CN111588368A - Signal processing filtering method - Google Patents
Signal processing filtering method Download PDFInfo
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- CN111588368A CN111588368A CN202010446291.2A CN202010446291A CN111588368A CN 111588368 A CN111588368 A CN 111588368A CN 202010446291 A CN202010446291 A CN 202010446291A CN 111588368 A CN111588368 A CN 111588368A
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- 238000001914 filtration Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 17
- 210000004556 brain Anatomy 0.000 claims abstract description 32
- 238000005070 sampling Methods 0.000 claims abstract description 7
- 230000002490 cerebral effect Effects 0.000 claims abstract description 4
- 230000017531 blood circulation Effects 0.000 claims description 28
- 230000008859 change Effects 0.000 claims description 16
- 238000010586 diagram Methods 0.000 claims description 9
- 239000008280 blood Substances 0.000 claims description 5
- 210000004369 blood Anatomy 0.000 claims description 5
- 210000003625 skull Anatomy 0.000 claims description 4
- 230000008344 brain blood flow Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 3
- 230000003727 cerebral blood flow Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 210000002565 arteriole Anatomy 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 210000001124 body fluid Anatomy 0.000 description 1
- 239000010839 body fluid Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000000004 hemodynamic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000036581 peripheral resistance Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 230000002792 vascular Effects 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
- A61B5/026—Measuring blood flow
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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
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0536—Impedance imaging, e.g. by tomography
-
- 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
- A61B5/7225—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
Abstract
The invention discloses a signal processing filtering method, which specifically comprises the following steps: 1) extracting characteristic points, dividing heartbeat characteristic cycles, wherein the brain electrical impedance data is continuous waveform data and comprises a plurality of heartbeat cycles. Taking a 50Hz sampling frequency device as an example, 500-point brain impedance data are acquired after 10 s. 2) And (5) performing cubic spline interpolation to obtain a basic impedance curve, removing the basic impedance, and completing reconstruction of the cerebral impedance rheogram. 3) And 4) Butterworth filtering is used for filtering noise, an impedance curve contains excessive noise, and 4) the Butterworth filtering causes the whole waveform to be translated rightwards, and the phases of all characteristic points are translated along with the translation, so that the positions of the characteristic points need to be determined on the filtered waveform again.
Description
Technical Field
The invention relates to the technical field of filtering of a hemogram, in particular to a signal processing filtering method.
Background
At present, the application of the filtering technology to the blood flow graph is relatively independent. In the cerebral blood flow graph technology, the electrical conductivity of various tissue structures of a human body is different, including various body fluids, and the electrical conductivity of blood is the best. Weak high-frequency current is applied between two parts of the skull, and the current or voltage change between two detection electrodes is observed according to the ohm's law and the principle of volume conduction, so that the transient change condition of the hemodynamics in the electric field range of the detection part can be known. The change of the electrical impedance of the brain caused by each beat of the heart is a time-dependent function curve, which is closely related to the heart activity (inflow), reflects the change of the vascular tension and elasticity, and is influenced by the peripheral resistance (the calibers of the middle, small arteries and arterioles) and the blood viscosity (the fluid property). Different human cerebrovascular health states have different degrees and different waveforms and waveform indexes, and the cerebrovascular health state of the user is judged by comparing the waveform indexes with the normal waveforms. In the blood flow graph filtering technology, various noises are attached to the current blood flow graph acquisition process, so that a great deal of interference is caused on identification calculation and the like of the blood flow graph. Therefore, the invention provides a filtering technology for a hemogram.
Disclosure of Invention
1. Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
A signal processing and filtering method is used for denoising, reconstructing a blood flow diagram and correcting a phase of impedance blood flow diagram data acquired by an intelligent head ring. Impedance flowsheet data acquired by the device is converted into standard flowsheet data using some other signal processing method.
The method specifically comprises the following steps:
1) extracting characteristic points, dividing heartbeat characteristic cycles, collecting 500-point brain impedance data which comprises a plurality of heartbeat cycle data in 10s by taking 50Hz sampling frequency equipment as an example, wherein the brain impedance data is continuous waveform data.
2) And calculating the basic impedance, performing cubic spline interpolation by taking the characteristic points as base points to obtain a basic impedance curve, removing the basic impedance, and completing the reconstruction of the cerebral impedance hemogram.
3) And the Butterworth filtering is used for filtering noise, an impedance curve contains excessive noise, and the brain impedance blood flow graph t needs to be filtered in order to reflect the change of the brain blood flow volume.
4) The phase correction and the Butterworth filtering cause the waveform to shift to the right integrally, and the phases of all the characteristic points also shift along with the shift, so that the positions of the characteristic points need to be determined on the filtered waveform again.
And (2) according to the feature point extraction in the step (1), dividing heartbeat feature periods, measuring and calculating by heart rate of 60-75/min, wherein the heartbeat periods are about 10 periods, and the heartbeat feature point P needs to be obtained firstly to divide each heartbeat period.
According to the Butterworth filtering in step 3, "Butterworth filtering of 4 th order (the filtering parameter is based on filtering the curve into a near-chord curve L)", "obtaining P by taking the maximum value", "phase correction (finding the maximum value point, namely P, within the range of +/-10 points on the L by taking P as the reference)" three small steps, and obtaining P. And dividing each heartbeat cycle by taking the P point as a heartbeat cycle characteristic point.
The filtering parameters are based on the curve is filtered into a near-chord curve L, and a maximum value point, namely P, is found in a range of +/-10 points on L by taking P as a reference.
According to step 2: the brain impedance data comprises basic impedance values such as brain impedance, brain electrode contact impedance, skull impedance and the like, and the basic impedance values need to be removed to obtain brain impedance blood flow diagram data. While flipping the curve L along the X-axis so that the impedance change appears as a blood volume change.
And (3) carrying out cubic spline interpolation by taking the point P as a datum point to obtain a basic impedance curve B, and obtaining an unfiltered brain impedance blood flow graph curve t by taking B-L as t.
According to step 3:
filtering parameters: the sampling frequency fs is 50 Hz;
passband boundary frequency: wc is 8 × 2/fs (rad/s).
2. Advantageous effects
Compared with the prior art, the invention has the advantages that:
the invention carries out noise reduction processing on the acquired impedance data, and improves the signal-to-noise ratio; the basic impedance can be removed more accurately, the volume change of the blood flow graph can be reflected, and the data of the blood flow graph can be restored better; after optimization, later data processing is facilitated, and effective parameters are extracted; the anti-interference performance and stability of signals are improved; the calculated amount is small, and the real-time performance is high; can adapt to different types of blood flow graphs; and accurately marking off the heartbeat cycle.
Drawings
FIG. 1 is a flowchart of the filtering algorithm for a hemogram of the present invention;
FIG. 2 is a schematic diagram of a heartbeat cycle according to the present invention;
FIG. 3 is a schematic representation of a brain impedance rheogram reconstruction of the present invention;
FIG. 4 is a schematic diagram of noise filtering according to the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; all other embodiments obtained by a person skilled in the art without making any inventive step are within the scope of protection of the present invention.
Referring to fig. 1, the purpose of the filtering algorithm for a blood flow graph is to perform denoising, reconstruction of the blood flow graph, and phase correction on impedance blood flow graph data acquired by an intelligent head loop. The cubic spline interpolation algorithm and the Butterworth filtering algorithm used in the processing process are standard general algorithms. The filtering algorithm of the blood flow graph is to combine cubic spline interpolation algorithm and Butterworth filtering algorithm, the impedance blood flow graph data that the apparatus gathers are changed into the standard blood flow graph data, the impedance data of the brain, describe as the impedance change of the brain, it turns into the impedance blood flow graph data of the brain through operations such as "remove the impedance baseline", "filtering", etc., describe as the change of the volume of cerebral blood flow.
According to the graph shown in fig. 2, feature point extraction is performed, a heartbeat feature period is divided, the brain impedance data is continuous waveform data, and by taking 50Hz sampling frequency equipment as an example, 500-point brain impedance data obtained by continuously acquiring 10s is shown as a curve L in fig. 2. The heart rate is measured and calculated by 60-75/min, wherein the heart cycle is about 10 cycles. The heartbeat feature point P needs to be obtained first to divide each heartbeat cycle. P is obtained by three steps of ' 4-order Butterworth filtering (the filtering parameter is based on filtering the curve into a near-chord curve L), ' obtaining a maximum value to obtain P ', ' phase correction (finding a maximum value point, namely P, within a range of +/-10 points on the basis of P on L) '. And dividing each heartbeat cycle by taking the P point as a heartbeat cycle characteristic point.
According to the illustration of fig. 3, cubic spline interpolation is performed to obtain the basic impedance curve. And removing the basic impedance to complete the reconstruction of the cerebral impedance hemogram. The brain impedance data comprises basic impedance values such as brain impedance, brain electrode contact impedance, skull impedance and the like, and the basic impedance values are removed, so that the brain impedance blood flow diagram data can be obtained. While flipping the curve L along the X-axis so that the impedance change appears as a blood volume change. And (3) carrying out cubic spline interpolation by taking the point P as a datum point to obtain a basic impedance curve B, and obtaining an unfiltered brain impedance blood flow graph curve t by taking B-L as t.
According to fig. 4, butterworth filters, filter out noise. Because the original impedance curve contains excessive noise, the brain impedance blood flow graph t needs to be filtered in order to more intuitively reflect the change of the brain blood flow volume. Selecting 3-order Butterworth low-pass filtering to process to obtain a final brain impedance blood flow graph data curve T;
filtering parameters: the sampling frequency fs is 50 Hz;
the passband boundary frequency wc is 8 × 2/fs (rad/s).
The butterworth filtering causes the waveform to shift to the right as a whole, and the phases of all the feature points also shift along with the shift, so that the positions of the feature points need to be determined again on the filtered waveform.
The foregoing is only a preferred embodiment of the present invention; the scope of the invention is not limited thereto. Any person skilled in the art should be able to cover the technical scope of the present invention by equivalent or modified solutions and modifications within the technical scope of the present invention.
Claims (7)
1. A signal processing and filtering method is used for denoising, reconstructing a blood flow diagram and correcting a phase of impedance blood flow diagram data acquired by an intelligent head ring. Converting impedance flowsheet data acquired by the device into standard flowsheet data using some other signal processing method;
the method specifically comprises the following steps:
1) extracting characteristic points, dividing heartbeat characteristic cycles, collecting 500-point brain impedance data which comprises a plurality of heartbeat cycle data in 10s by taking 50Hz sampling frequency equipment as an example, wherein the brain impedance data is continuous waveform data.
2) And calculating the basic impedance, performing cubic spline interpolation by taking the characteristic points as base points to obtain a basic impedance curve, removing the basic impedance, and completing the reconstruction of the cerebral impedance hemogram.
3) And the Butterworth filtering is used for filtering noise, an impedance curve contains excessive noise, and the brain impedance blood flow graph t needs to be filtered in order to reflect the change of the brain blood flow volume.
4) The butterworth filtering causes the waveform to shift to the right as a whole, and the phases of all the feature points also shift along with the shift, so that the positions of the feature points need to be determined again on the filtered waveform.
2. A signal processing filtering method according to claim 1, characterized by: and (2) according to the feature point extraction in the step (1), dividing heartbeat feature periods, measuring and calculating by heart rate of 60-75/min, wherein the heartbeat periods are about 10 periods, and the heartbeat feature point P needs to be obtained firstly to divide each heartbeat period.
3. A signal processing filtering method according to claim 1, characterized by: according to the Butterworth filtering in step 3, "Butterworth filtering of 4 th order (the filtering parameter is based on filtering the curve into a near-chord curve L)", "obtaining P by taking the maximum value", "phase correction (finding the maximum value point, namely P, within the range of +/-10 points on the L by taking P as the reference)" three small steps, and obtaining P. And dividing each heartbeat cycle by taking the P point as a heartbeat cycle characteristic point.
4. A signal processing filtering method according to claim 3, characterized in that: the filtering parameters are based on the curve is filtered into a near-chord curve L, and a maximum value point, namely P, is found in a range of +/-10 points on L by taking P as a reference.
5. A signal processing filtering method according to claim 1, characterized by: according to step 2: the brain impedance data comprises basic impedance values such as brain impedance, brain electrode contact impedance, skull impedance and the like, and the basic impedance values need to be removed to obtain brain impedance blood flow diagram data. While flipping the curve L along the X-axis so that the impedance change appears as a blood volume change.
6. A signal processing filtering method according to claim 5, characterized by: and (3) carrying out cubic spline interpolation by taking the point P as a datum point to obtain a basic impedance curve B, and obtaining an unfiltered brain impedance blood flow graph curve t by taking B-L as t.
7. A signal processing filtering method according to claim 1, characterized by: according to step 3:
filtering parameters: the sampling frequency fs is 50 Hz;
passband boundary frequency: wc is 8 × 2/fs (rad/s).
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| CN202010446291.2A CN111588368A (en) | 2020-05-25 | 2020-05-25 | Signal processing filtering method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115865046A (en) * | 2023-02-15 | 2023-03-28 | 南京凯奥思数据技术有限公司 | Butterworth low-pass filtering method and filter based on FPGA |
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|---|---|---|---|---|
| CN87106212A (en) * | 1987-09-05 | 1988-08-03 | 哈尔滨工业大学 | Intelligent free respiration impedance blood flow graph instrument |
| CN1305778A (en) * | 2000-01-20 | 2001-08-01 | 深圳市辉大高科技发展有限公司 | Admittance-type automatic test method of bilateral cerebral technogram and tester based on said method |
| CN202173395U (en) * | 2011-07-22 | 2012-03-28 | 天津万安康泰医疗科技有限公司 | Digital brain impedance rheogram machine |
| CN202207142U (en) * | 2011-09-08 | 2012-05-02 | 程浩川 | Brain impedance image monitor |
| CN103619244A (en) * | 2011-04-12 | 2014-03-05 | 奥森医疗科技有限公司 | Devices and methods for monitoring intracranial pressure and additional intracranial hemodynamic parameters |
-
2020
- 2020-05-25 CN CN202010446291.2A patent/CN111588368A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN87106212A (en) * | 1987-09-05 | 1988-08-03 | 哈尔滨工业大学 | Intelligent free respiration impedance blood flow graph instrument |
| CN1305778A (en) * | 2000-01-20 | 2001-08-01 | 深圳市辉大高科技发展有限公司 | Admittance-type automatic test method of bilateral cerebral technogram and tester based on said method |
| CN103619244A (en) * | 2011-04-12 | 2014-03-05 | 奥森医疗科技有限公司 | Devices and methods for monitoring intracranial pressure and additional intracranial hemodynamic parameters |
| CN202173395U (en) * | 2011-07-22 | 2012-03-28 | 天津万安康泰医疗科技有限公司 | Digital brain impedance rheogram machine |
| CN202207142U (en) * | 2011-09-08 | 2012-05-02 | 程浩川 | Brain impedance image monitor |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115865046A (en) * | 2023-02-15 | 2023-03-28 | 南京凯奥思数据技术有限公司 | Butterworth low-pass filtering method and filter based on FPGA |
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