A Crucial Wave Detection and Delineation Method for Twelve-Lead ECG Signals

Abstract

Delineating the crucial waves in electrocardiogram records is a paramount work for the automatic diagnosis system of heart diseases. In this paper, a novel method is described to determine the boundaries and the peaks of P waves, QRS complexes and T waves by utilizing twelve-lead electrocardiogram signals. It avoids the difficulty of setting the thresholds when determining the boundaries of crucial waves and also the trouble of selection of wavelet basis as the wavelet-based method does. The signals are first preprocessed by a bandpass filter. After that, the locations of QRS complexes are identified. And based on the QRS locations, adaptive search windows are set to detect the locations of P waves and T waves. Then, a method called local distance transform decides the wave boundary in each lead. Finally, the final boundary determination rule is applied to obtain reliable boundaries. We justify the performance of our algorithm on LUDB database. When the tolerance window interval is 40ms, the peak accuracies of P wave, QRS complex and T wave are all beyond 98% and their boundary accuracies are all above 96%. Compared with the derivative threshold method and the wavelet-based method where the tolerance window interval is 150ms, the algorithm shows a sensitivity and a positive predictive value of peaks and boundaries greater than or equal to 98.43% and 96.44% for the P wave, 99.89% and 99.86% for the QRS complex and 99.21% and 99.85% for the T wave. For the critera of average error and standard deviation, our method has the performance similar to those methods. In addition, our algorithm can also handle such several situations where the boundary determination of crucial waves is tough as high T wave, high noise and baseline wandering well.

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

“…Regarding two commonly used cutoff frequencies (i.e., 40 and 150 Hz) in low-pass filtering of ECGs, 40 Hz could effectively eliminate the high-frequency noises but lead to the elevation of J-point, i.e., the junction between QRS termination and ST-segment onset ( Nakagawa et al, 2014 ; Christov et al, 2017 ), resulting in inaccuracy of the onset of the ST segment, whereas 150 Hz could overcome this problem but cause a high level of residual noise ( Ricciardi et al, 2016 ; Christov et al, 2017 ). As compared with a band-pass filter, the discrete wavelet transform could perform better in terms of eliminating high-frequency noise and keeping the morphology feature points of the ECG signal ( Addison, 2005 ; Singh and Pradhan, 2018 ; Chen et al, 2020 ), as illustrated in Supplementary Figure S1 . Therefore, high-frequency noise was removed using discrete wavelet transform and wavelet thresholding ( Kumar et al, 2021 ).…”

Electrocardiogram Fiducial Point Detector Using a Bilateral Filter and Symmetrical Point-Filter Structure

“…Table 5 shows the PQRST complex detection result of the proposed method and existing methods (LPD [33], WT [41], PCGS [44], HFEA [42], MHMM [47]) on 27 Records (19 Records except for MHMM from the original paper) from the QT-DB. However, the result of TLBLDT was obtained by the LU database [48]. Two-standard-deviation tolerances have been included in the last row [57].…”

“…These methods exhibit excellent performance in identifying ECG FPs. Recently, a 12 lead-based local distance transformation (TLBLDT) was proposed for ECG wave detection and delineation [48]. However, the studies did not perform HRV analysis based on the detected ECG FPs for some medical diagnosis or pathology analysis.…”

A Multi-Lead Fusion Method for the Accurate Delineation of QRS Complex Location in 12 Lead ECG Signal

“…The 12 lead ECG signal shows the pathological status of cardiovascular system by changes in its waveforms or rhythms [3]. In the clinical environment, physicians analyze the ECG records of patients to judge whether they have a benign or unkind heart state [4]. Manual analysis process is much laborious and repetitive.…”