CN108261196B - Electrocardio-electrode falling detection method and device, computer equipment and storage medium - Google Patents
Electrocardio-electrode falling detection method and device, computer equipment and storage medium Download PDFInfo
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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
- A61B5/25—Bioelectric electrodes therefor
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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
- A61B5/24—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
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6844—Monitoring or controlling distance between sensor and tissue
Abstract
The invention relates to an electrocardio-electrode falling detection method, a device, computer equipment and a storage medium, comprising the following steps: acquiring electrocardio data according to the differential signals acquired by the differential signal acquisition electrodes; detecting whether the differential signal acquisition electrode falls off or not by detecting the dispersion degree of the electrocardio data; and detecting whether the common mode signal suppression electrode falls off or not by detecting the periodicity of the electrocardio data. According to the electrocardioelectrode falling detection method, the electrocardioelectrode falling detection device, the computer equipment and the storage medium, whether the electrocardioelectrode falls or not is judged directly by analyzing the obtained electrocardio data, a hardware circuit does not need to be adjusted, the electrocardioelectrode falling detection method is not influenced by environmental factors and tester factors, and the accuracy and the consistency of the electrocardioelectrode falling detection are effectively improved.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to an electrocardio-electrode falling-off detection method, an electrocardio-electrode falling-off detection device, computer equipment and a storage medium.
Background
Along with the development of society, the life style and the dietary structure of people are greatly changed, the incidence rate of cardiovascular and cerebrovascular diseases is increased year by adding bad living habits and excessive psychological and mental stress, and researches show that the number of people dying from the cardiovascular and cerebrovascular diseases every year in the world is as high as 1500 ten thousands of people, and the people live at the first position of various causes of death. Aiming at the phenomenon, medically, people gradually adjust the early prevention from late treatment, an electrocardiograph is adopted to collect electrocardiosignals of people, and whether the people suffer from heart diseases can be diagnosed according to the collected electrocardiosignals, so that the treatment can be carried out as soon as possible.
The electrode of electrocardiograph has several different modes with human body, in which, the I lead connection method has three leads, respectively LA (left shoulder) electrode, RA (right shoulder) electrode and LL (left leg) electrode. The LA and RA lead ends collect the differential signals of the electrocardio of the human body, and the LL lead end inhibits the common mode signal. However, the electrodes may be detached due to various factors during the connection process, and therefore, a corresponding method is required to detect whether the electrodes are detached.
The traditional method is realized by adjusting hardware circuits, for example, detecting according to impedance change generated by electrode falling or detecting according to lead signal amplitude generated by electrode falling. However, the traditional method for detecting the falling off of the electrocardio-electrode has the defects of low accuracy and poor consistency under the influence of external factors such as detection environment, electrocardiosignal difference of a tester, body movement of the electrocardio-tester and the like.
Disclosure of Invention
In view of the above, it is necessary to provide an electrocardiograph electrode drop detection method, an electrocardiograph electrode drop detection device, a computer device, and a storage medium, for solving the problems of low accuracy and poor consistency of the conventional electrocardiograph electrode drop detection method.
An electrocardio-electrode falling-off detection method comprises the following steps: acquiring electrocardio data according to the differential signals acquired by the differential signal acquisition electrodes; detecting whether the differential signal acquisition electrode falls off or not by detecting the discrete degree of the electrocardio data; and detecting whether the common mode signal suppression electrode falls off or not by detecting the periodicity of the electrocardio data.
In one embodiment, the detecting whether the differential signal collecting electrode falls off by detecting the degree of dispersion of the electrocardiographic data includes the following steps: calculating to obtain the standard deviation of the electrocardio data; and comparing the standard deviation with a preset standard deviation threshold, and when the standard deviation is smaller than the preset standard deviation threshold, falling off the differential signal acquisition electrode.
In one embodiment, the detecting whether the differential signal collecting electrode falls off by detecting the degree of dispersion of the electrocardiographic data includes the following steps: calculating to obtain the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data; and comparing the ratio with a preset continuous data ratio threshold, and when the ratio is greater than the continuous data ratio threshold, dropping the differential signal acquisition electrode.
In one embodiment, the detecting whether the common mode signal suppression electrode falls off by detecting the periodicity of the electrocardiographic data includes the following steps: carrying out autocorrelation processing on the electrocardiogram data to obtain an autocorrelation processing result; periodically analyzing the electrocardio data according to the autocorrelation processing result to obtain a periodic analysis result; and performing drop detection on the common mode signal suppression electrode according to the periodic analysis result of the electrocardiogram data to obtain a detection result.
In one embodiment, the periodically analyzing the electrocardiographic data according to the autocorrelation processing result to obtain a periodic analysis result, includes the following steps: judging whether the electrocardiogram data has an extreme point or not according to the autocorrelation processing result; and when the extreme point exists, obtaining a periodic analysis result of the electrocardiogram data according to the extreme point and the autocorrelation data with zero delay in the autocorrelation processing result.
In one embodiment, the performing falling-off detection on the common-mode signal rejection electrode according to the result of the periodic analysis of the electrocardiographic data to obtain a detection result includes the following steps: and when the result of the periodic analysis of the electrocardio data is periodic, the common-mode signal electrode is suppressed to fall off.
An electrocardio-electrode detachment detection device, the device comprising: the electrocardio data acquisition module is used for acquiring electrocardio data according to the differential signal acquired by the differential signal acquisition electrode; the first electrode falling detection module is used for detecting whether the differential signal acquisition electrode falls off or not by detecting the dispersion degree of the electrocardio data; and the second electrode falling detection module is used for detecting whether the common-mode signal suppression electrode falls or not by detecting the periodicity of the electrocardio data.
In one embodiment, the second electrode-drop detection module includes: the autocorrelation processing module is used for carrying out autocorrelation processing on the electrocardio data to obtain an autocorrelation processing result; the periodicity analysis module is used for periodically analyzing the electrocardio data according to the autocorrelation processing result to obtain a periodicity analysis result; and the falling detection module is used for detecting the falling of the common mode signal suppression electrode according to the periodic analysis result of the electrocardio data to obtain a detection result.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
The electrocardioelectrode falling detection method, the electrocardioelectrode falling detection device, the computer equipment and the storage medium directly analyze the obtained electrocardio data so as to judge whether the electrocardioelectrode falls or not, do not need to adjust a hardware circuit, are not influenced by environmental factors and tester factors, and effectively improve the accuracy of the electrocardioelectrode falling detection.
Drawings
FIG. 1 is a flow chart of a method for detecting a detachment of an ECG electrode according to an embodiment;
FIG. 2 is a schematic diagram illustrating a process of detecting the falling of the differential signal collecting electrode in one embodiment;
FIG. 3 is a schematic diagram illustrating a process of detecting the falling of the differential signal collecting electrodes in another embodiment;
FIG. 4 is a schematic flow chart illustrating an embodiment of detecting common mode signal electrode dropout;
FIG. 5 is a schematic flow chart illustrating a method for suppressing common mode signal electrode drop detection in another embodiment;
FIG. 6 is a schematic diagram of an apparatus for detecting a falling-off of an ECG electrode according to an embodiment;
FIG. 7 is a schematic diagram of an embodiment of a differential signal acquisition electrode drop detection configuration;
FIG. 8 is a schematic diagram of a differential signal acquisition electrode drop detection configuration in another embodiment;
FIG. 9 is a schematic diagram of an embodiment of a structure for suppressing common mode signal electrode drop detection;
FIG. 10 is a schematic diagram of another embodiment of a structure for suppressing common mode signal electrode drop detection;
FIG. 11 is a schematic diagram of normal ECG according to an embodiment;
FIG. 12 is a schematic diagram of an electrocardiogram with the LA and RA electrodes detached in one embodiment;
FIG. 13 is a schematic diagram of an ECG showing the LL electrode falling off in one embodiment;
FIG. 14 is a diagram illustrating the result of autocorrelation processing of a normal cardiac signal in an embodiment;
FIG. 15 is a diagram illustrating the result of autocorrelation processing of an LL electrode shed electrocardiosignal in one embodiment;
FIG. 16 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Referring to fig. 1, a method for detecting the falling of an electrocardiograph electrode includes the steps of:
and S100, acquiring the electrocardiogram data according to the differential signals acquired by the differential signal acquisition electrodes. Specifically, two electrodes are used to collect differential signals of a human body, and the positions collected by the electrodes for collecting the differential signals are different in different lead connection methods. Taking the I-lead connection as an example, the differential signal acquisition electrodes are LA electrodes and RA electrodes. Because the electrocardiosignals are very small, the noise in the environment is possibly higher than the electrocardiosignals, and because the differential signals have stronger anti-interference capability, the interference of the environmental noise on the electrocardiosignals can be effectively inhibited by adopting the differential signals. Further, when acquiring the electrocardiogram data, the electrocardiogram data of the buffer area acquired by the acquisition card can be directly acquired, the length of the data is not unique, the electrocardiogram data stored in the buffer area within one second can be acquired, the electrocardiogram data of other time lengths can also be acquired, and the falling detection of the subsequent steps can be carried out according to the acquired electrocardiogram data.
And S200, detecting whether the differential signal acquisition electrode falls off or not by detecting the discrete degree of the electrocardio data. Specifically, when the differential signal collecting electrode falls off, the electrocardiographic data of the living body cannot be collected, so that the obtained electrocardiogram is a straight line, and all the data are the same in size. The electrocardiogram under the normal state and the electrocardiogram when the differential signal acquisition electrode falls off are obviously different. Therefore, whether the differential signal acquisition electrode falls off or not can be judged by directly detecting the discrete degree of the electrocardio data.
Further, the manner of detecting the degree of dispersion of the electrocardiographic data is not unique. Referring to fig. 2, in one embodiment, step S200 includes step S210 and step S220.
And step S210, calculating to obtain the standard deviation of the electrocardio data. Specifically, when calculating the standard deviation of the electrocardiographic data, according to the formula:
wherein x isiThe standard deviation represents the ith electrocardiogram data, sigma represents the standard deviation, N represents the number of the acquired electrocardiogram data, and u represents the average number of the acquired electrocardiogram data.
Step S220, comparing the standard deviation with a preset standard deviation threshold, and dropping the differential signal collecting electrode when the standard deviation is smaller than the preset standard deviation threshold. And comparing the standard deviation of the acquired differential signals obtained by calculation with a preset standard deviation, and when the standard deviation obtained by calculation is smaller than a preset standard deviation threshold value, the differential signal acquisition electrode falls off. Whether the differential signal acquisition electrode falls off or not can be judged by calculating the standard deviation of the electrocardio data and comparing the standard deviation with a preset standard deviation threshold value, a hardware circuit does not need to be operated, and the method has strong operation convenience. Here, the size of the preset standard deviation threshold is 0.02, and it should be noted that the value of the preset standard deviation threshold is not unique as long as the degree of dispersion of the data can be reasonably represented.
Specifically, the following algorithm may be adopted to implement the step S220:
wherein, dataBuff represents the electrocardiogram data, specifically, the electrocardiogram data can be the electrocardiogram data in a buffer of 1 second, std (dataBuff) represents the standard deviation of the electrocardiogram data, and stdThreshold represents the preset standard deviation threshold.
Referring to fig. 3, in another embodiment, step S200 includes step S230 and step S240.
Step S230, a ratio of the number of the adjacent point data equal to the number of the electrocardiographic data is obtained by calculation. Specifically, taking a set of data 1, 1, 2, 2, 3, 4, 5, 5, 6 as an example, where the number of the adjacent point data equals to 3, and the total data number equals to 9, the ratio of the number of the adjacent point data equals to the total data number is 3/9. Based on the method, the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data is calculated.
And step S240, comparing the ratio with a preset continuous data ratio threshold, and when the ratio is greater than the continuous data ratio threshold, dropping the differential signal acquisition electrode. Comparing the ratio of the number of the adjacent point data obtained by calculation to the number of the electrocardiographic data with a preset continuous data ratio threshold, wherein the size of the continuous data ratio threshold is 0.5, and it should be noted that the size of the continuous data ratio threshold is not unique as long as the dispersion degree of the electrocardiographic data can be effectively reflected; and when the ratio of the number of the adjacent point data equal to the number of the electrocardio data obtained by calculation to the number of the electrocardio data is larger than the preset continuous data ratio, the differential signal acquisition electrode falls off. The method is used for detecting the falling of the differential signal falling electrode, a hardware circuit is not required to be operated, the influence of environmental factors is avoided, and the method has strong operation convenience.
Specifically, step S240 may be implemented by using the following algorithm:
num1 represents the number of the data of the electrocardio data which are adjacent to each other, N represents the number of the electrocardio data, and ratiothhreshold represents a preset continuous data ratio threshold.
It should be noted that, in the above two embodiments, the method for detecting whether the differential signal collecting electrode falls off by detecting the dispersion degree of the electrocardiographic data may be any one of the methods in the specific detection process, or may be the method for detecting whether the differential signal collecting electrode falls off by using two detection methods simultaneously, and in the specific detection process, the method is selected according to the actual situation. If two methods are selected for detection, the following algorithm can be adopted:
and step S300, detecting whether the common mode signal suppression electrode falls off or not by detecting the periodicity of the electrocardio data. Specifically, suppressing the common mode signal electrode from falling off makes the circuit for acquiring the bioelectric cardiac signal unable to form a loop, and thus there is no output of the common mode suppression signal. Because the voltage amplitude of the common-mode signal is far greater than that of the electrocardiosignal, the shape and the frequency domain of the electrocardiosignal are close to the common-mode signal, and the common-mode signal is mainly subjected to 50Hz power frequency interference in the electrocardio detection process, so that whether the electrode for inhibiting the common-mode signal falls off or not can be judged by detecting the periodicity of the signal. Similarly, taking the I-lead connection as an example, the suppression common mode signal electrode is an LL electrode, and by periodically detecting the cardiac signal, it can be determined whether the LL electrode is detached.
Referring to fig. 4, in one embodiment, step S300 includes step S310, step S320 and step S330.
Step S310, autocorrelation processing is carried out on the electrocardio data to obtain autocorrelation processing results. Specifically, in one embodiment, the electrocardiographic data subjected to the autocorrelation processing is buffer electrocardiographic data within one second, and in consideration of real-time performance of electrode drop detection, when the electrocardiographic data is subjected to the autocorrelation processing, only the electrocardiographic data until the maximum delay M is calculated, where M is 0.02 Fs 2+2, where Fs is a sampling rate, and a calculation formula of the autocorrelation processing is as follows:
wherein, M is 0, 1, 2 … M, dataBuff is the one-second buffer electrocardio data, and N is the number of the one-second buffer electrocardio data. It should be noted that the value of the maximum delay time M is not unique, and may be appropriately adjusted according to the actual detection environment, for example, M may also be 0.02 × Fs × 3+2, as long as the requirement of the real-time performance of the electrocardiograph electrode drop detection can be met. When the autocorrelation processing is carried out, only the autocorrelation data when the maximum delay is reached is calculated by considering the real-time performance of the electrocardiosignal, and the accuracy of the electrode falling detection is effectively improved.
And step S320, periodically analyzing the electrocardio data according to the autocorrelation processing result to obtain a periodic analysis result. Specifically, referring to fig. 5, step S320 includes step S321 and step S322.
Step S321, judging whether the electrocardiogram data has an extreme point according to the autocorrelation processing result. When the electrocardio data is subjected to autocorrelation processing, the autocorrelation processing result is data with zero delay to M delay, whether extreme points exist in a sampling period is judged according to the processing result, the size of the sampling period is 0.02 x Fs because a signal detected by the falling detection is a 50Hz signal, and whether extreme points exist in all the sampling periods is judged according to the relation between the sampling period and the number of the calculated autocorrelation data. Further, in one embodiment, the maximum delay M is 0.02 × 500 × 2+2, 22, and the sampling period size is 0.02 × 500 for a sampling signal with a sampling rate of 500Hz, and at this time, the data of the autocorrelation result includes two sampling periods, so when determining the extreme point, it is only necessary to determine whether there is an extreme point in the electrocardiographic data in the two sampling periods. It should be noted that the number of sampling periods included in the autocorrelation processing result corresponds to the value of the maximum delay M, and is not necessarily two, and when determining whether there is an extremum, the determination should be performed according to the actual detection environment.
And step S322, when the extreme point exists, obtaining a periodic analysis result of the electrocardiogram data according to the extreme point and the autocorrelation data with zero delay in the autocorrelation processing result.
Specifically, when there is an extreme point, the extreme point is compared with autocorrelation data whose delay is zero at the time of autocorrelation processing, and it is determined whether or not the value of the extreme point is close to autocorrelation data whose delay is zero. When extreme points exist, the interval between each extreme point is equal and is equal to the number of sampling points corresponding to 50Hz, and the extreme points are close to the data with zero delay in the autocorrelation processing, the electrocardio data has periodicity. Further, when it is determined whether or not the value of the extreme point is close to the autocorrelation data with a delay of zero in the autocorrelation process, the value of the extreme point may be compared with 0.8 times the value of the data with a delay of zero in the autocorrelation process, and when the value of the extreme point is larger than the value of the data with a delay of zero in the autocorrelation process, the value of the extreme point is close to the value of the data with a delay of zero in the autocorrelation process, thereby detecting that the electrocardiographic data has periodicity. Note that, when determining whether or not the value of the extreme point is close to the data whose delay is zero in the autocorrelation processing, it is not necessary to compare the value of the extreme point with 0.8 times the value of the data whose delay is zero in the autocorrelation processing, and other multiples may be used as long as whether or not the value of the extreme point is close to the data whose delay is zero in the autocorrelation processing can be reasonably reflected, and the result of the periodic analysis can be obtained.
And S330, performing falling detection on the common-mode signal suppression electrode according to the periodic analysis result of the electrocardiogram data to obtain a detection result. Specifically, referring to fig. 5, in one embodiment, step S330 includes step S331.
In step S331, when the result of the periodic analysis of the electrocardiographic data has periodicity, the common mode signal electrode is inhibited from falling off. And performing autocorrelation processing on the electrocardiosignal according to the fact that the autocorrelation processing has the effect of detecting the periodicity of the signal, so as to judge whether the electrocardiosignal has periodicity according to an autocorrelation result, and when the electrocardiosignal has periodicity, judging that the detection result is to inhibit the falling of the common-mode signal electrode. It can be understood that when the cardiac signal has no periodicity, the detection result is that the common mode signal electrode is not dropped. Whether the common mode signal suppression electrode falls off is judged by whether the common mode signal suppression electrode has periodicity, a hardware circuit does not need to be operated, and the method has high safety and operation convenience.
When the maximum delay M is 0.02 × Fs × 2+2 and it is determined whether the maximum delay M is close to 0.8 times of the autocorrelation data whose delay is zero, the above steps S322 and S331 may be implemented by using the following algorithm:
here, the cycleSample indicates the number of sampling points at a 50Hz frequency within an acquisition time of 1 second, and takes a sampling rate of 500Hz as an example, where the number of sampling points cycleSample is (1/50) × 500 ═ 10, and data (0) indicates autocorrelation data in which the delay is zero during autocorrelation processing.
According to the electrocardioelectrode falling detection method, whether the electrocardioelectrode falls off or not is judged directly by analyzing the obtained electrocardio data, a hardware circuit is not required to be adjusted, the electrocardioelectrode falling detection method is not influenced by environmental factors and tester factors, and the accuracy of electrocardioelectrode falling detection is effectively improved.
In order to facilitate an understanding of the invention, the following detailed description is given in conjunction with specific examples.
In this embodiment, the electrocardiograph electrodes adopt an I-lead connection method, that is, the LA electrode and the RA electrode collect differential signals, and the LL electrode suppresses common mode signals. Referring to fig. 11-13, fig. 11 is a schematic diagram of a normal electrocardiogram in an embodiment, and fig. 12 is a schematic diagram of an electrocardiogram when the LA electrode and the RA electrode fall off in an embodiment, which shows that there is a great difference between the dispersion degree of the electrocardiogram data when the normal electrocardiogram and the LA electrode and the RA electrode fall off, so that a method for detecting the dispersion degree of the electrocardiogram data is used to detect whether the LA electrode and the RA electrode fall off; fig. 13 is an electrocardiographic schematic diagram of the LL electrode falling in the embodiment, and whether the LL electrode falls is detected by a method for detecting periodicity of electrocardiographic data. Specifically, the method includes the steps of obtaining one-second electrocardiographic data in a buffer area, calculating a standard deviation of the obtained electrocardiographic data, comparing the calculated standard deviation with a preset standard deviation threshold, wherein the preset standard deviation threshold is 0.02, when the calculated standard deviation is smaller than the preset standard deviation threshold, falling off of an LA electrode and an RA electrode, meanwhile, calculating a ratio of the number of adjacent point data of the electrocardiographic data equal to the number of electrocardiographic data in one second, comparing the obtained ratio with a preset continuous data ratio threshold, when the obtained ratio is larger than the preset continuous data ratio threshold, falling off of the LA electrode and the RA electrode, and simultaneously detecting falling off of the LA electrode and the RA electrode, wherein one of the two conditions is satisfied, namely, the falling off of the LA electrode and the RA electrode is explained, and the method can be specifically realized through the following algorithm:
since the autocorrelation processing has a function of detecting the periodicity, the periodicity is detected by performing autocorrelation processing on the electrocardiographic data. Referring to fig. 14-15, fig. 14 is a schematic diagram illustrating a result of autocorrelation processing of a normal electrocardiographic signal in an embodiment, and fig. 15 is a schematic diagram illustrating a result of autocorrelation processing of a LL-electrode-off electrocardiographic signal in an embodiment. Specifically, the sampling rate is 500Hz, and in consideration of the real-time performance of the electrocardiographic data, when the electrocardiographic data is subjected to autocorrelation processing within one second of the buffer, only the data up to the maximum delay M is calculated, where M is 0.02 × 500 × 2+2 is 22, and the sampling period is 0.02 × 500 is 10, and first, it is detected whether an extreme point exists in the autocorrelation processing result within the sampling period; when an extreme point exists, further judging whether the value of the extreme point is close to autocorrelation data with zero delay in an autocorrelation processing result, specifically comparing the value of the extreme point with 0.8 time of the autocorrelation data with zero delay in the autocorrelation processing result, and when the value of the extreme point is greater than 0.8 time of the autocorrelation data with zero delay in the autocorrelation processing result, approaching the value of the extreme point to the autocorrelation data with zero delay in the autocorrelation processing result, further obtaining that the electrocardiogram data has periodicity; when the electrocardiographic data has periodicity, the LL electrode falls off, otherwise, the electrocardiographic data does not have periodicity, and the LL electrode does not fall off, which can be specifically realized by the following algorithm:
referring to fig. 6, an electrocardiograph electrode fall-off detection apparatus includes an electrocardiograph data acquisition module 100, a first electrode fall-off detection module 200, and a second electrode fall-off detection module 300.
The electrocardiographic data acquisition module 100 acquires electrocardiographic data based on the differential signal acquired by the differential signal acquisition electrode. Specifically, two electrodes are used to collect differential signals of a human body, and the positions collected by the electrodes for collecting the differential signals are different in different lead connection methods. Taking the I-lead connection as an example, the differential signal acquisition electrodes are LA electrodes and RA electrodes. Because the electrocardiosignals are very small, the noise in the environment is possibly higher than the electrocardiosignals, and because the differential signals have stronger anti-interference capability, the interference of the environmental noise on the electrocardiosignals can be effectively inhibited by adopting the differential signals. Further, when acquiring the electrocardiogram data, the electrocardiogram data of the buffer area acquired by the acquisition card can be directly acquired, the length of the data is not unique, the electrocardiogram data stored in the buffer area within one second can be acquired, the electrocardiogram data of other time lengths can also be acquired, and the falling detection of the subsequent steps can be carried out according to the acquired electrocardiogram data.
The first electrode falling detection module 200 detects whether the differential signal acquisition electrode falls off by detecting the discrete degree of the electrocardiographic data. Specifically, when the differential signal collecting electrode falls off, the electrocardiographic data of the living body cannot be collected, so that the obtained electrocardiogram is a straight line, and all the data are the same in size. The electrocardiogram under the normal state and the electrocardiogram when the differential signal acquisition electrode falls off are obviously different. Therefore, whether the differential signal acquisition electrode falls off or not can be judged by directly detecting the discrete degree of the electrocardio data.
Further, the manner of detecting the degree of dispersion of the electrocardiographic data is not unique. Referring to fig. 7, in one embodiment, the first electrode-drop detecting module 200 includes a standard deviation calculating unit 210 and a first comparing unit 220. The standard deviation calculating unit 210 calculates a standard deviation of the electrocardiographic data. Specifically, when calculating the standard deviation of the electrocardiographic data, according to the formula:
wherein xiThe standard deviation represents the ith electrocardiogram data, sigma represents the standard deviation, N represents the number of the acquired electrocardiogram data, and u represents the average number of the acquired electrocardiogram data.
The first comparing unit 220 compares the standard deviation with a preset standard deviation threshold, and when the standard deviation is smaller than the preset standard deviation threshold, the differential signal collecting electrode falls off. And comparing the standard deviation of the acquired differential signals obtained by calculation with a preset standard deviation, and when the standard deviation obtained by calculation is smaller than a preset standard deviation threshold value, the differential signal acquisition electrode falls off. Whether the differential signal acquisition electrode falls off or not can be judged by calculating the standard deviation of the electrocardio data and comparing the standard deviation with a preset standard deviation threshold value, a hardware circuit does not need to be operated, and the method has strong operation convenience. Here, the size of the preset standard deviation threshold is 0.02, and it should be noted that the value of the preset standard deviation threshold is not unique as long as the degree of dispersion of the data can be reasonably represented.
Referring to fig. 8, in another embodiment, the first electrode-drop detecting module 200 includes a ratio calculating unit 230 and a second comparing unit 240.
The ratio calculating unit 230 obtains the ratio of the number of the adjacent point data being equal to the number of the electrocardiographic data by calculation. Specifically, taking a set of data 1, 1, 2, 2, 3, 4, 5, 5, 6 as an example, where the number of the adjacent point data equals to 3, and the total data number equals to 9, the ratio of the number of the adjacent point data equals to the total data number is 3/9. Based on the method, the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data is calculated.
The second comparing unit 240 compares the ratio with a preset continuous data ratio threshold, and when the ratio is greater than the continuous data ratio threshold, the differential signal collecting electrode falls off. Comparing the ratio of the number of the adjacent point data obtained by calculation to the number of the electrocardiographic data with a preset continuous data ratio threshold, wherein the size of the continuous data ratio threshold is 0.5, and it should be noted that the size of the continuous data ratio threshold is not unique as long as the dispersion degree of the electrocardiographic data can be effectively reflected; and when the ratio of the number of the adjacent point data equal to the number of the electrocardio data obtained by calculation to the number of the electrocardio data is larger than the preset continuous data ratio, the differential signal acquisition electrode falls off. The method is used for detecting the falling of the differential signal falling electrode, a hardware circuit is not required to be operated, the influence of environmental factors is avoided, and the method has strong operation convenience.
The second electrode falling detection module 300 detects whether the common mode signal suppression electrode falls off by detecting the periodicity of the electrocardiographic data. Specifically, suppressing the common mode signal electrode from falling off makes the circuit for acquiring the bioelectric cardiac signal unable to form a loop, and thus there is no output of the common mode suppression signal. Because the voltage amplitude of the common-mode signal is far greater than that of the electrocardiosignal, the shape and the frequency domain of the electrocardiosignal are close to the common-mode signal, and the common-mode signal is mainly subjected to 50Hz power frequency interference in the electrocardio detection process, so that whether the electrode for inhibiting the common-mode signal falls off or not can be judged by detecting the periodicity of the signal. Similarly, taking the I-lead connection as an example, the suppression common mode signal electrode is an LL electrode, and by periodically detecting the cardiac signal, it can be determined whether the LL electrode is detached.
Referring to fig. 9, in one embodiment, the second electrode fall-off detection module 300 includes an autocorrelation processing module 310, a periodicity analysis module 320, and a fall-off detection module 330.
The autocorrelation processing module 310 performs autocorrelation processing on the electrocardiographic data to obtain an autocorrelation processing result. Specifically, in one embodiment, the electrocardiographic data subjected to the autocorrelation processing is buffer electrocardiographic data within one second, and in consideration of real-time performance of electrode drop detection, when the electrocardiographic data is subjected to the autocorrelation processing, only the electrocardiographic data until the maximum delay M is calculated, where M is 0.02 Fs 2+2, where Fs is a sampling rate, and a calculation formula of the autocorrelation processing is as follows:
wherein, M is 0, 1, 2 … M, dataBuff is the one-second buffer electrocardio data, and N is the number of the one-second buffer electrocardio data. It should be noted that the value of the maximum delay time M is not unique, and may be appropriately adjusted according to the actual detection environment, for example, M may also be 0.02 × Fs × 3+2, as long as the requirement of the real-time performance of the electrocardiograph electrode drop detection can be met. When the autocorrelation processing is carried out, only the autocorrelation data when the maximum delay is reached is calculated by considering the real-time performance of the electrocardiosignal, and the accuracy of the electrode falling detection is effectively improved.
The periodicity analysis module 320 performs periodicity analysis on the electrocardiographic data according to the autocorrelation processing result to obtain a periodicity analysis result. Specifically, referring to fig. 10, the periodicity analysis module 320 includes an extremum detecting unit 321 and an extremum comparing unit 322.
The extreme value detecting unit 321 determines whether the electrocardiographic data has an extreme value point based on the autocorrelation processing result. When the electrocardio data is subjected to autocorrelation processing, the autocorrelation processing result is data with zero delay to M delay, whether extreme points exist in a sampling period is judged according to the processing result, the size of the sampling period is 0.02 x Fs because a signal detected by the falling detection is a 50Hz signal, and whether extreme points exist in all the sampling periods is judged according to the relation between the sampling period and the number of the calculated autocorrelation data. Further, in one embodiment, the maximum delay M is 0.02 × 500 × 2+2, 22, and the sampling period size is 0.02 × 500 for a sampling signal with a sampling rate of 500Hz, and at this time, the data of the autocorrelation result includes two sampling periods, so when determining the extreme point, it is only necessary to determine whether there is an extreme point in the electrocardiographic data in the two sampling periods. It should be noted that the number of sampling periods included in the autocorrelation processing result corresponds to the value of the maximum delay M, and is not necessarily two, and when determining whether there is an extremum, the determination should be performed according to the actual detection environment.
The extremum comparing unit 322 obtains a periodic analysis result of the electrocardiographic data according to the extremum point and the autocorrelation data with zero delay in the autocorrelation processing result when the extremum point exists. Specifically, when there is an extreme point, the extreme point is compared with autocorrelation data whose delay is zero at the time of autocorrelation processing, and it is determined whether or not the value of the extreme point is close to autocorrelation data whose delay is zero. When extreme points exist, the interval between each extreme point is equal and is equal to the number of sampling points corresponding to 50Hz, and the extreme points are close to the data with zero delay in the autocorrelation processing, the electrocardio data has periodicity. Further, when it is determined whether or not the value of the extreme point is close to the autocorrelation data with a delay of zero in the autocorrelation process, the value of the extreme point may be compared with 0.8 times the value of the data with a delay of zero in the autocorrelation process, and when the value of the extreme point is larger than the value of the data with a delay of zero in the autocorrelation process, the value of the extreme point is close to the value of the data with a delay of zero in the autocorrelation process, thereby detecting that the electrocardiographic data has periodicity. Note that, when determining whether or not the value of the extreme point is close to the data whose delay is zero in the autocorrelation processing, it is not necessary to compare the value of the extreme point with 0.8 times the value of the data whose delay is zero in the autocorrelation processing, and other multiples may be used as long as whether or not the value of the extreme point is close to the data whose delay is zero in the autocorrelation processing can be reasonably reflected, and the result of the periodic analysis can be obtained.
And the falling detection module 330 is used for detecting falling of the common mode signal suppression electrode according to the periodic analysis result of the electrocardiogram data to obtain a detection result. Specifically, referring to fig. 10, in one embodiment, the fall-off detection module 330 includes a fall-off determination unit 331.
The drop determination unit 331 suppresses the drop of the common mode signal electrode when the result of the periodic analysis of the electrocardiographic data is periodic. And performing autocorrelation processing on the electrocardiosignal according to the fact that the autocorrelation processing has the effect of detecting the periodicity of the signal, so as to judge whether the electrocardiosignal has periodicity according to an autocorrelation result, and when the electrocardiosignal has periodicity, judging that the detection result is to inhibit the falling of the common-mode signal electrode. It can be understood that when the cardiac signal has no periodicity, the detection result is that the common mode signal electrode is not dropped. Whether the common mode signal suppression electrode falls off is judged by whether the common mode signal suppression electrode has periodicity, a hardware circuit does not need to be operated, and the method has high safety and operation convenience.
According to the electrocardioelectrode falling detection device, whether the electrocardioelectrode falls off or not is judged directly by analyzing the obtained electrocardio data, a hardware circuit is not required to be adjusted, the electrocardioelectrode falling detection device is not influenced by environmental factors and tester factors, and the accuracy of electrocardioelectrode falling detection is effectively improved.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 16. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the electrocardiogram data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize the electrocardio-electrode falling detection method.
Those skilled in the art will appreciate that the architecture shown in fig. 16 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring electrocardio data according to the differential signals acquired by the differential signal acquisition electrodes;
detecting whether the differential signal acquisition electrode falls off or not by detecting the dispersion degree of the electrocardio data;
and detecting whether the common mode signal suppression electrode falls off or not by detecting the periodicity of the electrocardio data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
calculating to obtain standard deviation of the electrocardio data;
and comparing the standard deviation with a preset standard deviation threshold, and when the standard deviation is smaller than the preset standard deviation threshold, dropping the differential signal acquisition electrode.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
calculating to obtain the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data;
and comparing the ratio with a preset continuous data ratio threshold, and when the ratio is greater than the continuous data ratio threshold, dropping the differential signal acquisition electrode.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
carrying out autocorrelation processing on the electrocardiogram data to obtain an autocorrelation processing result;
periodically analyzing the electrocardiogram data according to the autocorrelation processing result to obtain a periodic analysis result;
and (4) carrying out falling detection on the common mode signal suppression electrode according to the periodic analysis result of the electrocardio data to obtain a detection result.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
judging whether the electrocardiogram data has an extreme point or not according to the autocorrelation processing result;
when the extreme point exists, obtaining a periodic analysis result of the electrocardiogram data according to the extreme point and the autocorrelation data with zero delay in the autocorrelation processing result;
and when the result of the periodic analysis of the electrocardiogram data is periodic, the common-mode signal electrode is restrained from falling off.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring electrocardio data according to the differential signals acquired by the differential signal acquisition electrodes;
detecting whether the differential signal acquisition electrode falls off or not by detecting the dispersion degree of the electrocardio data;
and detecting whether the common mode signal suppression electrode falls off or not by detecting the periodicity of the electrocardio data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating to obtain standard deviation of the electrocardio data;
and comparing the standard deviation with a preset standard deviation threshold, and when the standard deviation is smaller than the preset standard deviation threshold, dropping the differential signal acquisition electrode.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating to obtain the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data;
and comparing the ratio with a preset continuous data ratio threshold, and when the ratio is greater than the continuous data ratio threshold, dropping the differential signal acquisition electrode.
In one embodiment, the computer program when executed by the processor further performs the steps of:
carrying out autocorrelation processing on the electrocardiogram data to obtain an autocorrelation processing result;
periodically analyzing the electrocardiogram data according to the autocorrelation processing result to obtain a periodic analysis result;
and (4) carrying out falling detection on the common mode signal suppression electrode according to the periodic analysis result of the electrocardio data to obtain a detection result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
judging whether the electrocardiogram data has an extreme point or not according to the autocorrelation processing result;
when the extreme point exists, obtaining a periodic analysis result of the electrocardiogram data according to the extreme point and the autocorrelation data with zero delay in the autocorrelation processing result;
and when the result of the periodic analysis of the electrocardiogram data is periodic, the common-mode signal electrode is restrained from falling off.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The computer equipment and the storage medium directly analyze the acquired electrocardio data to judge whether the electrocardio electrode falls off, do not need to adjust a hardware circuit, are not influenced by environmental factors and tester factors, and effectively improve the accuracy of the detection of the fall-off of the electrocardio electrode.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (12)
1. An electrocardio-electrode falling-off detection method is characterized by comprising the following steps:
acquiring electrocardio data according to the differential signals acquired by the differential signal acquisition electrodes;
detecting whether the differential signal acquisition electrode falls off or not by detecting the discrete degree of the electrocardio data;
carrying out autocorrelation processing on the electrocardiogram data to obtain an autocorrelation processing result; periodically analyzing the electrocardio data according to the autocorrelation processing result to obtain a periodic analysis result; and performing drop detection on the common-mode signal suppression electrode according to the periodic analysis result of the electrocardiogram data to obtain a detection result.
2. The method for detecting the falling of the electrocardio-electrode according to claim 1, wherein the step of detecting whether the differential signal acquisition electrode falls off by detecting the discrete degree of the electrocardio-data comprises the following steps:
calculating to obtain the standard deviation of the electrocardio data;
and comparing the standard deviation with a preset standard deviation threshold, and when the standard deviation is smaller than the preset standard deviation threshold, falling off the differential signal acquisition electrode.
3. The method for detecting the falling of the electrocardio-electrode according to claim 1, wherein the step of detecting whether the differential signal acquisition electrode falls off by detecting the discrete degree of the electrocardio-data comprises the following steps:
calculating to obtain the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data;
and comparing the ratio with a preset continuous data ratio threshold, and when the ratio is greater than the continuous data ratio threshold, dropping the differential signal acquisition electrode.
4. The method for detecting the falling off of the electrocardio-electrode according to claim 1, wherein the step of periodically analyzing the electrocardio-data according to the autocorrelation processing result to obtain a periodic analysis result comprises the following steps:
judging whether the electrocardiogram data has an extreme point or not according to the autocorrelation processing result;
and when the extreme point exists, obtaining a periodic analysis result of the electrocardiogram data according to the extreme point and the autocorrelation data with zero delay in the autocorrelation processing result.
5. The method for detecting the falling of the electrocardio-electrode according to claim 1, wherein the step of detecting the falling of the common mode signal suppression electrode according to the periodic analysis result of the electrocardio-data to obtain a detection result comprises the following steps:
and when the result of the periodic analysis of the electrocardio data is periodic, the common-mode signal electrode is suppressed to fall off.
6. An electrocardio-electrode fall-off detection device, characterized in that the device comprises:
the electrocardio data acquisition module is used for acquiring electrocardio data according to the differential signal acquired by the differential signal acquisition electrode;
the first electrode falling detection module is used for detecting whether the differential signal acquisition electrode falls off or not by detecting the dispersion degree of the electrocardio data;
the second electrode falling detection module comprises an autocorrelation processing module, a periodic analysis module and a falling detection module; the autocorrelation processing module is used for carrying out autocorrelation processing on the electrocardio data to obtain an autocorrelation processing result; the periodicity analysis module is used for periodically analyzing the electrocardio data according to the autocorrelation processing result to obtain a periodicity analysis result; and the falling detection module is used for detecting the falling of the common mode signal suppression electrode according to the periodic analysis result of the electrocardio data to obtain a detection result.
7. The apparatus according to claim 6, wherein the first electrode falling detection module comprises:
the standard deviation calculation unit is used for obtaining the standard deviation of the electrocardio data through calculation;
and the first comparison unit is used for comparing the standard deviation with a preset standard deviation threshold value, and when the standard deviation is smaller than the preset standard deviation threshold value, the differential signal acquisition electrode falls off.
8. The apparatus according to claim 6, wherein the first electrode falling detection module comprises:
the ratio calculation unit is used for calculating the ratio of the number of the adjacent point data equal to the number of the electrocardiogram data;
and the second comparison unit is used for comparing the ratio with a preset continuous data ratio threshold value, and when the ratio is greater than the continuous data ratio threshold value, the differential signal acquisition electrode falls off.
9. The apparatus according to claim 6, wherein the periodicity analysis module comprises:
the extreme value detection unit is used for judging whether the electrocardiogram data has an extreme value point or not according to the autocorrelation processing result;
and the extreme value comparison unit is used for obtaining the periodic analysis result of the electrocardio data according to the extreme value point and the autocorrelation data with zero delay in the autocorrelation processing result when the extreme value point exists.
10. The apparatus according to claim 6, wherein the fall-off detection module comprises:
and the falling judgment unit is used for inhibiting the common mode signal electrode from falling off when the periodicity analysis result of the electrocardio data is periodicity.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-5 are implemented when the processor executes the program.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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| CN111836577B (en) * | 2020-02-03 | 2023-08-08 | 深圳市汇顶科技股份有限公司 | Contact state detection device and wearable equipment |
| CN111657931B (en) * | 2020-06-30 | 2023-06-06 | 康泰医学系统（秦皇岛）股份有限公司 | Method, device and storage medium for multiplexing electrocardio electrode into key |
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