CN109157220B - Respiratory index extraction system based on multi-channel dynamic monitoring and working method - Google Patents
Respiratory index extraction system based on multi-channel dynamic monitoring and working method Download PDFInfo
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- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
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- 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
- A61B5/0531—Measuring skin impedance
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- 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
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
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- 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]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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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
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
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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
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Abstract
The invention relates to the field of medical machinery, in particular to a respiratory index extraction system based on multi-channel dynamic monitoring and a working method. The device comprises a body surface excitation electrode, a body surface electrocardioelectrode, a body surface reference electrode, an intracardiac catheter, a multi-channel electrophysiological measurement device and a computer system, and is characterized in that: the body surface exciting electrodes, the body surface electrocardio electrodes and the body surface reference electrodes are all connected to a multi-channel physiological measuring device, two intracardiac catheters are further arranged on the multi-channel physiological measuring device, the intracardiac catheters are inserted into the heart, and the multi-channel physiological measuring device is connected with a computer system. Compared with the prior art, the method can dynamically monitor the multi-channel signal containing the respiration, feed back and update the data source channel of the respiration index in real time, and obtain the respiration index with better signal-to-noise ratio; the method is beneficial to monitoring the respiration state of a patient in operation and the electrode attaching state in operation, and improves the positioning accuracy of the interventional surgical instrument and the definition of medical three-dimensional imaging.
Description
Technical Field
The invention relates to the field of medical machinery, in particular to a respiratory index extraction system based on multi-channel dynamic monitoring and a working method.
Background
Atrial Fibrillation (Atrial Fibrillation) is the most common cardiac arrhythmia in the clinic, with prevalence estimated to vary from 1% in the general population to 17% in the 80 year old population. Patients with atrial fibrillation account for about one third of hospitalized arrhythmias, and the incidence increases with age. Atrial fibrillation was defined in 2010 as: the atria lose normal and orderly electrical activity and are replaced by rapidly disorganized tremor waves, which are the most serious barriers to atrial activity. With the aging population and the rise of cardiovascular morbidity, atrial fibrillation has become a public health safety problem with high complications, and seriously harms the physical and mental health of human beings. In foreign countries, the prevalence of atrial fibrillation in the general population is about 0.4% to 1.0%. From the ATRIA survey, it is known that patients with atrial fibrillation have a prevalence rate of 0.1% below 50 years and a prevalence rate of 9% above 80 years, which indicates that the prevalence rate of atrial fibrillation increases with age. From the Framingharm survey sample, the prevalence of atrial fibrillation in a subgroup aged over 80 years increased to 8.8%. Framingharm survey shows that the atrial fibrillation prevalence is increased by 1 time in 1968-1988. In the Rotterdam area of the Netherlands, the rate of atrial fibrillation in residents aged 55-59 years is only 0.7%, but the rate of atrial fibrillation in the crowd aged over 85 years reaches 17.8%. In the northern area of the population, the prevalence of atrial fibrillation in residents over 65 years of age is 7.4%. The japanese national cardiovascular disease census shows that the prevalence of atrial fibrillation in this country has risen from 0.7% in 1980 to 0.9% in 2000, and the number of patients with atrial fibrillation has increased by 1-fold within 20 years, which is expected to exceed 100 million by 2020. A study of the singapore on 1839 chinese people older than 55 years showed that this population had an atrial fibrillation prevalence of 1.5%, with men having a higher prevalence than women. According to statistics, about 220 million people in the United states have paroxysmal or persistent atrial fibrillation in the last 90 th century, and the prevalence rate of general population is 0.4%. With the aging population, the incidence of atrial fibrillation is increasing, from the united states to 2050, atrial fibrillation is expected to affect 600-1200 ten thousand people, and from the european to 2060, atrial fibrillation is expected to affect 1790 ten thousand people. In China, 2 epidemiological surveys are mainly referred to for the epidemic situation of atrial fibrillation. In 2002, the rate of atrial fibrillation in Chinese was shown to be 0.7% according to survey. In 2004, 29079 adults of natural population in 14 provinces and cities of China were subjected to epidemiological investigation statistics, wherein the age range was 30 to 85 years, and the research showed that the total incidence of atrial fibrillation in China was 0.77%.
In recent years, with the continuous development and application of a catheter ablation technology, a plurality of groups of clinical random contrast tests prove that the treatment effect of the catheter-ablated atrial fibrillation is better, the success rate of the treatment on paroxysmal atrial fibrillation is up to 90 percent, and the treatment effect is far higher than that of an antiarrhythmic medicament. For atrial fibrillation, currently, a three-dimensional cardiac mapping technology is mainly used for diagnosing and positioning the focus position, and a radio frequency ablation technology is applied to carry out bioelectricity isolation on a target spot. The intracardiac catheter electric field location is the key technology of endocardium mapping and radio frequency ablation, the power plant location is the catheter location technology calculated based on the impedance gradient related to the catheter electrode and the reference, the body surface driving electrode and the related measuring circuit of the electric field location are made of common materials, the processing technology is simple, and the cost is low. Without special processes, the electrodes of a common catheter can be positioned using an electric field. In the endocardial navigation device positioned in an electric field mode, the acquisition of a positioning signal is carried out by measuring an electric field applied to the trunk of a human body through a catheter electrode, and the electric conductivity of air in the lung is greatly different from that of other tissues, so that the lung has a great influence on the distribution of a low-frequency electric field applied to the human body from the body surface, the influence is dynamically time-varying along with the respiration of a patient, the amplitude of the electric potential measured from a fixed position of the human body is also time-varying along with the respiration, the signal is an obvious interference source for a measuring system, and the respiratory motion is easily influenced by consciousness, so that the extraction of a stable respiratory waveform is critical.
The respiratory reference signal generally records the respiratory signal by monitoring mechanical, electrical, temperature changes caused by respiratory motion. Currently, common respiratory monitoring methods are: the electrical impedance monitor measures the change of the measuring potential, and the electrical impedance measuring mode is that the electrical conductivity of the human body is changed along with the change of respiration according to the characteristics of the volume conductor of the human body; the temperature sensitive monitoring system measures respiratory signals, air convection is generated by inspiration and expiration in the respiratory process, and temperature change is further caused, the measurement mode has certain requirements on the environment temperature, and the temperature sensitive monitoring system has higher sensitivity on a sensor for measuring the temperature; the spirometer measures the change of the volume of the lung, which is a measure of the volume of gas, and the volume change of the gas exhaled by the exhalation during respiration is not beneficial to measuring the respiratory state during inhalation; the real-time position monitoring system tracks and measures the infrared reflection mark fixed on the chest of the patient, the measuring mode is that according to the change of the respiratory thorax, the mark fixed on the chest of the patient is driven, the infrared reflection receiving time is measured, and the displacement change of the mark is calculated, so that the respiratory change state is obtained.
The respiratory interference is the main dynamic interference factor influencing the inaccurate positioning of the electric field, and the extraction of stable respiratory signals is the basis for dynamic compensation of the positioning electric field. An EDR algorithm is proposed in a model to estimate respiration from vector memory measures published in 1974, and in the method, the respiration amplitude and frequency are obtained by extracting vector cardiogram (VGG) information by taking an electrocardiographic waveform as a data source. The respiratory signal is extracted by taking a quadratic B-spline wavelet method as a core in the design of respiratory signals from single-lead ecg published in 2008. "respiratory rate estimation based on electrocardiographic and pulse wave data fusion", published in 2012, uses a body surface electrocardiographic signal as a data source, calculates RR intervals of a body surface QRS complex, and extracts a respiratory signal by a Singular Value Decomposition (SVD) method. Data fusion for simulating respiratory rate from a single-lead ECG published in 2013 suggests that there is a respiratory modulation signal in the cardiac signal, and the modulation can be extracted by Data fusion. Respiration waveforms were extracted by multi-lead electrocardiographic waveform fitting, published in 2015. The above methods all propose methods for extracting respiratory signals from single or multi-channel signals. But does not dynamically monitor and alarm the multi-channel data acquisition.
Disclosure of Invention
The respiratory index extraction system and the working method thereof can improve the signal-to-noise ratio of respiratory signals through multi-channel dynamic monitoring, simultaneously monitor the contact or attachment state of electrodes, and carry out normalization processing through the extracted respiratory signals to obtain the respiratory index, are beneficial to monitoring the respiratory state of a patient in operation, and improve the positioning accuracy of an interventional surgical instrument and the definition of medical three-dimensional imaging.
In order to achieve the purpose, the respiratory index extraction system based on multi-channel dynamic monitoring and the working method are designed, and the respiratory index extraction system comprises a human body, a heart, a body surface exciting electrode, a body surface electrocardioelectrode, a body surface reference electrode, an intracardiac catheter, a multi-channel electrophysiological measurement device and a computer system, and is characterized in that: the body surface exciting electrodes are at least six and are respectively stuck outside the body surface of the human body, the body surface electrocardio-electrodes are stuck at the chest lead position in the conventional body surface twelve leads of the human body, the body surface reference electrode is attached to the right lower abdomen of the human body, the body surface excitation electrode, the body surface electrocardio-electrode and the body surface reference electrode are all connected on the multi-channel physiological measurement device, the multi-channel physiological measurement device is also provided with two intracardiac catheters which are inserted into the heart and connected with a computer system, the computer system consists of a multi-channel dynamic monitoring device, an alarm device, a breathing index extraction device and a display, the multi-channel dynamic monitoring device is provided with three interfaces which are respectively connected with the multi-channel physiological measuring device, the alarm device and the respiratory index extraction device, the respiratory index extraction device is provided with two ports and is respectively connected with the multi-channel dynamic monitoring device and the display;
the working method comprises the following steps:
s1, collecting data sources of N channels of the body surface excitation electrode, the body surface electrocardio electrode and the body surface reference electrode, and setting a threshold TH and a preset value M of an effective coefficient;
s2, preprocessing the data acquired by the multiple channels, and mainly filtering out high-frequency interference and baseline drift;
s3, performing principal component calculation on the preprocessed multi-channel data;
s4, performing correlation calculation on the calculation result of S3 and the preprocessed data of each channel to obtain N correlation numbers;
s5, comparing the N relative numbers with the preset relative threshold, entering the effective channel array with the relative coefficient larger than the threshold TH and executing the step S7, otherwise, entering the abnormal channel array with the relative coefficient smaller than or equal to the threshold TH and executing the step S6;
s6, carrying out alarm prompt on the abnormal channel in S5;
s7, judging the number of effective channels in S5, wherein the number of effective channels is larger than a preset value M, indicating that enough channel data are available for extracting the respiratory index, and executing the step S9; if the number of the effective channels is less than the preset value M, the strength of the respiration signal is weak, the respiration index is not extracted enough, and the step S8 is executed;
s8, prompting by the computer system to perform abnormal electrode adjustment, and then returning to S1 to perform effective coefficient extraction again;
s9, selecting data with more than M channels in S7 to calculate the principal component again, and removing the data with lower relevance compared with the principal component result in S3;
s10, setting a time period T, and performing one-time circulation in the time period T to achieve the purpose of real-time monitoring; for this purpose, the principal component result obtained in S9 is subjected to normalization processing;
and S11, displaying and outputting the respiration index.
The range of the correlation coefficient threshold TH in the step S1 is 0.85-0.9; the predetermined value M of the significant coefficient is larger than 5, and M is smaller than an integer of N/3, wherein N is the total number of channels.
The preprocessing method of the data collected by multiple channels in the step S2 is to combine low-pass filtering and median filtering to design a group of filtering parameters, and different data adopt the same filtering parameters, so as to ensure that different data have the same time delay after being filtered; the cut-off frequency of the low-pass filtering is 1Hz, the low-pass filtering is used for filtering high-frequency interference with the frequency higher than the heart rate, and the median filtering window is selected to be 0.8 multiplied by TrspX fs, wherein TrspFs is the sampling frequency for the breathing cycle.
The principal component calculation step in step S3 is: s31, Signal XiRemoving direct current; s32, solving a covariance matrix; s33, solving an eigenvalue and an eigenvector of the covariance matrix; s34, selecting the maximumA feature value and a feature vector; and S35, calculating the projection sum of each signal on the characteristic vector.
In the correlation calculation in step S4, the correlation coefficient ρ =
，
Wherein E (X)i) Is the expectation of X, D (X)i) Is XiXi is one of the data, i =1,2 … N, Y is the principal component calculation result.
The value range of the period T in the step S9 is 2Trsp～5Trsp。
The normalization processing in step S10 includes extracting a mean value of the amplitude range of the respiratory signal over a period of time as a denominator of the normalization calculation, and calculating to obtain the respiratory index.
Compared with the prior art, the method can dynamically monitor the multi-channel signal containing the respiration, feed back and update the data source channel of the respiration index in real time, and obtain the respiration index with better signal-to-noise ratio; the method is beneficial to monitoring the respiration state of a patient in operation and the electrode attaching state in operation, and improves the positioning accuracy of the interventional surgical instrument and the definition of medical three-dimensional imaging.
Drawings
FIG. 1 is a schematic view of the apparatus of the present invention.
Fig. 2 is a flow chart of the operation of the present invention.
Fig. 3 is a waveform diagram of raw data collected in an embodiment of the present invention.
Fig. 4 is a waveform diagram before and after single-channel data preprocessing in an embodiment of the invention.
FIG. 5 is a waveform of a respiratory index calculated from the principal components and normalized according to an embodiment of the present invention.
Referring to fig. 1 to 5, wherein 1 is a human body, 2 is a heart, 3 is a body surface excitation electrode, 4 is a body surface electrocardioelectrode, 5 is a body surface reference electrode, 6 is an intracardiac catheter, 7 is a multichannel physiological measurement device, 8 is a computer system, 9 is a multichannel dynamic monitoring device, 10 is an alarm device, 11 is a respiratory index extraction device, and 12 is a display.
Detailed Description
The invention is further illustrated below with reference to the accompanying drawings.
Example (b):
FIG. 1 is a schematic view of the present invention. The multi-channel physiological measurement device collects signals from the intracardiac fixed electrodes, impedance signals of the body surface excitation electrodes, electric field positioning signals of the body surface electrocardio electrodes and other data sources containing respiratory signals. The multi-channel dynamic monitoring device monitors the multi-channel signal acquisition quality in real time and automatically distinguishes an effective channel from an abnormal channel. The alarm device alarms the abnormal channel in dynamic monitoring and prompts to adjust the abnormal electrode. The respiratory index extraction device extracts respiratory signals from effective communication signals obtained by dynamic monitoring and calculates respiratory indexes through normalization processing. The display is used for displaying the finally obtained breathing index.
As shown in fig. 2, a work flow diagram of the present invention is shown, and the specific work flow is as follows:
s1, collecting data sources of N channels, wherein the sources comprise signals of the intracardiac fixed electrodes, impedance signals of the body surface excitation electrodes, electric field positioning signals of the body surface electrocardioelectrodes and other data sources containing breathing signals, as shown in figure 3.
And S2, preprocessing the acquired data, and mainly filtering out high-frequency interference and baseline drift. The data preprocessing method is to combine low-pass filtering and median filtering to design a group of filtering parameters, and different data adopt the same filtering parameters, so as to ensure that different data have the same time delay after being filtered; the cut-off frequency of the low-pass filtering is 1Hz, the low-pass filtering is used for filtering high-frequency interference with the frequency higher than the heart rate, and the median filtering window is selected to be 0.8 multiplied by TrspX fs, wherein TrspFs is the sampling frequency for the breathing cycle. The filtering results are shown in fig. 4.
And S3, performing principal component calculation on the preprocessed data. The calculation step of the principal component is S31, Signal XiRemoving direct current; s32, finding the coordinationA variance matrix; s33, solving an eigenvalue and an eigenvector of the covariance matrix; s34, selecting the largest characteristic value and the largest characteristic vector; and S35, calculating the projection sum of each signal on the characteristic vector.
And S4, performing correlation calculation on the calculation result of the S3 and the preprocessed data of each channel to obtain N correlation numbers. The correlation calculation is formulated by a correlation coefficient ρ =
，
Wherein E (X)i) Is the expectation of X, D (X)i) Is XiXi is one of the data, i =1,2 … N, Y is the principal component calculation result.
S5, comparing the N relative numbers with the preset relative threshold, the relative coefficient is larger than the threshold TH entering the effective channel array and paralleling S7, otherwise, the relative coefficient is smaller than or equal to the threshold TH entering the abnormal channel array and executing S6. The range of the correlation coefficient threshold TH is 0.85-0.9.
And S6, performing alarm prompt on the abnormal channel in the S5.
S7, judging the number of the coefficients of the effective channel in S5, wherein the effective coefficient is larger than a preset value M, indicating that enough data are available for extracting the respiratory index, and executing the step S9; if the effective coefficient is smaller than the preset value M, it indicates that the data is insufficient, the strength of the respiration signal is weak, and the respiration index is not extracted enough, and step S8 is executed. The preset value M of the significant coefficient is more than 5 and is less than an integer of N/3, wherein N is the total channel number
S8, the computer system prompts abnormal electrode adjustment, and then returns to S1 to extract the significant coefficient again.
At S9, data of a number greater than M is selected at S7 and principal component calculation is performed again, and data with lower correlation is removed compared with the principal component result at S3.
S10, setting a time period T, and circulating once in the time period T to achieve real-time monitoringThe object of (a); for this purpose, the principal component result obtained in S9 is normalized. The value range of the period T is 2Trsp～5Trsp。
And S11, displaying and outputting the respiration index.
As shown in fig. 3, it is a waveform diagram of raw data acquired by multiple channels according to the present invention, in which the abscissa represents time, and the ordinate represents the magnitude of the electric potential measured by the electrode, and it can be known from the diagram that the signal intensities acquired by different channels are different, and the interference intensities are also different. Wherein 20 is a signal waveform acquired by the body surface excitation electrode, 21 is a signal waveform acquired by the intracardiac fixed electrode, and 22-25 are signal waveforms acquired by the body surface electrode. The respiration signal measured by the intracardiac fixed electrode has better signal-to-noise ratio, and the interference in the signal measured by the body surface electrode is stronger.
FIG. 4 is a waveform diagram before and after data preprocessing, which is an example of a single channel according to the present invention. In the figure, 26 is an original data waveform acquired by a certain channel, and 27 is a filtered data waveform, and it can be known that high-frequency interference and baseline drift can be better removed by adopting a preprocessing method of low-pass filtering and median filtering.
Fig. 5 is a waveform diagram of the respiratory index calculated and normalized for the principal component of the present invention. The amplitude range of the respiration waveform after normalization processing is-0.5. 28 is a respiration value which is monitored and extracted by multiple channels in a period of time, and the respiration value can better show the respiration depth change of different times.
Claims (7)
1. A working method of a respiratory index extraction system based on multi-channel dynamic monitoring comprises a human body, a heart, body surface exciting electrodes, body surface electrocardioelectrodes, body surface reference electrodes, an intracardiac catheter, a multi-channel electrophysiological measurement device and a computer system, and is characterized in that: body surface excitation electrode (3) are equipped with six at least and paste respectively outside the body surface of human (1), body surface electrocardioelectrode (4) paste the chest position of leading in the twelve leads of human conventional body surface, body surface reference electrode (5) paste the lower abdomen position in the right side of human body, body surface excitation electrode (3), body surface electrocardioelectrode (4) and body surface reference electrode (5) are all connected on multichannel physiology measuring device (7), still be equipped with two intracardiac pipes (6) on multichannel physiology measuring device (7), heart (2) are inserted in intracardiac pipe (6), and computer system (8) is connected in multichannel physiology measuring device (7), computer system (8) comprise multichannel dynamic monitoring device (9), alarm device (10), respiratory index extraction element (11) and display (12), multichannel dynamic monitoring device (9) are equipped with three interface and connect multichannel physiology measuring device respectively The device comprises a device (7), an alarm device (10) and a respiratory index extraction device (11), wherein the respiratory index extraction device (11) is provided with two ports and is respectively connected with a multi-channel dynamic monitoring device (9) and a display (12);
the working method comprises the following steps:
s1, collecting a data source of the total N channels of multiple channels of the body surface excitation electrode, the body surface electrocardio electrode and the body surface reference electrode, and setting a relevant threshold TH and an effective coefficient preset value M;
s2, preprocessing the data acquired by the multiple channels, and mainly filtering out high-frequency interference and baseline drift;
s3, performing principal component calculation on the preprocessed multi-channel data;
s4, performing correlation calculation on the calculation result of S3 and the preprocessed data of each channel to correspondingly obtain correlation coefficients of the N channels;
s5, comparing the correlation coefficients of the N channels with a preset correlation threshold TH, entering the effective channel array with the correlation coefficient larger than the threshold TH and executing the step S7, otherwise, entering the abnormal channel array with the correlation coefficient smaller than or equal to the threshold TH and executing the step S6;
s6, carrying out alarm prompt on the abnormal channel in S5;
s7, judging the number of effective channels in S5, wherein the number of effective channels is larger than a preset value M, indicating that enough channel data are available for extracting the respiratory index, and executing the step S9; if the number of the effective channels is less than the preset value M, the strength of the respiration signal is weak, the respiration index is not extracted enough, and the step S8 is executed;
s8, prompting by the computer system to perform abnormal electrode adjustment, and then returning to S1 to perform effective coefficient extraction again;
s9, selecting data with more than M channels in S7 to calculate the principal component again, and removing the data with lower relevance compared with the principal component result in S3;
s10, setting a time period T, and performing one-time circulation in the time period T to achieve the purpose of real-time monitoring; for this purpose, the principal component result obtained in S9 is subjected to normalization processing;
and S11, displaying and outputting the respiration index.
2. The working method of the respiratory index extraction system based on the multichannel dynamic monitoring as claimed in claim 1 is characterized in that: the range of the correlation coefficient threshold TH in the step S1 is 0.85-0.9; the predetermined value M of the significant coefficient is larger than 5, and M is smaller than an integer of N/3, wherein N is the total number of channels.
3. The working method of the respiratory index extraction system based on the multichannel dynamic monitoring as claimed in claim 1 is characterized in that: the preprocessing method of the data collected by multiple channels in the step S2 is to combine low-pass filtering and median filtering to design a group of filtering parameters, and different data adopt the same filtering parameters, so as to ensure that different data have the same time delay after being filtered; the cut-off frequency of the low-pass filtering is 1Hz, the low-pass filtering is used for filtering high-frequency interference with the frequency higher than the heart rate, and the median filtering window is selected to be 0.8 multiplied by TrspX fs, wherein TrspFs is the sampling frequency for the breathing cycle.
4. The working method of the respiratory index extraction system based on the multichannel dynamic monitoring as claimed in claim 1 is characterized in that: the principal component calculation step in step S3 is: s31, Signal XiRemoving direct current; s32, solving a covariance matrix; s33, solving an eigenvalue and an eigenvector of the covariance matrix; s34, selectingThe largest one eigenvalue and eigenvector; and S35, calculating the projection sum of each signal on the characteristic vector.
5. The working method of the respiratory index extraction system based on the multichannel dynamic monitoring as claimed in claim 1 is characterized in that: the correlation calculation and the correlation coefficient in the step S4
，
Wherein E (X)i) Is the expectation of X, D (X)i) Is Xi Xi is one of the data, i =1,2 … N, Y is the principal component calculation result.
6. The working method of the respiratory index extraction system based on the multichannel dynamic monitoring as claimed in claim 1 is characterized in that: the value range of the period T in the step S9 is 2Trsp～5Trsp。
7. The working method of the respiratory index extraction system based on the multichannel dynamic monitoring as claimed in claim 1 is characterized in that: the normalization processing in step S10 includes extracting a mean value of the amplitude range of the respiratory signal over a period of time as a denominator of the normalization calculation, and calculating to obtain the respiratory index.
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| CN115429250B (en) * | 2022-11-10 | 2023-04-14 | 北京肿瘤医院(北京大学肿瘤医院) | MRI (magnetic resonance imaging) gating method and system based on multi-channel pressure induction |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1228014A (en) * | 1996-07-30 | 1999-09-08 | 伊塔马医药(C.M)1997有限公司 | Method and apparatus for non-invasive detection of medical conditions by monitoring peripheral arterial tone |
| WO2009036327A1 (en) * | 2007-09-14 | 2009-03-19 | Corventis, Inc. | Adherent device for respiratory monitoring and sleep disordered breathing |
| CN101953685A (en) * | 2009-04-20 | 2011-01-26 | 迈瑞Ds美国有限责任公司 | Measure the device and method of electrocardiogram and breathing |
| CN103505202A (en) * | 2012-06-27 | 2014-01-15 | 天津宇迪智能技术有限公司 | Heart and brain coupled system for synchronously collecting ECG signals and EEG signals |
| CN104135912A (en) * | 2012-02-29 | 2014-11-05 | 通用电气公司 | System and method for determining physiological parameters based on electrical impedance measurements |
| CN105611872A (en) * | 2013-10-09 | 2016-05-25 | 皇家飞利浦有限公司 | An apparatus and method for evaluating multichannel ECG signals |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110066008A1 (en) * | 2009-09-14 | 2011-03-17 | Matt Banet | Body-worn monitor for measuring respiration rate |
| EP2969034A4 (en) * | 2013-03-15 | 2016-11-30 | Zoll Medical Corp | Processing impedance signals for breath detection |
| CN104720808B (en) * | 2013-12-20 | 2017-09-08 | 中国移动通信集团公司 | A kind of method and apparatus for detecting sleep quality breathing |
| CN105286868B (en) * | 2015-10-21 | 2018-11-16 | 深圳市理邦精密仪器股份有限公司 | A kind of method and device of the intelligent lead switching based on respiration parameter |
-
2018
- 2018-09-11 CN CN201811056299.7A patent/CN109157220B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1228014A (en) * | 1996-07-30 | 1999-09-08 | 伊塔马医药(C.M)1997有限公司 | Method and apparatus for non-invasive detection of medical conditions by monitoring peripheral arterial tone |
| WO2009036327A1 (en) * | 2007-09-14 | 2009-03-19 | Corventis, Inc. | Adherent device for respiratory monitoring and sleep disordered breathing |
| CN101953685A (en) * | 2009-04-20 | 2011-01-26 | 迈瑞Ds美国有限责任公司 | Measure the device and method of electrocardiogram and breathing |
| CN104135912A (en) * | 2012-02-29 | 2014-11-05 | 通用电气公司 | System and method for determining physiological parameters based on electrical impedance measurements |
| CN103505202A (en) * | 2012-06-27 | 2014-01-15 | 天津宇迪智能技术有限公司 | Heart and brain coupled system for synchronously collecting ECG signals and EEG signals |
| CN105611872A (en) * | 2013-10-09 | 2016-05-25 | 皇家飞利浦有限公司 | An apparatus and method for evaluating multichannel ECG signals |
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