R-wave detection and adaptive-based power frequency filtering method, device and storage medium

Abstract

The application discloses a power frequency filtering method and device based on R wave detection and self-adaption and a storage medium. The method comprises the following steps: inputting electrocardio data; performing QRS complex wave detection on the electrocardio data; judging whether the current electrocardio data is within the range of the QRS complex wave; if the current electrocardio data is within the range of the QRS complex wave, the weight parameter of the self-adaptive filter is not updated; if the current electrocardio data is not within the range of the QRS complex wave, the weight parameter of the self-adaptive filter is updated according to the error between the output of the self-adaptive filter and the expectation; and the electrocardio data is filtered to remove power frequency interference according to the weight parameter of the self-adaptive filter. The application can avoid the ringing effect, so that the ST segment analysis and arrhythmia analysis are not affected by the power frequency filtering, and the waveform characteristics can be retained to a large extent.

Description

Technical Field

[0001] This application relates to the field of adaptive filtering, and in particular to power frequency filtering methods, devices, and storage media based on R-wave detection and adaptation. Background Technology

[0002] In most medical equipment operating environments, power frequency interference is unavoidable. Power frequency interference can affect electrocardiogram monitoring and diagnostic analysis, therefore it needs to be filtered out.

[0003] However, when using minimum mean square error adaptive filtering to remove power frequency interference, the QRS complex wave will exhibit a ringing effect at the tail of the QRS wave after passing through the filter, which will cause errors in arrhythmia analysis and ST value calculation.

[0004] Therefore, the aforementioned technical problems in the relevant technologies urgently need to be solved. Summary of the Invention

[0005] This application aims to solve one of the technical problems in related technologies. To this end, embodiments of this application provide a power frequency filtering method, apparatus, and storage medium based on R-wave detection and adaptation, which can adaptively filter and remove power frequency interference.

[0006] According to one aspect of the embodiments of this application, a power frequency filtering method based on R-wave detection and adaptation is provided, the method comprising:

[0007] Enter ECG data;

[0008] The electrocardiogram data were subjected to QRS complex detection;

[0009] Determine whether the current ECG data is within the range of the QRS complex. If the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated. If the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output of the adaptive filter and the expectation.

[0010] The ECG data is filtered to remove power frequency interference based on the weight parameters of the adaptive filter.

[0011] In one embodiment, the input electrocardiogram data includes:

[0012] After acquiring the electrocardiogram (ECG) voltage signal, the ECG voltage signal is converted from analog to digital to obtain a digital signal with a fixed sampling rate.

[0013] In one embodiment, after inputting ECG data, the method further includes:

[0014] The electrocardiogram data are processed by difference, sum of squares and integration.

[0015] In one embodiment, the QRS complex detection of the electrocardiogram data includes:

[0016] The R peak, the start point of the QRS wave, and the end point of the QRS wave are located in the electrocardiogram data.

[0017] In one embodiment, after filtering out power line interference from the ECG data according to the weight parameters of the adaptive filter, the method further includes:

[0018] Once all ECG data has been filtered, the program terminates.

[0019] If there is unfiltered ECG data, the power frequency interference filtering process will be repeated.

[0020] In one embodiment, the expression for the electrocardiogram data is:

[0021] w i+1 =w i +2·μ·e i ·n′ i

[0022] In the formula, w i+1 w is the weight vector corresponding to the ECG data of the (i+1)th sampling point. i Let μ be the weight vector corresponding to the ECG data of the i-th sampling point, and e be the gradient descent coefficient. i Let n′ be the error between the expected value and the system output. i This is power frequency interference.

[0023] In one embodiment, the output of the adaptive filter continuously approximates the actual power frequency interference, and the error between the expected value and the system output continuously approximates the actual clean signal. The output formula of the adaptive filter is:

[0024] y i =w i T ·n′ i

[0025] In the formula, y i For the output of the adaptive filter, w i T Let n′ be the transpose of the weight vector corresponding to the ECG data of the i-th sampling point. i This is power frequency interference.

[0026] According to one aspect of an embodiment of this application, a power frequency filtering device based on R-wave detection and adaptation is provided, the device comprising:

[0027] The first module is used to input ECG data;

[0028] The second module is used to detect QRS complex waves in the electrocardiogram data;

[0029] The third module is used to determine whether the current ECG data is within the range of the QRS complex. If the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated. If the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output of the adaptive filter and the expectation.

[0030] The fourth module is used to filter out power frequency interference from the electrocardiogram data according to the weight parameters of the adaptive filter.

[0031] According to one aspect of an embodiment of this application, a power frequency filtering device based on R-wave detection and adaptation is provided, the device comprising:

[0032] At least one processor;

[0033] At least one memory for storing at least one program;

[0034] When at least one of the programs is executed by at least one of the processors, the power frequency filtering method based on R-wave detection and adaptation as described in the preceding embodiments is implemented.

[0035] According to one aspect of the embodiments of this application, a storage medium is provided, the storage medium storing a processor-executable program, which, when executed by a processor, implements the power frequency filtering method based on R-wave detection and adaptation as described in the preceding embodiments.

[0036] The beneficial effects of the power frequency filtering method, apparatus, and storage medium based on R-wave detection and adaptive filtering provided in this application are as follows: The method includes inputting electrocardiogram (ECG) data; performing QRS complex detection on the ECG data; determining whether the current ECG data is within the range of the QRS complex; if the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated; if the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output and the expected value of the adaptive filter; and filtering out power frequency interference from the ECG data according to the weight parameters of the adaptive filter. This application can avoid the ringing effect, so that ST segment analysis and arrhythmia analysis are not affected by power frequency filtering, and can preserve waveform characteristics to a large extent.

[0037] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the power frequency filtering method based on R-wave detection and adaptation provided in this application embodiment;

[0040] Figure 2 A flowchart illustrating an embodiment of the power frequency filtering method based on R-wave detection and adaptation provided in this application.

[0041] Figure 3 Waveform diagrams of experimental results provided for embodiments of this application;

[0042] Figure 4 A schematic diagram of a power frequency filtering device based on R-wave detection and adaptation provided in an embodiment of this application;

[0043] Figure 5 A schematic diagram of another power frequency filtering device based on R-wave detection and adaptation provided in an embodiment of this application. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0045] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0047] In most medical equipment operating environments, power frequency interference is unavoidable. Power frequency interference can affect electrocardiogram monitoring and diagnostic analysis, therefore it needs to be filtered out.

[0048] However, when using minimum mean square error adaptive filtering to remove power frequency interference, the QRS complex wave will exhibit a ringing effect at the tail of the QRS wave after passing through the filter, which will cause errors in arrhythmia analysis and ST value calculation.

[0049] To address the aforementioned issues, this application proposes a power frequency filtering method based on a combination of QRS wave detection and adaptive filtering. This method can resolve the problems of ringing effect and slow response speed in ECG signals when digital filters or adaptive filters perform power frequency filtering, thereby avoiding the impact of ringing effect on ECG ST segment and arrhythmia analysis and calculation.

[0050] Figure 1 A flowchart of the power frequency filtering method based on R-wave detection and adaptation provided in the embodiments of this application is shown below. Figure 1 As shown, the power frequency filtering method based on R-wave detection and adaptation proposed in this application includes:

[0051] S101, Input ECG data;

[0052] S102. Perform QRS complex detection on the electrocardiogram data;

[0053] S103. Determine whether the current ECG data is within the range of the QRS complex.

[0054] S104. If the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated.

[0055] S105. If the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output of the adaptive filter and the expectation.

[0056] S106. Filter out power frequency interference from the electrocardiogram data according to the weight parameters of the adaptive filter.

[0057] Specifically, the input ECG data in step S101 includes: acquiring the ECG voltage signal, and then converting the ECG voltage signal through analog-to-digital conversion to obtain a digital signal with a fixed sampling rate. After inputting the ECG data, the method further includes: performing differential, squared, and integral processing on the ECG data. After differential processing, the data is squared, and then subjected to a sliding integral of Fs / 7 points to obtain d′. i .

[0058] Specifically, step S102, which involves detecting the QRS complex in the ECG data, includes: locating the R peak, the start point of the QRS wave, and the end point of the QRS wave in the ECG data. If it is in the rising phase, the maximum value maxVal and the position of the maximum value maxValPos are saved; when d′ i When the distance from the current point to the maximum value of the R peak is less than 170ms (thr + maxVal) / 2, the QRS wave is confirmed to be found. The maximum value of the R peak is maxValPos. Let thr = 5mV.

[0059] In addition, after filtering out power frequency interference from the ECG data according to the weight parameters of the adaptive filter, the method further includes: if all ECG data filtering is completed, the program ends; if there is ECG data that has not been filtered, the power frequency interference filtering is repeated.

[0060] In this embodiment, the expression for the electrocardiogram data is:

[0061] w i+1 =w i +2·μ·e i ·n′ i

[0062] In the formula, w i+1 w is the weight vector corresponding to the ECG data of the (i+1)th sampling point. i Let μ be the weight vector corresponding to the ECG data of the i-th sampling point, and e be the gradient descent coefficient. i Let n′ be the error between the expected value and the system output. i This is power frequency interference.

[0063] It should be noted that the output of the adaptive filter continuously approximates the actual power frequency interference, and the error between the expected value and the system output continuously approximates the actual clean signal. The output formula of the adaptive filter is:

[0064] y i =w i T ·n′ i

[0065] In the formula, y iFor the output of the adaptive filter, w i T Let n′ be the transpose of the weight vector corresponding to the ECG data of the i-th sampling point. i This is power frequency interference.

[0066] Figure 2 A flowchart illustrating an embodiment of the power frequency filtering method based on R-wave detection and adaptation provided in this application is shown below. Figure 2 As shown, the specific implementation steps of the power frequency filtering method based on R-wave detection and adaptation provided in this application include:

[0067] A. Input ECG data: ECG voltage signals are collected from the human body and converted into digital signals with a fixed sampling rate through analog-to-digital conversion;

[0068] B. ECG signal preprocessing: The ECG signal is processed by differential, sum of squares, and integral.

[0069] C. QRS composite wave detection: For the preprocessed data, find the R peak, as well as the start and end points of the QRS wave;

[0070] D. Whether it is within the QRS wave range: Determine whether the current data point is within the QRS wave range;

[0071] E. Do not update adaptive filter parameters: If the current data is within the range of the QRS wave, then update the weight parameters of the adaptive filter.

[0072] F. Update adaptive filter parameters: If the current data is not within the range of the QRS wave, update the weight parameters of the adaptive filter based on the error between the filter output and the expectation.

[0073] G. Adaptive Filtering: Use the latest adaptive filter weight parameters to filter out power frequency interference from the preprocessed signal;

[0074] H. ECG data filtering completion judgment: If all data filtering is completed, the program ends; otherwise, repeat steps A to H.

[0075] Figure 3 The experimental result waveforms provided in the embodiments of this application are as follows: Figure 3As shown, from top to bottom, the waveforms are: original waveform, original waveform with added power frequency interference, minimum mean square error adaptive filtering, and minimum mean square error adaptive filtering based on R-wave detection. The waveform diagrams show that adding 50Hz power frequency interference and applying minimum mean square error adaptive filtering results in a ringing effect at the tail of the QRS complex, significantly impacting ST segment analysis and arrhythmia diagnosis. Minimum mean square error adaptive filtering combined with QRS detection filters the waveform with added power frequency interference, avoiding ringing and preventing impact on ST segment and arrhythmia analysis.

[0076] This application can avoid the ringing effect, so that ST segment analysis and arrhythmia analysis are not affected by power frequency filtering, and can preserve waveform characteristics to a large extent.

[0077] Furthermore, this application also proposes a power frequency filtering device based on R-wave detection and adaptation, such as... Figure 4 As shown, the device includes:

[0078] The first module, 401, is used to input electrocardiogram (ECG) data.

[0079] The second module 402 is used to perform QRS complex wave detection on the electrocardiogram data;

[0080] The third module 403 is used to determine whether the current ECG data is within the range of the QRS complex. If the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated. If the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output of the adaptive filter and the expectation.

[0081] The fourth module 404 is used to filter out power frequency interference from the electrocardiogram data according to the weight parameters of the adaptive filter.

[0082] Furthermore, this application also proposes a power frequency filtering device based on R-wave detection and adaptation, such as... Figure 5 As shown, the device includes:

[0083] At least one processor 501;

[0084] At least one memory 502, the memory 502 being used to store at least one program;

[0085] When at least one of the programs is executed by at least one of the processors 501, the power frequency filtering method based on R-wave detection and adaptation as described in the preceding embodiments is implemented.

[0086] Furthermore, this application also proposes a storage medium storing a processor-executable program that, when executed by a processor, implements the power frequency filtering method based on R-wave detection and adaptation as described in the preceding embodiments.

[0087] Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0088] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0089] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional technology for an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0090] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0092] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0093] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0094] In the foregoing description of this specification, the references to terms such as "one embodiment," "another embodiment," or "some embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0095] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0096] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A power frequency filtering method based on R-wave detection and adaptive filtering, characterized in that, The method includes: Enter ECG data; The electrocardiogram data were processed by difference, sum of squares and integration; The electrocardiogram data were subjected to QRS complex detection; Determine whether the current ECG data is within the range of the QRS complex. If the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated. If the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output of the adaptive filter and the expectation. The ECG data is filtered to remove power frequency interference based on the weight parameters of the adaptive filter. The step of detecting QRS complex waves in the electrocardiogram data includes: Find the R peak, the start point of the QRS wave, and the end point of the QRS wave in the ECG data; save the maximum value maxVal and the position of the maximum value maxValPos; when When the distance from the current point to the maximum value of the R peak is less than (thr + maxVal) / 2 and the distance from the current point to the maximum value of the R peak is less than 170ms, the QRS wave is confirmed to be found. The maximum value of the R peak is maxValPos, where thr = 5mV.

2. The power frequency filtering method based on R-wave detection and adaptation according to claim 1, characterized in that, The input electrocardiogram data includes: After acquiring the electrocardiogram (ECG) voltage signal, the ECG voltage signal is converted from analog to digital to obtain a digital signal with a fixed sampling rate.

3. The power frequency filtering method based on R-wave detection and adaptation according to claim 1, characterized in that, After filtering out power line interference from the ECG data according to the weight parameters of the adaptive filter, the method further includes: Once all ECG data has been filtered, the program terminates. If there is unfiltered ECG data, the power frequency interference filtering process will be repeated.

4. The power frequency filtering method based on R-wave detection and adaptation according to claim 1, characterized in that, The expression for the electrocardiogram data is: In the formula, Let i be the weight vector corresponding to the ECG data of the (i+1)th sampling point. Let be the weight vector corresponding to the ECG data of the i-th sampling point. This is the gradient descent coefficient. The error between the expected value and the system output, This is power frequency interference.

5. The power frequency filtering method based on R-wave detection and adaptation according to claim 4, characterized in that, The output of the adaptive filter continuously approximates the actual power frequency interference, and the error between the expected value and the system output continuously approximates the actual clean signal. The output formula of the adaptive filter is: In the formula, The output of the adaptive filter, This is the transpose of the weight vector corresponding to the ECG data of the i-th sampling point. This is power frequency interference.

6. A power frequency filtering device based on R-wave detection and adaptation, characterized in that, The device includes: The first module is used to input electrocardiogram (ECG) data and perform differential, square, and integral processing on the ECG data. The second module is used to detect QRS complex waves in the electrocardiogram data; The step of detecting QRS complex waves in the electrocardiogram data includes: Find the R peak, the start point of the QRS wave, and the end point of the QRS wave in the ECG data; save the maximum value maxVal and the position of the maximum value maxValPos; when When the distance from the current point to the maximum value of the R-wave is less than (thr + maxVal) / 2 and the distance from the current point to the maximum value of the R-wave is less than 170ms, the QRS wave is confirmed to be found. The maximum value of the R-wave is the position of the R-wave peak, where thr = 5mV. The third module is used to determine whether the current ECG data is within the range of the QRS complex. If the current ECG data is within the range of the QRS complex, the weight parameters of the adaptive filter are not updated. If the current ECG data is not within the range of the QRS complex, the weight parameters of the adaptive filter are updated according to the error between the output of the adaptive filter and the expectation. The fourth module is used to filter out power frequency interference from the electrocardiogram data according to the weight parameters of the adaptive filter.

7. A power frequency filtering device based on R-wave detection and adaptation, characterized in that, The device includes: At least one processor; At least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the power frequency filtering method based on R-wave detection and adaptation as described in any one of claims 1-5 is implemented.

8. A storage medium, characterized in that, The storage medium stores a processor-executable program, which, when executed by the processor, implements the power frequency filtering method based on R-wave detection and adaptation as described in any one of claims 1-5.

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