A method for detecting multi-lead low sampling rate pacing pulses

Abstract

A method for detecting multi-lead low-sampling-rate pacing pulses includes the following steps: Step 1, filtering the electrocardiogram signal; Step 2, summing the absolute values ​​of the signals; Step 3, low-pass filtering; Step 4, detecting potential pacing pulses; and Step 5, screening pacing pulses. This invention enables low-memory and low-power pacing pulse detection while ensuring recognition accuracy.

Description

Technical Field

[0001] This invention relates to the field of electrocardiogram signal detection technology, and in particular to a method for detecting multi-lead low sampling rate pacing pulses. Background Technology

[0002] Cardiac pacemakers are widely used in clinical practice to effectively treat conditions including bradycardia, heart failure, atrioventricular block, and atrial fibrillation. With improved economic conditions and increased public awareness of health, more and more patients are having pacemakers implanted. After a period of time following pacemaker implantation, follow-up is necessary to determine if the pacemaker is functioning adequately for the patient. Currently, the clinical monitoring and evaluation of pacemaker status primarily relies on electrocardiogram (ECG) analysis.

[0003] According to the American National Standards Institute / Association for the Advancement of Medical Instrumentation (ANSI / AAM IEC 11) standard for diagnostic electrocardiogram (ECG) devices, the detected pacemaker pulse duration must be between 0.1 ms and 2 ms. The standard IEC 60601-2-27, "Medical Electrical Equipment—Part 2-27: Detailed Requirements for Basic Safety and Necessary Performance of ECG Monitoring Equipment," requires a pacemaker pulse duration of 0.5 ms to 2.0 ms. Since typical pacemaker pulse durations range from microseconds to milliseconds, a Nyquist frequency of at least 4 kHz is required to reliably capture the high-frequency content of the pacemaker pulse. Traditional pacemaker detection methods include hardware and software approaches. Hardware detection involves adding a dedicated pacemaker detection circuit, increasing hardware costs, and due to limitations in the detection principle, the accuracy of pacemaker pulse detection is relatively low. Software detection methods perform detection based on the morphology of the pacemaker pulse at a high sampling rate. This high sampling rate method requires a large amount of memory and also increases power consumption, affecting the device's battery life. Therefore, a high-precision, low-power detection method is needed for long-term operation in portable wearable ECG devices to monitor pacemaker functionality. Summary of the Invention

[0004] To address the shortcomings of the existing technology, this invention provides a multi-lead low sampling rate pacing pulse detection method that can identify pacing pulses with minimal resources while ensuring accuracy.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A method for detecting multi-lead low sampling rate pacing pulses includes the following steps:

[0007] 1) Signal filtering: High-pass filtering is performed on the raw ECG signals of each lead to remove power line interference, QRS wave, P wave, T wave ECG components and low-frequency noise, while retaining the pacing signal;

[0008] 2) Absolute value and superposition of signals: Take the absolute value of the filtered signals from each channel, and then superimpose the signals from each lead to further amplify the pacing pulse;

[0009] 3) Low-pass filtering: The superimposed signals are subjected to multi-stage FIR low-pass filtering to remove high-frequency noise;

[0010] 4) Potential pacing pulse detection: Based on the ECG signal after preprocessing in 3), find positive peak points; determine a threshold based on the detected positive peak points; find peak points exceeding the threshold based on the threshold, as potential pacing pulse position sequences, and denote paceCandi[k] as the k-th pacing pulse position, k = 1, 2, ..., P;

[0011] 5) Pacing pulse screening: The pacing pulses detected in step 4) are screened. Based on the amplitude, it is determined whether there are falsely detected pacing pulses and missed pacing pulses. If so, the missed pacing pulses are searched and the falsely detected pacing pulses are removed. It is also confirmed whether there are missed low-amplitude pacing pulses. If so, the missed pacing pulses are searched according to the backward search algorithm.

[0012] Furthermore, in step 1), the original ECG signals of each lead are subjected to high-order FIR high-pass filtering to remove power line interference, QRS wave, P wave, T wave ECG components and low-frequency noise, while retaining the pacing signal.

[0013] In step 2), the absolute value superposition of the high-pass filtered ECG signal includes:

[0014] y(n)=|y1(n)|+|y2(n-1)|+...+|y N (n)|

[0015] Where y(n) is the superimposed signal and N is the number of leads.

[0016] Furthermore, in step 4), the threshold Threshold is determined based on the positive peak point. The process is as follows: with the detected positive peak point as the center and W as the window length, the area of ​​the signal within the window region is calculated.

[0017]

[0018] Where K represents the set coefficient.

[0019] Furthermore, in step 4), peak points exceeding the threshold are identified as potential pacing pulse position sequences. The process is as follows: Starting from the i-th peak point, with the current detection point p(i) as the center, the signal area areaVal within the region [i-0.25W, i+0.25W] is calculated. This area areaVal is compared with the threshold Threshold. If it exceeds half of the threshold, the peak point is recorded as the potential pacing pulse position paceCandi[k], where k is the k-th potential pacing pulse detected. The process continues to detect the next peak point until all peak points have been detected.

[0020] Further, in step 5), the process of determining whether there are missed or false positives between two adjacent pacing pulses based on amplitude is as follows: Search for the maximum peak point located between two adjacent pacing pulses p[k] and p[k+1]. If its amplitude exceeds K1 times the average amplitude of the two pacing pulses p[k] and p[k+1], and the distance from both pacing pulses is at least T1, then this peak point is marked as a valid pacing pulse. If the amplitude of pacing pulse p[k+1] is less than K1 times the average amplitude of the two adjacent pacing pulses p[k] and p[k+2], then this pacing pulse is a false positive pacing pulse and is removed. This screening process is repeated T times.

[0021] Further, in step 5), it is confirmed whether there are any missed low-amplitude pacing pulses. If so, the missed pacing pulses are searched according to the backward search algorithm. The process is as follows: Taking the identified pacing pulse as the center, calculate the ensemble mean eaECG of the data with a length of T3. Calculate the interval between all adjacent pacing pulses, find the pacing pulse intervals shorter than period T2, and calculate the average interval avgPP; for each pacing pulse interval [p[k], p[k+1]] longer than period T2, find the first K2 maximum peak points between these two pacing pulses as candidate pacing pulses, where K2 = (p[k+1] - p[k]) / avgPP. Taking the candidate pacing pulse as the center, take the data cdECG of a length of T3, and perform cross-correlation analysis between it and the ensemble mean eaECG. If the correlation coefficient Corcoef is greater than 0.5, the candidate pacing pulse is considered a valid pacing pulse.

[0022]

[0023] Where P is the number of pacing pulses identified, and p(k) is the position of the kth pacing pulse;

[0024]

[0025] Where m(cdECG) is the average value of cdECG and m(eaECG) is the average value of eaECG.

[0026] This invention uses high-pass filtering to remove ECG components and low-frequency noise while retaining pacing pulses. By superimposing the absolute values ​​of each lead and using dynamic threshold detection, the location of the pacing pulse can be detected, thus improving the accuracy of pacing pulse detection.

[0027] The beneficial effect of this invention is that the method enables the detection of pacing pulses even at low sampling rates. Attached Figure Description

[0028] Figure 1 This is a flowchart of the pacing pulse detection method of the present invention;

[0029] Figure 2 This is the waveform after high-pass filtering;

[0030] Figure 3 It is a waveform diagram after the absolute values ​​of each lead are superimposed;

[0031] Figure 4 This is the waveform after low-pass filtering;

[0032] Figure 5 This is a schematic diagram of the pacing pulse detection results. Detailed Implementation

[0033] The present invention will now be further described with reference to the accompanying drawings.

[0034] Reference Figures 1-5 A multi-lead low-sampling-rate pacing pulse detection method is proposed. Through signal filtering, signal absolute value superposition, low-pass filtering, potential pacing pulse detection, and pacing pulse screening, the pacing pulse location can be obtained and the pacing pulse can be detected at a low sampling rate.

[0035] like Figure 1 As shown, the present invention provides a multi-lead low sampling rate pacing pulse detection method, comprising the following steps: 1) signal filtering; 2) signal absolute value superposition; 3) low-pass filtering; 4) potential pacing pulse detection; 5) pacing pulse screening.

[0036] In the signal filtering process step 1), such as Figure 2 As shown, the original ECG signals of each lead are subjected to high-pass filtering to remove power line interference, QRS waves, P waves, T waves and other ECG components and low-frequency noise, while retaining the pacing signal. In this embodiment, a 55Hz second-order FIR high-pass filter is used to filter baseline drift, power line interference, normal ECG signal components and low-frequency interference and other noise signals.

[0037] In the step of summing the absolute values ​​of the signals in step 2), such as Figure 3As shown, the absolute values ​​of the filtered signals from each lead are taken, and then the signals from each lead are superimposed to further amplify the pacing pulse, wherein the number of leads numLead satisfies 3≤numLead≤12;

[0038] In the low-pass filtering step described in 3), such as Figure 4 As shown, the superimposed signal is subjected to multi-order (second order and above) FIR low-pass filtering to remove high-frequency noise. In this embodiment, a 5Hz second-order FIR low-pass filter is used.

[0039] In the pacing pulse detection step described in 4), based on the preprocessed ECG signal from 3), a positive peak point is located. Using the detected positive peak point as the center and W as the window length, the area of ​​the signal within this window region is calculated to determine the threshold.

[0040]

[0041] Where K represents the set coefficient. Starting from the i-th peak point, with the current detection point p(i) as the center, calculate the signal area areaVal within the region [i-0.25W, i+0.25W]. Compare this area areaVal with the threshold Threshold. If it exceeds half of the threshold, record the peak point as the potential pacing pulse position paceCandi[k], where k is the k-th potential pacing pulse detected. Continue detecting the next peak point until all peak points have been detected.

[0042] In the pacing pulse screening step described in 5), the maximum peak point located between two adjacent pacing pulses p[k] and p[k+1] is searched. If its amplitude exceeds K1 times the average amplitude of the two pacing pulses p[k] and p[k+1], and the distance from both pacing pulses is at least T1, then this peak point is marked as a valid pacing pulse. If the amplitude of the pacing pulse p[k+1] is less than K1 times the average amplitude of the two adjacent pacing pulses p[k] and p[k+2], then this pacing pulse is a false positive pacing pulse and is removed. This screening process is repeated T times to obtain a pacing pulse position sequence. Taking the identified pacing pulse as the center, the ensemble average eaECG of data with a length of T2 is calculated; the interval between all adjacent pacing pulses is calculated, the pacing pulse intervals shorter than the period T3 are found, and the average interval avgPP is calculated. For each pacing pulse interval [p[k], p[k+1]] greater than cycle T3, find the first K2 maximum peak points between these two pacing pulses as candidate pacing pulses, where K2 = (p[k+1] - p[k]) / avgPP. Using the candidate pacing pulses as centers, take data of length T2 (cdECG) and perform cross-correlation analysis with the ensemble mean (eaECG). If the correlation coefficient Corcoef is greater than 0.5, the candidate pacing pulse is considered a valid pacing pulse, as follows:

[0043]

[0044] Where P is the number of pacing pulses identified, and p(k) is the position of the kth pacing pulse;

[0045]

[0046] Where m(cdECG) is the average value of cdECG, and m(eaECG) is the average value of eaECG. For example... Figure 5 As shown, this invention demonstrates its effectiveness in detecting the position of a pacing pulse.

[0047] The above-described embodiments provide a detailed explanation of the technical solution of the present invention, which can effectively detect the position of pacing pulses at low sampling rates.

[0048] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in these embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for detecting multi-lead low-sampling-rate pacing pulses, characterized in that, The method includes the following steps: 1) Signal filtering: High-pass filtering is performed on the raw ECG signals of each lead to remove power line interference, QRS wave, P wave, T wave ECG components and low-frequency noise, while retaining the pacing signal; 2) Absolute value and superposition of signals: Take the absolute value of the filtered signals from each channel, and then superimpose the signals from each lead to further amplify the pacing pulse; 3) Low-pass filtering: The superimposed signals are subjected to multi-stage FIR low-pass filtering to remove high-frequency noise; 4) Potential pacing pulse detection: Based on the ECG signal after preprocessing in 3), find positive peak points; determine a threshold based on the detected positive peak points; find peak points exceeding the threshold based on the threshold, as the potential pacing pulse position sequence, and denote paceCandi[k] as the k-th pacing pulse position, k = 1, 2, …, P; Determine the threshold based on the positive peak point. Threshold The process is as follows: Using the detected positive peak point as the center and W as the window length, calculate the area of ​​the signal within the window region; ； Where K represents the set coefficient; 5) Pacing pulse screening: The pacing pulses detected in 4) are screened. Based on the amplitude, it is determined whether there are falsely detected pacing pulses and missed pacing pulses. If so, the missed pacing pulses are searched and the falsely detected pacing pulses are removed. It is also confirmed whether there are missed low-amplitude pacing pulses. If so, the missed pacing pulses are searched according to the backward search algorithm.

2. The method for detecting low-sampling-rate pacing pulses in leads as described in claim 1, characterized in that, In step 1), the original ECG signals of each lead are subjected to high-order FIR high-pass filtering to remove power frequency interference, QRS wave, P wave, T wave ECG components and low-frequency noise, while retaining the pacing signal.

3. The method for detecting low sampling rate pacing pulses in leads as described in claim 1, characterized in that, In step 2), the absolute value superposition of the high-pass filtered ECG signal includes: ; in, The signal is the superimposed signal, and N is the number of leads.

4. The method for detecting low-sampling-rate pacing pulses in leads as described in claim 1, characterized in that, In step 4), the process of finding peak points exceeding the threshold as potential pacing pulse position sequences is as follows: Starting from the i-th peak point, with the current detection point p(i) as the center, calculate the signal area areaVal within the region [i-0.25W, i+0.25W], and then multiply this area areaVal by the threshold. Threshold The comparison is performed. If the peak value exceeds half of the threshold, the peak value is recorded as the potential pacing pulse position paceCandi[k], where k is the kth potential pacing pulse detected. The next peak value is detected, and so on, until all peak values ​​are detected.

5. The method for detecting low sampling rate pacing pulses in leads as described in claim 1, characterized in that, In step 5), the amplitude is used to determine whether there are missed or false identifications between two adjacent pacing pulses: the maximum peak point between two adjacent pacing pulses p[k] and p[k+1] is searched. If its amplitude exceeds K1 times the average amplitude of the two pacing pulses p[k] and p[k+1], and the distance from both pacing pulses is at least T1, then this peak point is marked as a valid pacing pulse; if the amplitude of pacing pulse p[k+1] is less than K1 times the average amplitude of the two adjacent pacing pulses p[k] and p[k+2], then the pacing pulse is a false positive pacing pulse and is removed; this screening process is repeated T times.

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