CN110584646A - Adaptive second-order filtering electrocardiosignal denoising preprocessing device and method - Google Patents
Adaptive second-order filtering electrocardiosignal denoising preprocessing device and method Download PDFInfo
- Publication number
- CN110584646A CN110584646A CN201910769222.2A CN201910769222A CN110584646A CN 110584646 A CN110584646 A CN 110584646A CN 201910769222 A CN201910769222 A CN 201910769222A CN 110584646 A CN110584646 A CN 110584646A
- Authority
- CN
- China
- Prior art keywords
- adaptive
- order
- filtering
- electrocardiosignals
- electrocardiosignal
- 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.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
-
- 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
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
-
- 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
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- 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
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Surgery (AREA)
- Medical Informatics (AREA)
- Veterinary Medicine (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Artificial Intelligence (AREA)
- Psychiatry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Power Engineering (AREA)
- Cardiology (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses a self-adaptive second-order filtering electrocardiosignal denoising preprocessing device and a self-adaptive second-order filtering electrocardiosignal denoising preprocessing method.
Description
Technology neighborhood
The invention relates to the technical field of medical equipment, in particular to a self-adaptive second-order filtering electrocardiosignal denoising preprocessing device and method.
Background
With the acceleration of urban life, heart diseases have become a major trouble in normal life. How to apply the correct detection and prevention of heart diseases is a hot spot of research in today's society. The electrocardiogram is a standard for diagnosing heart diseases, and plays a great role in diagnosing heart diseases such as myocardial infarction, ventricular hypertrophy and the like. Various characteristic waveforms in the electrocardiosignals have an inseparable corresponding relationship with mechanical motion, physiological functions and health conditions of the heart, so that doctors can find early cardiovascular diseases by observing the variation condition of the electrocardio-waveform characteristics for a long time. However, the electrocardiosignals are weak signals of human bodies, a large amount of noise exists in the acquisition process, and the effective parts of the electrocardiosignals are extracted to be the premise of electrocardio detection and identification. At present, in medical treatment or scientific research and analysis, electrocardiosignals are obtained by collecting potential differences between electrode plates attached to special positions of human skin, the electrocardiosignals obtained in the mode are electrocardiosignals containing noise, certain interference exists, the electrocardiosignals are taken as a diagnosis standard, diagnosis precision can be achieved, and even misjudgment can be brought.
Disclosure of Invention
The invention provides a self-adaptive second-order filtering electrocardiosignal denoising preprocessing device and method, aiming at solving the problem that the electrocardiosignal acquired by the existing method has noise and influences the subsequent diagnosis precision.
In order to achieve the above purpose, the technical means adopted is as follows:
a self-adaptive second-order filtering electrocardiosignal denoising preprocessing device comprises: the electrocardiosignal acquisition device, the self-adaptive controller and the second-order filter;
the electrocardiosignal collector is used for collecting original electrocardiosignals;
the adaptive controller is used for carrying out adaptive control processing on the electrocardiosignals acquired by the electrocardiosignal acquisition device and filtering low-frequency noise in the electrocardiosignals;
the second-order filter is used for carrying out second-order filtering on the electrocardiosignals processed by the self-adaptive controller, filtering high-frequency noise in the electrocardiosignals, and obtaining final de-noised electrocardiosignals.
According to the scheme, the electrocardiosignals obtained by collecting the potential difference between the electrode plates attached to the special positions of the skin of the human body are subjected to self-adaptive second-order filtering pretreatment, so that low-order and high-order noises in the electrocardiosignals are filtered, and the electrocardiosignals which can be used for medical treatment and effective scientific research and analysis are obtained.
Preferably, the preprocessing device further comprises an analog-to-digital converter, which is used for performing discretization processing on the original electrocardiosignals acquired by the electrocardiosignal acquisition device. In the preferred embodiment, since the computer can only recognize discrete binary numbers or hexadecimal numbers similar to 0/1, the discretization process is performed on the acquired original electrocardiosignals.
Preferably, the second-order filter is configured to perform second-order filtering processing on a characteristic waveform of the electrocardiographic signal, which includes a P wave, a Q wave, an R wave, an S wave, a T wave, and a U wave.
Preferably, the second order filter includes a first filter and a second filter connected in a sequential structure. In the preferred scheme, the first filter and the second filter are connected in a sequential structure, so that the electrocardiosignals are in smooth transition in the filter processing process, and unnecessary jitter of the electrocardiosignals is avoided.
Preferably, the input signal of the adaptive controller includes a target signal and a reference signal, the target signal is an acquired electrocardiographic signal, the reference signal is a signal of the target signal that is irrelevant to a noise signal, and the adaptive control process cancels the noise signal of the target signal and retains a relevant signal corresponding to the reference signal, so as to obtain an electrocardiographic signal without low-frequency noise;
the model of the adaptive controller is as follows:
wherein S (n) is a reference signal at the nth time, the reference signal is a sample signal under a high-precision medical measuring instrument and a signal processing technical means, and data stored in a PC (personal computer) can be converted by AD, V (n) is a target signal acquired at the nth time, and X (n)/X (k) is an adaptive controller input signal obtained by overlapping S (n)/S (k) and V (n)/V (k); x (n) is a signal obtained by averaging the sampling values of the target signal at the previous d moments; y (n) represents the output signal of the adaptive controller at time n; e (n) is an error value obtained by subtracting the adaptive controller input signal X (n) and the adaptive controller output value y (n) at the nth moment; m (n) is the tap coefficient of the adaptive controller, and τ is the step factor, where m (n) is ∈ [1,100], τ ∈ (0.001, 0.1).
In the preferred embodiment, to achieve the adaptive control effect, it is only necessary to increase the tap coefficient of the adaptive controller and decrease the step factor thereof appropriately, so as to cancel the noise signal in the target signal and keep the correlation signal corresponding to the reference signal. The acquired electrocardiosignals are subjected to simple filtering processing by an adaptive controller, and low-frequency component noise lower than 50HZ in myoelectric interference can be filtered, so that the finally obtained electrocardiosignals are electrocardiosignals without low-frequency noise.
Preferably, the model of the second order filter is:
wherein k is1 i、k2 iIs the coefficient of a second order filter, v1i、v2iRepresenting the state of a second order filter, v2iIs the output of a second order filter, viAnd v2iInput and output, respectively, of a second order filteriAnd (t) is a noise coefficient caused by the second-order filtering communication network. In the preferred scheme, because the acquisition of the electrocardiosignals comes from the body surface of a human body, myoelectric interference is inevitably introduced. The myoelectric baseline generated by myoelectric interference is usually in a very small voltage range and is therefore generally not obvious. The main energy of the electromyographic interference is concentrated in the frequency range of 30Hz-300Hz, and high-frequency components including baseline drift, electromyographic interference, electrode contact noise and the like in electrocardiosignal components can be filtered by adopting a second-order filter.
Preferably, due to the filtering error, the actual coefficients of the second order filter are:
kεi(t)＝k1 i+εi(t)
wherein k is1 iIs the coefficient of a second order filter, εiAnd (t) is a noise coefficient caused by the second-order filtering communication network.
Preferably, the coefficient k of the second order filter1 iAnd k2 iThe filtering process is a negative feedback regulation mode, namely when the filtering signal is enhancedReducing the effect of the input electrocardiosignals; when the filtered signal is attenuated, the effect of the electrocardiosignal is enhanced.
The invention also provides a self-adaptive second-order filtering electrocardiosignal denoising preprocessing method based on the self-adaptive second-order filtering electrocardiosignal denoising preprocessing device, which comprises the following steps:
s1, acquiring original electrocardiosignals through an electrocardiosignal collector;
s2, the self-adaptive controller performs self-adaptive control processing on the acquired electrocardiosignals, and low-frequency noise in the electrocardiosignals is filtered;
and S3, carrying out second-order filtering processing on the electrocardiosignals subjected to the self-adaptive control processing by a second-order filter, and filtering high-frequency noise in the electrocardiosignals to obtain the final de-noised electrocardiosignals.
Preferably, the step S3 further includes adjusting coefficients of the adaptive controller and the second-order filter, so that the second-order filter satisfies a preset convergence rate and overshoot when converging, thereby completing filtering of high-frequency noise in the adaptively controlled electrocardiographic signal.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the invention, the electrocardiosignals acquired by collecting the potential difference between the electrode plates attached to the special positions of the skin of the human body are subjected to self-adaptive control processing and second-order filtering processing, so that low-order and high-order noises in the electrocardiosignals are filtered, the electrocardiosignals which can be effectively used for medical treatment and scientific research analysis are obtained, and the problem that the electrocardiosignals acquired by the existing method have noises and influence the subsequent diagnosis precision is solved.
Drawings
FIG. 1 is a schematic diagram of the apparatus of example 1.
Fig. 2 is a flowchart of a denoising preprocessing method in embodiment 2.
Fig. 3 is a schematic diagram of the internal structure of the second-order filter in embodiment 2.
Fig. 4 is an electrocardiographic signal diagram of step S2 in embodiment 2.
Fig. 5 is a denoised electrocardiographic signal obtained in step S3 in embodiment 2.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and their descriptions may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
An adaptive second-order filtering electrocardiosignal denoising preprocessing device, as shown in fig. 1, includes: the electrocardiosignal acquisition device 1, the analog-to-digital converter 2, the self-adaptive controller 3 and the second-order filter 4;
the electrocardiosignal collector 1 is used for collecting original electrocardiosignals;
the preprocessing device also comprises an analog-to-digital converter 2, which is used for carrying out discretization processing on the original electrocardiosignals acquired by the electrocardiosignal acquisition device 1;
the adaptive controller 3 is used for performing adaptive control processing on the discretized electrocardiosignals and filtering low-frequency noise in the discretized electrocardiosignals; the input signals of the adaptive controller 3 comprise target signals and reference signals, the target signals are acquired electrocardiosignals, the reference signals are signals irrelevant to noise signals in the target signals, and the adaptive control process is to counteract the noise signals in the target signals and reserve relevant signals corresponding to the reference signals so as to obtain electrocardiosignals without low-frequency noise;
the model of the adaptive controller 3 is:
wherein, s (n) is a reference signal at the nth time, v (n) is a target signal acquired at the nth time, and x (n)/x (k) is an input signal of the adaptive controller 3 after s (n)/s (k) and v (n)/v (k) are superposed; x (n) is a signal obtained by averaging the sampling values of the target signal at the previous d moments; y (n) represents the output signal of adaptive controller 3 at time n; e (n) is an error value obtained by subtracting the input signal X (n) of the adaptive controller 3 at the nth time from the output value y (n) of the adaptive controller 3; m (n) is the tap coefficient of the adaptive controller 3, τ is the step factor, where m (n) e [1,100], τ e (0.001, 0.1);
the second-order filter 4 comprises a first filter and a second filter which are connected in a sequential structure and is used for carrying out second-order filtering on the electrocardiosignals processed by the self-adaptive controller 3, and filtering high-frequency noise of characteristic waveforms including P waves, Q waves, R waves, S waves, T waves and U waves to obtain final de-noised electrocardiosignals;
the model of the second order filter 4 is:
wherein k is1 i、k2 iThe coefficient of the second-order filter 4, the filtering process of which is a negative feedback regulation mode, namely, when the filtering signal is enhanced, the function of the input electrocardiosignal is reduced; when the filtered signal is weakened, the action v of said electrocardiosignal is strengthened1i、v2iRepresenting the state of the second order filter 4, v2iIs the output of a second order filter 4, viAnd v2iInput and output, respectively, of a second order filteriAnd (t) is a noise coefficient caused by the second-order filtering communication network.
Due to the filtering error, the actual coefficients of the second order filter 4 are:
kεi(t)＝k1 i+εi(t)
wherein k is1 iIs the coefficient of the second order filter 4, εiAnd (t) is a noise coefficient caused by the second-order filtering communication network.
Example 2
This embodiment 2 is a denoising preprocessing method based on the adaptive second-order filtering electrocardiographic signal denoising preprocessing device provided in the above embodiment 1, and performs an experiment, and as shown in fig. 2, includes the following steps:
s1, acquiring original electrocardiosignals through an electrocardiosignal acquisition device 1;
s2, the self-adaptive controller 3 performs self-adaptive control processing on the acquired electrocardiosignals and filters low-frequency noise in the electrocardiosignals; in the present embodiment 2, the electrocardiographic signal shown in fig. 4 is obtained after step S2;
s3, the second-order filter 4 carries out second-order filtering processing on the electrocardiosignals after the self-adaptive control processing, and as shown in figure 3, the electrocardiosignals after the self-adaptive control processing are firstly amplified to k1 iMultiplying the coefficient, and then performing an integral operation on the coefficient, which also amplifies k2 iAnd multiplying the coefficient, and performing integral processing on the coefficient to filter high-frequency noise in the signal so as to obtain the final de-noised electrocardiosignal.
For this step, that is, when the number of times n of adjustment of the second-order filter 4 tends to infinity, the tap coefficient m (n) needs to be increased to achieve the condition that the convergence of the second-order filter 4 needs to meet, so that the whole filter control system is stabilized; and dynamically adjusting the step factor tau according to the convergence rate and the overshoot to be achieved: the step factor tau is increased when the convergence rate is fast, and the step factor tau is decreased when the overshoot is small; by increasing k1 iAnd decrease k2 iThe coefficients of the two second order filters 4 control the threshold of the high frequency noise of the second order filters, thereby achieving the purpose of filtering the high frequency noise. Dynamically adjusting the adaptive controller 3 and the second-order filter 4 according to the parameter adjustment rule to obtain the final denoised electrocardiosignal, in this embodiment 2, the coefficients of the second-order filter 4 are set to k respectively1 i1.8 and k2 iIn embodiment 2, as shown in fig. 5, the final denoised ecg signal graph is obtained after step S3.
The terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. The utility model provides a preprocessing device that removes noise of self-adaptation second order filtering electrocardiosignal which characterized in that includes: the electrocardiosignal acquisition device, the self-adaptive controller and the second-order filter;
the electrocardiosignal collector is used for collecting original electrocardiosignals;
the adaptive controller is used for carrying out adaptive control processing on the electrocardiosignals acquired by the electrocardiosignal acquisition device and filtering low-frequency noise in the electrocardiosignals;
the second-order filter is used for carrying out second-order filtering on the electrocardiosignals processed by the self-adaptive controller, filtering high-frequency noise in the electrocardiosignals, and obtaining final de-noised electrocardiosignals.
2. The adaptive second-order filtering denoising preprocessing device for electrocardiosignal according to claim 1, further comprising an analog-to-digital converter for discretizing the original electrocardiosignal acquired by the electrocardiosignal acquisition device.
3. The adaptive second-order filtering denoising pre-processing device for the electrocardiographic signal according to claim 1, wherein the second-order filter is specifically configured to perform second-order filtering processing on a characteristic waveform of the electrocardiographic signal including a P wave, a Q wave, an R wave, an S wave, a T wave, and a U wave.
4. The adaptive second-order filtering denoising pre-processing device for electrocardiosignal denoising according to claim 1, wherein the second-order filter comprises a first filter and a second filter which are connected in a sequential structure.
5. The adaptive second-order filtering denoising pre-processing device according to claim 1,
the input signals of the self-adaptive controller comprise target signals and reference signals, the target signals are acquired electrocardiosignals, the reference signals are signals irrelevant to noise signals in the target signals, and the self-adaptive control process is to counteract the noise signals in the target signals and reserve relevant signals corresponding to the reference signals so as to obtain the electrocardiosignals without low-frequency noise;
the model of the adaptive controller is as follows:
wherein, S (n) is a reference signal at the nth time, V (n) is a target signal acquired at the nth time, and X (n)/X (k) is an adaptive controller input signal obtained by superposing S (n)/S (k) and V (n)/V (k); x (n) is a signal obtained by averaging the sampling values of the target signal at the previous d moments; y (n) represents the output signal of the adaptive controller at time n; e (n) is an error value obtained by subtracting the adaptive controller input signal X (n) and the adaptive controller output value y (n) at the nth moment; m (n) is the tap coefficient of the adaptive controller, and τ is the step factor, where m (n) is ∈ [1,100], τ ∈ (0.001, 0.1).
6. The adaptive second-order filtering electrocardiosignal denoising pre-processing device according to claim 1, wherein the model of the second-order filter is:
wherein k is1 i、k2 iIs the coefficient of a second order filter, v1i、v2iRepresenting the state of a second order filter, v2iIs the output of a second order filter, viAnd v2iInput and output, respectively, of a second order filteriAnd (t) is a noise coefficient caused by the second-order filtering communication network.
7. The adaptive second-order filtering electrocardiosignal denoising pre-processing device according to claim 6, wherein due to the existence of filtering errors, the actual coefficients of the second-order filter are:
kεi(t)＝k1 i+εi(t)
wherein k is1 iIs the coefficient of a second order filter, εiAnd (t) is a noise coefficient caused by the second-order filtering communication network.
8. The adaptive second-order filtering denoising pre-processing device for electrocardiosignal according to claim 7, wherein the coefficient k of the second-order filter is1 iAnd k2 iThe filtering process of (2) is a negative feedback regulation mode, namely when the filtering signal is enhanced, the function of the input electrocardiosignal is reduced; when the filtered signal is attenuated, the effect of the electrocardiosignal is enhanced.
9. The adaptive second-order filtering electrocardiosignal denoising preprocessing method of the adaptive second-order filtering electrocardiosignal denoising preprocessing device according to any one of claims 1 to 8 is characterized by comprising the following steps:
s1, acquiring original electrocardiosignals through an electrocardiosignal collector;
s2, the self-adaptive controller performs self-adaptive control processing on the acquired electrocardiosignals, and low-frequency noise in the electrocardiosignals is filtered;
and S3, carrying out second-order filtering processing on the electrocardiosignals subjected to the self-adaptive control processing by a second-order filter, and filtering high-frequency noise in the electrocardiosignals to obtain the final de-noised electrocardiosignals.
10. The adaptive second-order filtering denoising preprocessing method according to claim 9, wherein the step S3 further comprises adjusting coefficients of the adaptive controller and the second-order filter, so that the second-order filter satisfies a predetermined convergence rate and overshoot when converging, thereby completing filtering of high-frequency noise in the adaptively controlled processed electrocardiographic signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910769222.2A CN110584646A (en) | 2019-08-20 | 2019-08-20 | Adaptive second-order filtering electrocardiosignal denoising preprocessing device and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910769222.2A CN110584646A (en) | 2019-08-20 | 2019-08-20 | Adaptive second-order filtering electrocardiosignal denoising preprocessing device and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110584646A true CN110584646A (en) | 2019-12-20 |
Family
ID=68854868
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910769222.2A Pending CN110584646A (en) | 2019-08-20 | 2019-08-20 | Adaptive second-order filtering electrocardiosignal denoising preprocessing device and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110584646A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022166181A1 (en) * | 2021-02-02 | 2022-08-11 | 武汉联影智融医疗科技有限公司 | Human body signal collection apparatus |
| CN117939384A (en) * | 2024-03-22 | 2024-04-26 | 深圳市东微智能科技股份有限公司 | Equipment detection method, device, terminal equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0580139A (en) * | 1991-09-20 | 1993-04-02 | Mitsubishi Heavy Ind Ltd | Noise removing filter device |
| CN1856974A (en) * | 2003-09-23 | 2006-11-01 | 皇家飞利浦电子股份有限公司 | Adaptive filter |
| CN104605841A (en) * | 2014-12-09 | 2015-05-13 | 电子科技大学 | Wearable electrocardiosignal monitoring device and method |
| CN106125604A (en) * | 2016-06-28 | 2016-11-16 | 东华理工大学 | A kind of ECG signal processing system |
| CN106691376A (en) * | 2016-11-30 | 2017-05-24 | 深圳市科曼医疗设备有限公司 | Method and device for adaptively filtering electrocardiograph signals |
| CN107280659A (en) * | 2016-04-12 | 2017-10-24 | 中国科学院微电子研究所 | Electrocardiosignal processing method and system |
| CN109247922A (en) * | 2018-10-29 | 2019-01-22 | 赵永刚 | A kind of detection method and device of electrocardiogram acquisition equipment pacemaker impulse |
-
2019
- 2019-08-20 CN CN201910769222.2A patent/CN110584646A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0580139A (en) * | 1991-09-20 | 1993-04-02 | Mitsubishi Heavy Ind Ltd | Noise removing filter device |
| CN1856974A (en) * | 2003-09-23 | 2006-11-01 | 皇家飞利浦电子股份有限公司 | Adaptive filter |
| CN104605841A (en) * | 2014-12-09 | 2015-05-13 | 电子科技大学 | Wearable electrocardiosignal monitoring device and method |
| CN107280659A (en) * | 2016-04-12 | 2017-10-24 | 中国科学院微电子研究所 | Electrocardiosignal processing method and system |
| CN106125604A (en) * | 2016-06-28 | 2016-11-16 | 东华理工大学 | A kind of ECG signal processing system |
| CN106691376A (en) * | 2016-11-30 | 2017-05-24 | 深圳市科曼医疗设备有限公司 | Method and device for adaptively filtering electrocardiograph signals |
| CN109247922A (en) * | 2018-10-29 | 2019-01-22 | 赵永刚 | A kind of detection method and device of electrocardiogram acquisition equipment pacemaker impulse |
Non-Patent Citations (1)
| Title |
|---|
| 维纳•K•英格尔等: "《数字信号处理MATLAB版》", 31 July 2013, 西安交通大学出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022166181A1 (en) * | 2021-02-02 | 2022-08-11 | 武汉联影智融医疗科技有限公司 | Human body signal collection apparatus |
| CN117939384A (en) * | 2024-03-22 | 2024-04-26 | 深圳市东微智能科技股份有限公司 | Equipment detection method, device, terminal equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rahman et al. | Efficient and simplified adaptive noise cancelers for ECG sensor based remote health monitoring | |
| Thakor et al. | Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection | |
| US5521851A (en) | Noise reduction method and apparatus | |
| CN103405227B (en) | Double-layer morphological filter based electrocardiosignal preprocessing method | |
| Kærgaard et al. | A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising | |
| Ebrahimzadeh et al. | ECG signals noise removal: Selection and optimization of the best adaptive filtering algorithm based on various algorithms comparison | |
| US9477640B2 (en) | Neural signal processing and/or interface methods, architectures, apparatuses, and devices | |
| CN107361764B (en) | Method for rapidly extracting electrocardiosignal characteristic waveform R wave | |
| CN110584646A (en) | Adaptive second-order filtering electrocardiosignal denoising preprocessing device and method | |
| Bortolan et al. | Dynamic filtration of high-frequency noise in ECG signal | |
| Chaitanya et al. | Electrocardiogram signal filtering using circulant singular spectrum analysis and cascaded Savitzky-Golay filter | |
| Almalchy et al. | Noise removal from ECG signal based on filtering techniques | |
| CN107280659A (en) | Electrocardiosignal processing method and system | |
| Elouaham et al. | Combination time-frequency and empirical wavelet transform methods for removal of composite noise in EMG signals | |
| Dai et al. | Removal of baseline wander from dynamic electrocardiogram signals | |
| Zhidong et al. | A novel cancellation method of powerline interference in ECG signal based on EMD and adaptive filter | |
| Nishida et al. | Application of Savitzky-Golay digital differentiator for QRS complex detection in an electrocardiographic monitoring system | |
| Al-Safi | ECG signal denoising using a novel approach of adaptive filters for real-time processing | |
| Liu et al. | Highly efficient low noise solutions in ECG signals | |
| Dembrani et al. | Accurate detection of ECG signals in ECG monitoring systems by eliminating the motion artifacts and improving the signal quality using SSG filter with DBE | |
| Patel et al. | Digital multiband filter design with power spectrum analysis for Electrocardiogram signals | |
| Moiz et al. | FPGA based ECG Denoising: Current Status and Future Technologies | |
| Shama et al. | Hamming filter design for ECG signal detection and processing using co-simulation | |
| Abd Rahman et al. | Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram | |
| Dakua et al. | Towards design and implementation of a low-cost EMG signal recorder for application in prosthetic arm control for developing countries like Bangladesh |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191220 |
|
| RJ01 | Rejection of invention patent application after publication |