CN108403094A - Method for identifying pulse wave crest - Google Patents
Method for identifying pulse wave crest Download PDFInfo
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- CN108403094A CN108403094A CN201810249214.0A CN201810249214A CN108403094A CN 108403094 A CN108403094 A CN 108403094A CN 201810249214 A CN201810249214 A CN 201810249214A CN 108403094 A CN108403094 A CN 108403094A
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000009466 transformation Effects 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 5
- 210000001367 artery Anatomy 0.000 claims description 4
- 210000003462 vein Anatomy 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 3
- 238000000205 computational method Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 3
- 238000007781 pre-processing Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 3
- 210000000707 wrist Anatomy 0.000 description 2
- 208000001953 Hypotension Diseases 0.000 description 1
- 230000009084 cardiovascular function Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000003205 diastolic effect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000002526 effect on cardiovascular system Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 208000021822 hypotensive Diseases 0.000 description 1
- 230000001077 hypotensive effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011430 maximum method Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
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- Pathology (AREA)
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- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Signal Processing (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Cardiology (AREA)
- Mathematical Physics (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
A method of identifying a peak of a pulse wave, comprising: (1) collecting waveform signals of pulse waves; (2) checking the main wave frequency distribution; (3) preprocessing an original pulse wave signal to remove noise; (4) selecting a filter based on the frequency range of the main wave, highlighting the main wave by using the selected filter, excluding other peaks, extracting shannon energy envelope from the processed pulse wave signal, and then extracting a local maximum value; (5) and positioning a real pulse wave peak value point on the original pulse wave signal by utilizing the local maximum value. The method disclosed by the invention is accurate in identification when processing signals with high and narrow peaks, has better stability for period division in non-stationary signals, and has strong anti-noise capability for pulse wave signals.
Description
Technical field
The present invention relates to pulse waveform recognition methods, and more particularly, to a kind of side of identification pulse wave wave crest
Method.
Background technology
Human pulse wave generally has 6 characteristic points, as shown in Figure 1, a points indicate that actively affectionately valve opening point, c points are to receive
Contracting phase pressure peak, d points are aortectasia hypotensive points, and e points are LV Diastolic starting points, and f points are again rich wave trough, g
Point is again rich wave wave crest, and b points are pulse wave end point.The characteristic point of arteries and veins figure reflects cardiovascular different conditions respectively, is tool
There is the characteristic point of physiological significance.Sphygmogram characteristic point both can be used for calculating cardiovascular function parameter, assistant analysis and judge painstaking effort
Tubulose condition can be used for the type and pulse condition that judge arteries and veins figure.To diagnosis by feeling the pulse automation, there are very important for the identification of characteristic point
Meaning is an essential ring.At present in the identification of pulse wave characteristic point, common method has slope-threshold arithmetic, small echo to become
It changes, the method for the Gauss model of ratio of peak and fitting wrist pulse signal, due to the defect of noise jamming and method itself, these
Method generally existing deviation, and robustness is poor.Main wave is most apparent most important feature in pulse wave signal, for identifying arteries and veins
Period of fighting is very crucial, determines that the position of main wave divides and identify that other characteristic points have important work to the accurate of period
With.Shannon energy envelope has extraordinary performance in the R blob detections of ECG signal, is equally applicable to pulse wave signal, how sharp
It is to improve the important breakthrough mouth of accuracy and robustness with the accurate identification of characteristic point is carried out the characteristics of main wave.
Invention content
In order to solve the above-mentioned problems in the prior art, the present invention provides a kind of method of identification pulse wave wave crest.
It is provided by the invention identification pulse wave wave crest method include：
(1) waveform signal of pulse wave is acquired；
(2) main wave frequency rate distribution is checked；
(3) original pulse wave signal is pre-processed, removes noise；
(4) frequency range based on main wave selects filter, highlights main wave using the filter of selection, excludes other peaks,
To treated, pulse wave signal extracts shannon energy envelope, then extracts local maximum；
(5) true pulse wave peak point is positioned on original pulse wave signal using the local maximum.
Preferably, check that the method that main wave frequency rate is distributed includes：Fast Fourier Transform (FFT) is carried out to original pulse wave signal,
Then the signal broken up checks main wave frequency spectrogram frequency distribution.
Preferably, described to pre-process the baseline drift that original pulse wave signal is removed including the use of high-pass filtering, utilization is low
Pass filter is used for handling due to the burr for shaking and breathing generation in waveform, and except carrying out normalizing to pulse wave signal after making an uproar
Change.
Preferably, the normalized method is：Wherein d [n] is indicated at n points
The amplitude of pulse wave, a [n] are the pulse wave after normalization.
Preferably, the computational methods of the shannon energy envelope are：
Se [n]=- f12[n]log(f12[n])
Wherein, F1 [n] is filtered signal.
Preferably, when extracting the local maximum, pulse wave signal is handled to subtract using low-pass filter
The complexity of small search local maximum.
Preferably, the method for local maximum is extracted in Hilbert transform, wavelet transformation and Fourier transformation
It is a kind of.
Preferably, the method for extracting local maximum is：Hilbert transform is carried out to pulse wave signal, then using cutting
Line approach method finds pulse wave peak point.
Preferably, the method for the Hilbert transform is：
Preferably, the side of true pulse wave peak point is positioned on original pulse wave signal using the local maximum
Method includes：The both sides that the local maximum of curve is identified in main wave, local minimum is found on original pulse waveform,
Position where the local minimum corresponds to the beginning and end of Pulse period；
Local maximum is found between beginning and end, the local maximum corresponds to main wave in pulse waveform signal
Wave crest.
Compared with prior art, the present invention has the following advantages：
(1) when processing has the signal at high and narrow peak, identification is accurate；
(2) in the signal of non-stationary, the present invention has better stability to the division in period；
(3) strong to the noise resisting ability of pulse wave signal.
Description of the drawings
Fig. 1 is the waveform signal of human body pulse wave；
Fig. 2 is flow chart of the method for the present invention；
Fig. 3 is collected original pulse wave signal in the embodiment of the present invention；
Fig. 4 is the pulse wave signal after bandpass filtering in the embodiment of the present invention；
Fig. 5 is the shannon energy envelope extracted in the embodiment of the present invention.
Specific implementation mode
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in further detail.
As shown in Fig. 2, the method for the identification pulse wave wave crest of the present invention includes：
1. acquiring pulse wave signal；
Patient's wrist C Pneumoniae is placed in by the sensor of pressure adjustable and obtains pulse voltage signal, segmentation is pressurizeed, via
Pulse-tracing collection circuit conversion is digital signal, is stored in computer, as shown in Figure 3.
2. checking main wave frequency rate distribution
Fast Fourier Transform (FFT) (FFT) is carried out to original pulse wave signal, the signal broken up checks main wave frequency spectrogram
Frequency distribution, the frequency range based on main wave design subsequent filter；
3. pretreatment
Corresponding noise is removed using high-pass filter and low-pass filter, is specifically included：It is removed using high-pass filtering former
The baseline drift of beginning pulse wave signal is used for handling the hair in waveform due to shaking and breathing generation using the low-pass filtering of 30Hz
Thorn.Except pulse wave signal being normalized after making an uproar：Wherein d [n] indicates the pulse at n points
The amplitude of wave.A [n] is the pulse wave after normalization.
4. finding peak point
According to the frequency range of main wave, suitable bandpass filter (such as 1-4Hz) is designed, for highlighting main wave, is excluded
Other peaks, as shown in Figure 4.To treated, pulse wave signal extracts shannon energy envelope, as shown in figure 5, then extracting
Local maximum, local maximum correspond to the apparent position of main wave.It can reduce search local maximum using low-pass filter
The complexity of value.
Shannon energy envelope Se [n] uses Se [n]=- f12[n]log(f12[n]) it calculates,
Wherein, F1 [n] is filtered signal.
The method for looking for envelope maximum point, which has, is much not limited to Hilbert transform, wavelet transformation and Fourier transformation
Deng.
Time-domain signal can be extracted in pulse wave, and frequency-region signal can be extracted by Fourier transformation, pass through wavelet transformation
Time frequency signal characteristic point can be extracted.
Important application is detection singular points to wavelet transformation in the signal processing.The rising edge of signal at singular point
Failing edge corresponds to a pair of of local extremum of wavelet transformation detail signal.
Hilbert transform determines that the step of local maximum is：
After low pass filtering using Hilbert transform, the method for Hilbert transform is：
Hilbert transform defines the convolution of 1/ π t and x (t).
The moving average filter signal H (n) after HT removes low frequency wonder, passes through positive axis to the zero cross point of negative axis
Position the wave crest R (k) of SEE signals (pulse wave signal after extraction shannon energy envelope).Accurate positioning for peak point
Moving average filter length is very important.The real peak point of main wave is pulse signal in 0.25s near R (k)
Maximum point.The maximum value of signal and main wave based on differentiation can find the starting point of each pulse period.
Pulse wave peak point is found using tangent line approach method, using between adjacent peak point on original pulse wave signal
Minimum value positioning pulse wave cycle starting point, finally will be between two neighboring cycle starting point on original pulse wave signal
Maximum value is determined as true pulse wave peak point.
Wherein, the partial detection packet on property field is carried out between adjacent cycle starting point on original pulse wave signal
It includes：
The both sides that the local maximum of curve is identified in main wave, find local minimum, institute on original pulse waveform
Position where stating local minimum corresponds to the starting point of Pulse period, terminal, i.e., this main wave on pulse wave signal curve
The trough v2 of trough v1 and next main wave；
Local maximum is found between beginning and end, the local maximum corresponds to main wave in pulse wave signal
Wave crest k.
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical solution and advantageous effect
Describe in detail bright, it should be understood that the above is only a specific embodiment of the present invention, is not intended to restrict the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the protection of the present invention
Within the scope of.
Claims (10)
1. a kind of method of identification pulse wave wave crest, including：
(1) waveform signal of pulse wave is acquired；
(2) main wave frequency rate distribution is checked；
(3) original pulse wave signal is pre-processed, removes noise；
(4) frequency range based on main wave selects filter, highlights main wave using the filter of selection, excludes other peaks, to place
Pulse wave signal after reason extracts shannon energy envelope, then extracts local maximum；
(5) true pulse wave peak point is positioned on original pulse wave signal using the local maximum.
2. the method for the method for claim 1, wherein checking main wave frequency rate distribution includes：To original pulse wave signal
Fast Fourier Transform (FFT) is carried out, then the signal broken up checks main wave frequency spectrogram frequency distribution.
3. the method for claim 1, wherein the pretreatment removes original pulse wave signal including the use of high-pass filtering
Baseline drift, be used for handling due to the burr for shaking and breathing generation in waveform using low-pass filtering, and except after making an uproar to arteries and veins
Wave signal of fighting is normalized.
4. the method for claim 1, wherein the normalized method is：Its
Middle d [n] indicates that the amplitude of the pulse wave at n points, a [n] are the pulse wave after normalization.
5. the method for claim 1, wherein the computational methods of the shannon energy envelope are：
Se [n]=- f12[n]log(f12[n])
Wherein, F1 [n] is filtered signal.
6. the method for claim 1, wherein when extracting the local maximum, using low-pass filter to pulse
Wave signal is handled to reduce the complexity of search local maximum.
7. the method for claim 1, wherein the method for extraction local maximum is selected from Hilbert transform, small echo becomes
It changes and one kind in Fourier transformation.
8. the method for claim 1, wherein the method for extraction local maximum is：Xi Er is carried out to pulse wave signal
Bert converts, and then finds pulse wave peak point using tangent line approach method.
9. method as claimed in claim 8, wherein the method for the Hilbert transform is：
10. the method for claim 1, wherein being positioned on original pulse wave signal very using the local maximum
The method of real pulse wave peak point includes：
The both sides that the local maximum of curve is identified in main wave, find local minimum, institute on original pulse waveform
Position where stating local minimum corresponds to the beginning and end of Pulse period；
Local maximum is found between beginning and end, the local maximum corresponds to the wave of main wave in pulse waveform signal
Peak.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110584624A (en) * | 2019-09-18 | 2019-12-20 | 中国科学院微电子研究所 | Pulse wave feature point identification method based on included angle value |
| CN111557650A (en) * | 2020-05-13 | 2020-08-21 | 南京邮电大学 | Adam-based fast batch gradient ascent method pulse wave feature extraction method |
| CN113080891A (en) * | 2021-03-17 | 2021-07-09 | 浙江大学 | Method for extracting respiration rate and heart rate based on human body micro-motion signal |
| CN113520356A (en) * | 2021-07-07 | 2021-10-22 | 浙江大学 | Heart disease early diagnosis system based on Korotkoff sounds |
| CN114711733A (en) * | 2022-06-07 | 2022-07-08 | 北京大学深圳研究生院 | Pulse signal extraction method and device, electronic equipment and storage medium |
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Application publication date: 20180817 |