CN107231142A - A kind of thrombelastogram instrument Adaptive Signal Processing Algorithm - Google Patents
A kind of thrombelastogram instrument Adaptive Signal Processing Algorithm Download PDFInfo
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- CN107231142A CN107231142A CN201710472660.3A CN201710472660A CN107231142A CN 107231142 A CN107231142 A CN 107231142A CN 201710472660 A CN201710472660 A CN 201710472660A CN 107231142 A CN107231142 A CN 107231142A
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H21/00—Adaptive networks
- H03H21/0012—Digital adaptive filters
- H03H21/0016—Non linear filters
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- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
Present invention comprises a kind of thrombelastogram instrument Adaptive Signal Processing Algorithm and its realization.The adaptive algorithm that the present invention is provided includes the adaptive filter algorithm of coagulation signal and the adaptive smooth algorithm of coagulation curve signal.Coagulation signal sequencing is by above two algorithm process, and the coagulation curve after processing ensure that the accuracy that multiple coagulation parameters results are obtained according to set formula.Inventive algorithm advantage being to be not required to the interference of human intervention automatic identification, being only filtered to there is the coagulation signal disturbed, only to there is the coagulation curve of interference smoothly reduce the utilization rate of processing unit, so as to improve the efficiency of overall instrument.In addition, inventive algorithm many places carry out using space compression, memory consumption is reduced.
Description
Technical field
The present invention relates to a kind of realization of thrombelastogram instrument Adaptive Signal Processing Algorithm.
Background technology
For the processing of blood coagulation periodic signal, conventional method has glide filter to filter out the noise jamming in signal.Due to
Blood coagulation periodic signal be in bandwidth range in a certain frequency (can be selected wherein when different detection types or different testing sample matter
A certain fixed frequency), for above-mentioned characteristics of signals, although glide filter will not be influenceed by different frequency, but it can only pin
To less mutation noise effects substantially, it is helpless for prolonged noise, when noise interference is larger, signal can be caused
Distortion, so as to influence the coagulation curve of next step to extract, there is provided the diagnosis of mistake letter for the serious coagulation parameters calculating mistake that causes
Breath.The present invention proposes adaptivity wave filter for the blood coagulation periodic signal that above-mentioned frequency of use is a scope, and principle is adaptive
The frequency that active user uses should be recognized, the wave filter of corresponding fixed coefficient is carried out.For the signal of frequency-adjustable, then fold
Plus the noise interferences of the different characteristic of the various noise sources introducing of different use environments, Real time identification frequency values, then judge
Noise jamming whether there is, quantizing noise level of interference, and related prompting is carried out if necessary.
Coagulation signal after above-mentioned processing, it is therefore an objective to which the coagulation curve for carrying out next step is extracted.
For the processing of coagulation curve, conventional method has glide filter, curve-fitting method, but both of which Shortcomings.It is sliding
Dynamic filtering can not be filtered out to larger interference, if curve distortion and increase computing overhead may be caused by carrying out multiple glide filter.
And curve-fitting method has certain improvement compared to glide filter, but obtained coagulation curve can be according to the difference of blood in itself
Or detection type is different and change shape, it is impossible to preferably determine polynomial fitting.So, the coagulation curve that the present invention is designed
Smoothing algorithm can carry out smooth according to coagulation curve self character.Specially according to the slope of curve and the difference of absolute time,
The points of regression analysis are selected to carry out smoothly.
The content of the invention
(1) adaptivity coagulation signal filtering algorithm
1. algorithm flow chart is as shown in Fig. 1 in Figure of description.
2. the execution of program is identified the frequency of signal, then passed through first after the coagulation signal of AD collections is obtained
Interference identification method 1 judges whether that interference carries out relevant treatment again.
The step of adaptivity of coagulation signal is filtered is as follows：
1) signal for sliding acquisition certain length is cached；
2) self-adapting estimation signal frequency；
3) signal interference method 1 is recognized；
4) corresponding fixed coefficient filter is selected.
The step of coagulation signal interference identification method 1, is as follows：
1) it regard the signal of above-mentioned slip buffer as reference signal；
2) energy of signal in caching is calculated；
If 3) energy is grown steadily or smooth decreasing, it is determined as non-noise interference presence；If energy is uprushed or bust,
It is determined as that noise jamming is present；
4) uprushed according to ability or the degree of bust is quantified, obtain degree of noise interference D1, (D1 is more than pre- if necessary
If interference threshold TH) corresponding prompting is provided.
(2) adaptive coagulation curve filtering algorithm
1. algorithm flow chart is as shown in Fig. 2 in Figure of description.
2. the execution of program is after coagulation curve is obtained, disturbance ecology is carried out first and obtains annoyance level D2, is then tied again
The real-time slope S of curve and absolute time T are closed, non-linear regression method is selected, specially determines the Size of Neighborhood in homing method.
The step that the adaptivity of coagulation curve is smooth is as follows：
1) curve for sliding acquisition certain length is cached；
2) signal interference method 2 is recognized, quantifies annoyance level D2；
3) the real-time slope S of curve is obtained；
4) curve absolute time T is obtained；
5) D2, S, T are combined and selects corresponding regression analysis.
Specific selection course is as follows in above-mentioned steps 5, the purpose is to select the neighborhood of nonlinear regression analysis, this neighborhood
Bigger smooth effect is better, but can increase memory consumption simultaneously.The present invention is opened in the case of for non-interference without caching
Ward off, and annoyance level is regarded when interference and is determined, specific decision-making is as follows：
1) calculated according to tri- variables of D2, S, T and obtain parameter P；
2) 2 threshold values Pth1, Pth2 are set；
3) when P is less than or equal to Pth1, the neighborhood of nonlinear regression analysis is K1；
4) when P, which is more than Pth1, is less than or equal to Pth2, the neighborhood of nonlinear regression analysis is K2；
5) when P is more than Pth2, the neighborhood of nonlinear regression analysis is K3.
(3) algorithm low memory consumption, high operation efficiency
It is embodied in：
1. adaptivity coagulation signal is filtered and adaptive coagulation curve filtering algorithm, entered in the way of sliding window
Row signal or curve caching, are filtered or smoothing processing rather than to whole signal or curve just for the signal that there is interference,
Operation efficiency is improved on the basis of reduction memory consumption.
2. adaptive coagulation curve filtering algorithm is handled on the neighborhood for choosing nonlinear regression analysis, adaptive to know
The degree do not disturbed, so that dynamic select Size of Neighborhood.
Brief description of the drawings
Fig. 1 is adaptivity coagulation signal filtering algorithm flow chart.
Fig. 2 is adaptive coagulation curve filtering algorithm flow chart.
Claims (4)
1. a kind of thrombelastogram instrument Adaptive Signal Processing Algorithm design, it is characterised in that including：Adaptive coagulation signal filter
Wave method, adaptive coagulation curve smoothing method.
2. this adaptive signal processing algorithm, operation efficiency is improved while relative saving memory consumption.
3. algorithm according to claim 1, adaptive coagulation signal filtering method characterized in that,
(1) AD signals are obtained, signal frequency is recognized.
(2) interference identification method 1.
(3) quantizing noise annoyance level D1, carries out related prompting if necessary.
(4) corresponding wave filter is selected to be filtered.
4. algorithm according to claim 1, its smooth feature of adaptive coagulation curve in,
(1) filtered coagulation signal is obtained, coagulation curve extraction is carried out.
(2) interference identification method 2.
(3) quantizing noise annoyance level, parameter P is obtained with reference to annoyance level D2 and the real-time slope S of curve, curve absolute time T.
(4) according to the size of parameter P values, corresponding nonlinear regression method is selected to be smoothed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710472660.3A CN107231142A (en) | 2017-06-21 | 2017-06-21 | A kind of thrombelastogram instrument Adaptive Signal Processing Algorithm |
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| CN201710472660.3A CN107231142A (en) | 2017-06-21 | 2017-06-21 | A kind of thrombelastogram instrument Adaptive Signal Processing Algorithm |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109684908A (en) * | 2018-09-21 | 2019-04-26 | 深圳沃德生命科技有限公司 | A kind of signal filtering method for thrombelastogram instrument |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5526111A (en) * | 1993-08-31 | 1996-06-11 | Boehringer Mannheim Corporation | Method and apparatus for calculating a coagulation characteristic of a sample of blood a blood fraction or a control |
| CN103634026A (en) * | 2013-12-02 | 2014-03-12 | 集美大学 | Digital zero intermediate frequency self-adaptation wave trapping method based on FPGA (filed programmable gate array) |
| US20140156197A1 (en) * | 2012-12-04 | 2014-06-05 | Samsung Electronics Co., Ltd. | Method and apparatus for processing signal |
| CN104323769A (en) * | 2014-09-19 | 2015-02-04 | 中国人民解放军第三军医大学 | Method and system for restricting ECG (Electrocardiogram) interference caused by cardio-pulmonary resuscitation and external defibrillator |
| CN104660217A (en) * | 2015-01-09 | 2015-05-27 | 深圳市美的连电子科技有限公司 | Signal analysis method and terminal |
| CN104739371A (en) * | 2013-12-31 | 2015-07-01 | 深圳迈瑞生物医疗电子股份有限公司 | Monitor and automatic multi-lead signal switching method and device thereof |
| CN106204497A (en) * | 2016-07-20 | 2016-12-07 | 长安大学 | A kind of pavement crack extraction algorithm based on smooth smoothed curve and matched curve |
| CN106645434A (en) * | 2016-11-17 | 2017-05-10 | 中国船舶重工集团公司第七0研究所 | Recognition method of strong pulse signal based on slope and amplitude difference value threshold |
-
2017
- 2017-06-21 CN CN201710472660.3A patent/CN107231142A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5526111A (en) * | 1993-08-31 | 1996-06-11 | Boehringer Mannheim Corporation | Method and apparatus for calculating a coagulation characteristic of a sample of blood a blood fraction or a control |
| US20140156197A1 (en) * | 2012-12-04 | 2014-06-05 | Samsung Electronics Co., Ltd. | Method and apparatus for processing signal |
| CN103634026A (en) * | 2013-12-02 | 2014-03-12 | 集美大学 | Digital zero intermediate frequency self-adaptation wave trapping method based on FPGA (filed programmable gate array) |
| CN104739371A (en) * | 2013-12-31 | 2015-07-01 | 深圳迈瑞生物医疗电子股份有限公司 | Monitor and automatic multi-lead signal switching method and device thereof |
| CN104323769A (en) * | 2014-09-19 | 2015-02-04 | 中国人民解放军第三军医大学 | Method and system for restricting ECG (Electrocardiogram) interference caused by cardio-pulmonary resuscitation and external defibrillator |
| CN104660217A (en) * | 2015-01-09 | 2015-05-27 | 深圳市美的连电子科技有限公司 | Signal analysis method and terminal |
| CN106204497A (en) * | 2016-07-20 | 2016-12-07 | 长安大学 | A kind of pavement crack extraction algorithm based on smooth smoothed curve and matched curve |
| CN106645434A (en) * | 2016-11-17 | 2017-05-10 | 中国船舶重工集团公司第七0研究所 | Recognition method of strong pulse signal based on slope and amplitude difference value threshold |
Non-Patent Citations (3)
| Title |
|---|
| KAH BIN LIM等: "Bayesian kernel inference for 2D objects recognition based on normalized curvature", 《2006 12TH INTERNATIONAL MULTI-MEDIA MODELLING CONFERENCE》 * |
| 周焰，等: "一种曲线平滑的自适应方法", 《计算机工程》 * |
| 石光明等: "基于分段轮廓平滑的目标识别", 《光子学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109684908A (en) * | 2018-09-21 | 2019-04-26 | 深圳沃德生命科技有限公司 | A kind of signal filtering method for thrombelastogram instrument |
| CN109684908B (en) * | 2018-09-21 | 2023-05-02 | 深圳沃德生命科技有限公司 | Signal filtering method for thromboelastography |
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Address after: 102200 Beijing, Changping District super Road, building No. 37, No. 7 Applicant after: Beijing Lepu Diagnostic Technology Co., Ltd Address before: 102200 Beijing, Changping District super Road, building No. 37, No. 7 Applicant before: BEIJING LEPU MEDICAL TECHNOLOGY Co.,Ltd. |
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