Noninvasive breathing frequency monitoring method and system for breathing machine |
| CN112932457A (en) * | 2021-01-26 | 2021-06-11 | 四川大学 | Respiratory system health monitoring method and device |
| CN113729655A (en) * | 2021-09-26 | 2021-12-03 | 重庆邮电大学 | Method for separating received signals of UWB radar sensor |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2529267Y (en) * | 2002-03-15 | 2003-01-01 | 中国人民解放军第四军医大学 | Radar non-contact life parameter detecting device |
| US20100130873A1 (en) * | 2008-04-03 | 2010-05-27 | Kai Sensors, Inc. | Non-contact physiologic motion sensors and methods for use |
| CN102018503A (en) * | 2010-10-21 | 2011-04-20 | 中国科学院深圳先进技术研究院 | Extraction method and device of breath and heartbeating signals in life probe radar |
| CN102360073A (en) * | 2011-09-08 | 2012-02-22 | 中国人民解放军第四军医大学 | Circuit functional module of signal calibration instrument for determining life detection radar performance |
| CN102429661A (en) * | 2011-09-20 | 2012-05-02 | 中国人民解放军第四军医大学 | Ultrawide-spectrum radar type non-contact life parameter real-time monitoring method |
| CN102488520A (en) * | 2011-10-27 | 2012-06-13 | 中国人民解放军第四军医大学 | Radar life body information extracting and processing system for monitoring life information |
| CN102499653A (en) * | 2011-10-27 | 2012-06-20 | 中国人民解放军第四军医大学 | Signal processing system for small-scale radar life detecting instrument |
| US20120245479A1 (en) * | 2011-03-23 | 2012-09-27 | Meena Ganesh | Physiology Monitoring and Alerting System and Process |
-
2012
- 2012-12-18 CN CN201210552351.4A patent/CN103110422B/en not_active Expired - Fee Related
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2529267Y (en) * | 2002-03-15 | 2003-01-01 | 中国人民解放军第四军医大学 | Radar non-contact life parameter detecting device |
| US20100130873A1 (en) * | 2008-04-03 | 2010-05-27 | Kai Sensors, Inc. | Non-contact physiologic motion sensors and methods for use |
| CN102018503A (en) * | 2010-10-21 | 2011-04-20 | 中国科学院深圳先进技术研究院 | Extraction method and device of breath and heartbeating signals in life probe radar |
| US20120245479A1 (en) * | 2011-03-23 | 2012-09-27 | Meena Ganesh | Physiology Monitoring and Alerting System and Process |
| CN102360073A (en) * | 2011-09-08 | 2012-02-22 | 中国人民解放军第四军医大学 | Circuit functional module of signal calibration instrument for determining life detection radar performance |
| CN102429661A (en) * | 2011-09-20 | 2012-05-02 | 中国人民解放军第四军医大学 | Ultrawide-spectrum radar type non-contact life parameter real-time monitoring method |
| CN102488520A (en) * | 2011-10-27 | 2012-06-13 | 中国人民解放军第四军医大学 | Radar life body information extracting and processing system for monitoring life information |
| CN102499653A (en) * | 2011-10-27 | 2012-06-20 | 中国人民解放军第四军医大学 | Signal processing system for small-scale radar life detecting instrument |
Non-Patent Citations (7)
| Title |
|---|
| MASSAGRAM, W. ET AL.: "Assessment of Heart Rate Variability and Respiratory Sinus Arrhythmia via Doppler Radar", 《MICROWAVE THEORY AND TECHNIQUES》, vol. 57, no. 10, 15 September 2009 (2009-09-15), pages 2542 - 2549, XP011276523, DOI: doi:10.1109/TMTT.2009.2029716 * |
| YANMING XIAO，ET AL.: "A Portable Noncontact Heartbeat and Respiration Monitoring System Using 5-GHz Radar", 《SENSORS JOURNAL》, vol. 7, no. 7, 15 May 2007 (2007-05-15), pages 1042 - 1043, XP011181160, DOI: doi:10.1109/JSEN.2007.895979 * |
| 张华 等: "非接触生物雷达基于自适应滤波的心跳信号检测", 《医疗卫生装备》, vol. 33, no. 1, 15 January 2012 (2012-01-15) * |
| 王健琪 等: "非接触生命参数检测系统自抖动干扰的自适应对消研究", 《生物医学工程学杂志》, vol. 21, no. 5, 31 October 2004 (2004-10-31), pages 749 - 752 * |
| 王元东 等: "基于自适应RLS算法的非接触式生命参数检测中呼吸和心跳信号的分离", 《医疗卫生装备》, vol. 30, no. 3, 15 March 2009 (2009-03-15), pages 25 - 27 * |
| 王元东: "基于超宽谱生物雷达的非接触式生命特征信息提取技术研究", 《中国优秀硕士学位论文全文数据库》, no. 3, 15 December 2009 (2009-12-15), pages 080 - 23 * |
| 王海滨 等: "LMS算法在非接触生命参数信号检测中的消噪应用", 《中国医疗器械杂志》, vol. 27, no. 1, 30 January 2003 (2003-01-30), pages 21 - 24 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106999105A (en) * | 2014-11-28 | 2017-08-01 | 夏普株式会社 | High-frequency device |
| CN104644143A (en) * | 2015-03-09 | 2015-05-27 | 耿希华 | Non-contact life sign monitoring system |
| CN105997083A (en) * | 2016-04-27 | 2016-10-12 | 深圳市前海万象智慧科技有限公司 | Detection device for human body breathing and detection method for same |
| CN105852850A (en) * | 2016-04-28 | 2016-08-17 | 深圳竹信科技有限公司 | Method and related device for extracting respiratory signals from ECG (electrocardiogram) signals |
| CN105962914B (en) * | 2016-05-24 | 2019-08-27 | 北京千安哲信息技术有限公司 | The separation method and device of breathing and heartbeat signal based on blind source separating |
| CN105919568A (en) * | 2016-05-24 | 2016-09-07 | 北京千安哲信息技术有限公司 | Gabor transformation based method and device for extracting and analyzing breathing and heartbeat signals |
| CN105962914A (en) * | 2016-05-24 | 2016-09-28 | 北京千安哲信息技术有限公司 | Respiration and heartbeat signal separation method and device based on blind source separation |
| CN106175731B (en) * | 2016-08-10 | 2020-02-21 | 上海交通大学 | Non-contact vital sign monitoring signal processing system |
| CN106175731A (en) * | 2016-08-10 | 2016-12-07 | 上海交通大学 | The signal processing system of non-contact vital sign monitoring |
| CN106821347A (en) * | 2016-12-20 | 2017-06-13 | 中国人民解放军第三军医大学 | A kind of life detection radar breathing of FMCW broadbands and heartbeat signal extraction algorithm |
| CN106821347B (en) * | 2016-12-20 | 2020-05-05 | 中国人民解放军第三军医大学 | FMCW broadband life detection radar respiration and heartbeat signal extraction algorithm |
| CN107167802A (en) * | 2017-05-24 | 2017-09-15 | 北京大学 | A kind of breath signal detection algorithm based on ULTRA-WIDEBAND RADAR |
| CN108697352A (en) * | 2017-06-29 | 2018-10-23 | 深圳和而泰智能控制股份有限公司 | Physiologic information measurement method and physiologic information monitoring device, equipment |
| CN108697352B (en) * | 2017-06-29 | 2021-04-20 | 深圳和而泰智能控制股份有限公司 | Physiological information measuring method, physiological information monitoring device and equipment |
| CN108852327A (en) * | 2018-04-16 | 2018-11-23 | 浙江大学 | A method of the faint life signal of non-contact detecting from motion artifacts |
| CN108852327B (en) * | 2018-04-16 | 2020-06-19 | 浙江大学 | Method for non-contact detection of weak vital signals in motion interference |
| CN109497968A (en) * | 2018-10-22 | 2019-03-22 | 中国人民解放军第四军医大学 | A kind of life signal synchronized measurement system and measurement method for bioradar detection |
| CN109497968B (en) * | 2018-10-22 | 2021-11-16 | 中国人民解放军第四军医大学 | Life signal synchronous measurement system and measurement method for biological radar detection |
| CN109480787A (en) * | 2018-12-29 | 2019-03-19 | 中国科学院合肥物质科学研究院 | A kind of contactless sleep monitor equipment and sleep stage method based on ULTRA-WIDEBAND RADAR |
| CN109480787B (en) * | 2018-12-29 | 2021-06-25 | 中国科学院合肥物质科学研究院 | Non-contact sleep monitoring equipment based on ultra-wideband radar and sleep staging method |
| CN109875529A (en) * | 2019-01-23 | 2019-06-14 | 北京邮电大学 | A kind of vital sign detection method and system based on ULTRA-WIDEBAND RADAR |
| CN110327029A (en) * | 2019-07-03 | 2019-10-15 | 上海交通大学 | A kind of heart rate and heart rate variability monitoring method based on microwave perception |
| CN110327029B (en) * | 2019-07-03 | 2021-07-23 | 上海交通大学 | Heart rate monitoring method based on microwave sensing |
| CN112932457A (en) * | 2021-01-26 | 2021-06-11 | 四川大学 | Respiratory system health monitoring method and device |
| CN112932457B (en) * | 2021-01-26 | 2022-11-25 | 四川大学 | Respiratory system health monitoring device |
| CN112914554A (en) * | 2021-03-24 | 2021-06-08 | 湖南万脉医疗科技有限公司 | Noninvasive breathing frequency monitoring method and system for breathing machine |
| CN113729655A (en) * | 2021-09-26 | 2021-12-03 | 重庆邮电大学 | Method for separating received signals of UWB radar sensor |
| CN113729655B (en) * | 2021-09-26 | 2024-03-08 | 重庆邮电大学 | Method for separating UWB radar sensor receiving signals |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103110422B (en) | 2014-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103110422B (en) | Breath and heartbeat real-time separating method based on biological radar detection | |
| CN105796096B (en) | A kind of heart rate variance analyzing method, system and terminal | |
| CN105232026A (en) | Heartbeat frequency detection algorithm of non-contact vital sign detection system | |
| CN106491156A (en) | A kind of fatigue drive of car detection method based on Multi-source Information Fusion | |
| MX2019013798A (en) | Algorithmic approach for estimation of respiration and heart rates. | |
| CN107361770A (en) | Sleep apnea event discriminating gear | |
| CN105769151A (en) | Multipoint pulse wave detection method and device | |
| Rajkumar et al. | Spectral and SNR improvement analysis of normal and abnormal heart sound signals using different windows | |
| CN105310688B (en) | One kind is based on non-negative blind separation Fetal ECG characteristic signal extraction method | |
| Ma et al. | Classification of motor imagery EEG signals based on wavelet transform and sample entropy | |
| Liu et al. | Extraction of fetal electrocardiogram using recursive least squares and normalized least mean squares algorithms | |
| TWI410233B (en) | The Architecture and Method of Removing Shaking Noise Technology of Physiological Signal | |
| Chaitanya et al. | A simple and robust QRS detection algorithm for wireless medical body area network | |
| Pandey | Adaptive filtering for baseline wander removal in ECG | |
| Tan et al. | Study on wavelet transform in the processing for ECG signals | |
| CN105919568A (en) | Gabor transformation based method and device for extracting and analyzing breathing and heartbeat signals | |
| CN111248900A (en) | Single-channel-based cardio-cerebral information coupling analysis method and system | |
| Jayadevappa et al. | An estimation technique using FFT for heart rate derived from PPG signal | |
| Prasad et al. | ECG signal analysis: different approaches | |
| Liang | Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancement | |
| Wu et al. | Development of full-featured ECG system for visual stress induced heart rate variability (HRV) assessment | |
| CN114298111A (en) | Cough sound identification method and device and readable storage medium | |
| Anisha et al. | Survey on fetal ECG extraction | |
| Zamrath et al. | Robust and computationally efficient approach for Heart Rate monitoring using photoplethysmographic signals during intensive physical excercise | |
| Viswanadham et al. | Artefacts removal from ECG signal: Dragonfly optimization-based learning algorithm for neural network-enhanced adaptive filtering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20141015 Termination date: 20171218 |