CN108078554A - A kind of human pulse ripple signal noise suppressing method - Google Patents
A kind of human pulse ripple signal noise suppressing method Download PDFInfo
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- 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
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- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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Abstract
The invention discloses a kind of human pulse ripple signal noise suppressing method, the present invention is clear display waveform form, and first, the original pulse wave signal amplitude collected is normalized.Then, to reduce the influence of edge effect, periodic extension is carried out to the both ends of signal.Finally, the noise suppressed that a kind of dual median filter is used for human pulse ripple signal is designed：The high-frequency noise in pulse wave signal is eliminated using the first weight median filter, the low-frequency noise in the second weight median filter estimation signal is recycled, removes the low-frequency noise of estimation in the signal after high-frequency noise is inhibited to obtain the signal after de-noising afterwards.The present invention can inhibit high-frequency noise, baseline drift and componental movement noise in human pulse ripple signal, while can be a kind of simple real-time noise suppressing method that can be applied to microprocessor to avoid increasing adjunct circuit or using complicated denoising algorithm.
Description
Technical field
The present invention relates to a kind of real-time pulse wave signal noise suppressing method based on dual medium filtering, especially suitable for
Portable pulse BOLD contrast.
Background technology
Human pulse ripple signal detection has been widely used in commenting for cardiovascular system, respiratory system and blood circulation system
Estimate.Human pulse ripple weak output signal is inevitably disturbed be subject to human motion, big and heavy breathing, so as to influence signal
Obtain the accuracy that quality and later phase clinical physiological parameter calculate.
At present, it is more mainly to include EMD Denoising Methods, sef-adapting filter Denoising Method, small echo for noise suppression algorithm both domestic and external
Resolution analysis Denoising Method, SVD Denoising Methods, ICA Denoising Methods, higher order statistical Denoising Method and the analysis of Cycle by Cycle fourier series
Deng in addition to this it is possible to which wavelet Denoising Method is integrated in DSP for inhibiting Hz noise, being adopted using acceleration transducer
Collect signal as motion reference to eliminate motion artifacts, the real-time side based on edge analysis is realized in 32 ARM microcontrollers
Method with detect the segmentation of pulse wave and artifact detection etc..
However, the occupation rate of market of traditional pulse blood oxygen instrument based on common microprocessor is higher, it is contemplated that cost, body
The factors such as product, real-time and realization method, above method or need to increase additional hardware circuit to meet motion disturbance signals
Acquisition or need using high-end microcontroller implementation complexity denoising algorithm or need to realize pulse wave signal data transmission simultaneously
Using the algorithm that host operation is complicated, the use of Portable pulse BOLD contrast is not suitable for.Therefore, the present invention focuses on offer one
Kind is suitable for the pulse wave real-time noise suppressing method of common microprocessor.
The content of the invention
The noise that the purpose of the present invention is be directed in pulse wave signal provides a kind of disappearing in real time based on dual medium filtering
Method for de-noising, this method comprise the following steps：
(1) it is normalized to obtain the pulse wave signal PPG after normalized to original pulse wave signal PPG
1；
(2) periodic extension is carried out to the pulse wave signal PPG 1 after normalized and handles to obtain continuation treated arteries and veins
Fight ripple signal PPG 2；
(3) the first heavy medium filtering is carried out to continuation treated pulse wave signal PPG 2, sliding window size is W1,
High-frequency noise for inhibiting in pulse wave signal is eliminated the pulse wave signal PPG3 of high-frequency noise；
(4) the second heavy medium filtering is carried out to the pulse wave signal PPG 3 for eliminating high-frequency noise, sliding window size is
W2, for estimating the low-frequency noise signal PPG 4 in pulse wave signal；
(5) the low-frequency noise signal PPG 4 of estimation is removed from the pulse wave signal PPG 3 for eliminating high-frequency noise, is obtained
Pulse wave signal PPG 5 after noise suppressed.
The normalized formula of the step (1) is as follows：
Wherein, i=1,2,3 ..., L；L is the data length of PPG 1, and for max to be maximized function, min is to be minimized
Function；
In the step (2) periodic extension length Q values be the second sliding window size W2 half, continuation formula
It is as follows：
In formula, L be PPG 1 data length, n=1,2,3 ..., 2Q+L；
The sliding window of the step (3) is 78ms, and the value of PPG3 (n) is to cause in sequence PPG2 (i)Minimum value, wherein, Q+1≤n≤Q+L,
The sliding window of the step (4) is 8ms, and the value of PPG4 (n) is to cause in sequence PPG3 (i)Minimum value, wherein, Q+1≤n≤Q+L,
The step (5) is specially to obtain the signal PPG5 after noise suppressed using equation below：
PPG5 (n)=PPG3 (n)-PPG4 (n), Q+1≤n≤Q+L.
Beneficial effects of the present invention：
The present invention can inhibit high-frequency noise, baseline drift and componental movement noise in human pulse ripple signal, together
When can be to avoid increasing adjunct circuit or be a kind of simple real-time noise that can be applied to microprocessor using complicated denoising algorithm
Suppressing method.
Description of the drawings
Fig. 1 is the program chart of the present invention.
Fig. 2 is acquisition and the pulse wave signal after normalized under high frequency environment noise states of the present invention.
Fig. 3 is result of the pulse wave signal shown in Fig. 2 after dual medium filtering noise suppressed.
Fig. 4 is the frequency spectrum of pulse wave signal shown in Fig. 2.
Fig. 5 is the frequency spectrum of pulse wave signal shown in Fig. 3.
Fig. 6 is acquisition and the pulse wave signal after normalized under motion state of the present invention.
Fig. 7 is result of the pulse wave signal shown in Fig. 6 after dual medium filtering noise suppressed.
Specific embodiment
The present invention is clear display waveform form, and the original pulse wave signal amplitude collected is normalized.
Then, to reduce the influence of edge effect, periodic extension is carried out to the both ends of signal.Finally, a kind of dual medium filtering is designed
Device is used for the noise suppressed of human pulse ripple signal：The high frequency in pulse wave signal is eliminated using the first weight median filter to make an uproar
Sound recycles the low-frequency noise in the second weight median filter estimation signal, afterwards in the signal after high-frequency noise is inhibited
Except the low-frequency noise of estimation to obtain the signal after de-noising.Fig. 1 is the program flow diagram of noise suppressing method.
Original pulse wave signal used in the specific embodiment of the invention is obtained by testing, and specifically shies sail using Tianjin
The detecting sphygmus and blood oxygen saturation measuring instrument that Science and Technology Ltd. develops carries out pulse wave signal acquisition, the sampling frequency of the pulse blood oxygen instrument
Rate is 100Hz.Human body arteries and veins is gathered under conditions of " LongDate suction types electromagnetic vibration generator system " simulation high frequency environment noise is used
It fights ripple signal, arbitrarily intercepts signal data of the segment length for 2000 sampled points, dual medium filtering pulse signal is made an uproar
Sound suppressing method comprises the following steps：
(1) it is clear display human pulse ripple signal aspect, original pulse wave signal PPG is normalized to obtain
Pulse wave signal PPG 1 after normalized, as shown in Fig. 2, normalization formula is as follows：
Wherein, i=1,2,3 ..., 2000；For max to be maximized function, min is to be minimized function；
(2) to reduce the influence of edge effect, 1 both ends of the pulse wave signal PPG progress cycle after normalized is prolonged
It opens up, chooses the half that continuation length Q values are the second sliding window size W2, i.e. Q=W2/2=50, treated signal
It is continuation treated pulse wave signal PPG 2, then has：
(3) the selection window time is 78ms, and the first sliding window size W1=is calculated for 100Hz according to instrument sample rate
10, high-frequency noise is eliminated using the first heavy sliding window median filter, signal is the pulse wave for eliminating high-frequency noise after processing
The value of signal PPG3, PPG3 (n) are to cause in sequence PPG2 (i)Minimum value, wherein 51
≤ n≤2050, n-5≤i≤n+4；
(4) the selection window time is 8ms, calculates the second sliding window size W2=100, utilizes the second heavy sliding window
Median filter obtain the value of low-frequency noise signal PPG4, PPG4 (n) as in sequence PPG3 (i) estimating low-frequency noise so thatMinimum value, wherein 51≤n≤2050, n-50≤i≤n+49；
(5) the low-frequency noise signal PPG4 that estimation is removed from the pulse wave signal PPG3 for eliminating high-frequency noise obtains de-noising
Signal afterwards obtains the pulse wave signal PPG5 after noise suppressed, such as Fig. 3：
PPG5 (n)=PPG3 (n)-PPG4 (n), 51≤n≤2050
For further quantitatively evaluating algorithm, spectrum analysis is carried out to Fig. 2, pulse wave signal shown in Fig. 3 respectively and has been obtained
In order to facilitate observation of, reference axis where the frequency in spectrogram is intercepted to 0-60Hz by Fig. 4, Fig. 5.
Since the energy of pulse wave is generally focused on 0.5-10Hz, so cannot clearly be embodied from more than spectrogram pair
The inhibition of low-frequency noise.Therefore, human pulse ripple signal is gathered under the motion state of tester's standing walking, it is arbitrary to cut
Signal data of the segment length for 2000 sampled points is taken, the arteries and veins after obtaining normalization as shown in Figure 6 after step (1) processing
It fights ripple signal, wherein baseline drift low-frequency noise of the main interference caused by human motion, such as the intermediate curve mark in figure
Note.Pulse wave signal such as Fig. 7 institutes after step (2), step (3), step (4), the dual medium filtering noise suppressed of step (5)
Show.
Claims (6)
1. a kind of human pulse ripple signal noise suppressing method, it is characterised in that：This method comprises the following steps：
(1) it is normalized to obtain the pulse wave signal PPG1 after normalized to original pulse wave signal PPG；
(2) periodic extension is carried out to the pulse wave signal PPG1 after normalized and handles to obtain continuation treated that pulse wave is believed
Number PPG2；
(3) the first heavy medium filtering is carried out to continuation treated pulse wave signal PPG2, sliding window size is W1, for pressing down
High-frequency noise in pulse wave signal processed is eliminated the pulse wave signal PPG3 of high-frequency noise；
(4) the second heavy medium filtering is carried out to the pulse wave signal PPG3 for eliminating high-frequency noise, sliding window size is W2, is used for
Estimate the low-frequency noise signal PPG4 in pulse wave signal；
(5) the low-frequency noise signal PPG4 of estimation is removed from the pulse wave signal PPG3 for eliminating high-frequency noise, obtains noise suppression
Pulse wave signal PPG5 after system.
2. a kind of human pulse ripple signal noise suppressing method according to claim 1, it is characterised in that：The step
(1) normalized formula is as follows：
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Wherein, i=1,2,3 ..., L；L is the data length of PPG1, and for max to be maximized function, min is to be minimized function.
3. a kind of human pulse ripple signal noise suppressing method according to claim 1, it is characterised in that：The step
(2) periodic extension length Q values are the half of the second sliding window size W2 in, and continuation formula is as follows：
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In formula, L be PPG1 data length, n=1,2,3 ..., 2Q+L.
4. a kind of human pulse ripple signal noise suppressing method according to claim 1, it is characterised in that：The step
(3) sliding window is 78ms, and the value of PPG3 (n) is to cause in sequence PPG2 (i)Minimum value, wherein, Q+1≤n≤Q+L,
5. a kind of human pulse ripple signal noise suppressing method according to claim 1, it is characterised in that：
The sliding window of the step (4) is 8ms, and the value of PPG4 (n) is to cause in sequence PPG3 (i)Minimum value, wherein, Q+1≤n≤Q+L,
6. a kind of human pulse ripple signal noise suppressing method according to claim 1, it is characterised in that：The step
(5) it is specially that the signal PPG5 after noise suppressed is obtained using equation below：
PPG5 (n)=PPG3 (n)-PPG4 (n), Q+1≤n≤Q+L.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109344809A (en) * | 2018-11-21 | 2019-02-15 | 上海交通大学 | Domestic electric appliance intelligent management system based on magnetic strength induction signal |
| CN110192867A (en) * | 2019-06-25 | 2019-09-03 | 深圳市蓝瑞格生物医疗科技有限公司 | A kind of method and system promoting measuring system measurement accuracy |
| CN114052688A (en) * | 2021-12-07 | 2022-02-18 | 山东大学 | Blood pressure monitoring device based on one-way pulse wave, storage medium and electronic equipment |
| CN114912478A (en) * | 2022-04-02 | 2022-08-16 | 浙江好络维医疗技术有限公司 | Interference detection method for pulse wave signals |
| CN115192044A (en) * | 2022-07-28 | 2022-10-18 | 西安交通大学 | Single-target SSVEP (simple sequence order vector order) identification system and method based on data continuation |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6993377B2 (en) * | 2002-02-22 | 2006-01-31 | The Board Of Trustees Of The University Of Arkansas | Method for diagnosing heart disease, predicting sudden death, and analyzing treatment response using multifractal analysis |
| CN1985764A (en) * | 2005-12-23 | 2007-06-27 | 深圳迈瑞生物医疗电子股份有限公司 | Blood oxygen measuring method and device capable of eliminating moving inteference |
| CN101099677A (en) * | 2006-07-07 | 2008-01-09 | 深圳迈瑞生物医疗电子股份有限公司 | Alternative current component detecting method and detecting device |
| CN101156771A (en) * | 2007-09-28 | 2008-04-09 | 天津市先石光学技术有限公司 | Method and apparatus for improving vascellum hardness measurement precision base on pulse wave frequency spectrum analysis |
| CN103405227A (en) * | 2013-08-02 | 2013-11-27 | 重庆邮电大学 | Double-layer morphological filter based electrocardiosignal preprocessing method |
| US20140073946A1 (en) * | 2012-09-11 | 2014-03-13 | Nellcor Puritan Bennett Llc | Methods and systems for determining an algorithm setting based on a difference signal |
| CN104027095A (en) * | 2014-06-25 | 2014-09-10 | 哈尔滨工业大学 | Pulse data preprocessing method |
| CN104107038A (en) * | 2013-04-16 | 2014-10-22 | 达尔生技股份有限公司 | Pulse wave signal de-noising processing method and device and pulse oximeter |
| CN105740845A (en) * | 2016-03-02 | 2016-07-06 | 深圳竹信科技有限公司 | Method and system for filtering baseline drift based on single layer morphology |
| CN106473750A (en) * | 2016-10-08 | 2017-03-08 | 西安电子科技大学 | Personal identification method based on photoplethysmographic optimal period waveform |
-
2018
- 2018-01-05 CN CN201810009638.XA patent/CN108078554A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6993377B2 (en) * | 2002-02-22 | 2006-01-31 | The Board Of Trustees Of The University Of Arkansas | Method for diagnosing heart disease, predicting sudden death, and analyzing treatment response using multifractal analysis |
| CN1985764A (en) * | 2005-12-23 | 2007-06-27 | 深圳迈瑞生物医疗电子股份有限公司 | Blood oxygen measuring method and device capable of eliminating moving inteference |
| CN101099677A (en) * | 2006-07-07 | 2008-01-09 | 深圳迈瑞生物医疗电子股份有限公司 | Alternative current component detecting method and detecting device |
| CN101156771A (en) * | 2007-09-28 | 2008-04-09 | 天津市先石光学技术有限公司 | Method and apparatus for improving vascellum hardness measurement precision base on pulse wave frequency spectrum analysis |
| US20140073946A1 (en) * | 2012-09-11 | 2014-03-13 | Nellcor Puritan Bennett Llc | Methods and systems for determining an algorithm setting based on a difference signal |
| CN104107038A (en) * | 2013-04-16 | 2014-10-22 | 达尔生技股份有限公司 | Pulse wave signal de-noising processing method and device and pulse oximeter |
| CN103405227A (en) * | 2013-08-02 | 2013-11-27 | 重庆邮电大学 | Double-layer morphological filter based electrocardiosignal preprocessing method |
| CN104027095A (en) * | 2014-06-25 | 2014-09-10 | 哈尔滨工业大学 | Pulse data preprocessing method |
| CN105740845A (en) * | 2016-03-02 | 2016-07-06 | 深圳竹信科技有限公司 | Method and system for filtering baseline drift based on single layer morphology |
| CN106473750A (en) * | 2016-10-08 | 2017-03-08 | 西安电子科技大学 | Personal identification method based on photoplethysmographic optimal period waveform |
Non-Patent Citations (4)
| Title |
|---|
| SUYI LI ET AL.: "Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals", 《BIOMED RESEARCH INTERNATIONAL》 * |
| 张爱华等: "基于压缩感知的脉搏信号重构方法研究", 《中国医疗器械杂志》 * |
| 杨力铭: "《时空域脉搏信号检测方法研究》", 31 December 2017, 西南交通大学出版社 * |
| 高伟等: "基于脉搏波传导时间的血压监测方法", 《测控技术》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109344809A (en) * | 2018-11-21 | 2019-02-15 | 上海交通大学 | Domestic electric appliance intelligent management system based on magnetic strength induction signal |
| CN110192867A (en) * | 2019-06-25 | 2019-09-03 | 深圳市蓝瑞格生物医疗科技有限公司 | A kind of method and system promoting measuring system measurement accuracy |
| CN114052688A (en) * | 2021-12-07 | 2022-02-18 | 山东大学 | Blood pressure monitoring device based on one-way pulse wave, storage medium and electronic equipment |
| CN114912478A (en) * | 2022-04-02 | 2022-08-16 | 浙江好络维医疗技术有限公司 | Interference detection method for pulse wave signals |
| CN114912478B (en) * | 2022-04-02 | 2024-07-16 | 浙江好络维医疗技术有限公司 | Pulse wave signal interference detection method |
| CN115192044A (en) * | 2022-07-28 | 2022-10-18 | 西安交通大学 | Single-target SSVEP (simple sequence order vector order) identification system and method based on data continuation |
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