An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform

Abstract

The detection and delineation of QRS-complexes and T-waves in Electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and Discrete Wavelet Transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the Modulus-Maxima Analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

“…Frequency/ Time Chen et al [22] Band pass 1-30 Hz Zhang et al [23] Band pass 1-45 Hz Xu et al [24] Band pass 5-45 Hz Prakash et al [25] Band pass 0.1-40 Hz…”

A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm

“…ECG signal analysis can be improved by locating the correct fiducial point [ 13 , 14 ]. Digital filters and nonlinear transforms are utilised to excerpt the feature constituents of the QRS signal [ 15 , 16 ]. The modulus maxima were found using a multiscale QRS indicator employing discrete wavelet transform (DWT).…”

Robust Identification of the QRS-Complexes in Electrocardiogram Signals Using Ramanujan Filter Bank-Based Periodicity Estimation Technique

“…Since the publication of the plausibly-first computerized ECG processing algorithm in 1961 (Pipberger et al, 1961), a multitude of algorithms are proposed in the literature to-date on the detection of the QRS-complexes in ECG signals. A methodological review (Berkayaa et al, 2018) suggests that these QRS-complex detection algorithms could be broadly categorized into two groups: 1) digital signal processing (Burguera, 2019;Hossain et al, 2019;Sharma et al, 2019;Zhang et al, 2020;Jorge et al, 2021;Modak et al, 2021;Morshedlou et al, 2021;Rahul et al, 2021;Tueche et al, 2021), and 2) artificial intelligence-based (Mehta and Lingayat, 2008;Merino et al, 2015;Chandra et al, 2019;Goovaerts et al, 2019;Chen and Maharatna, 2020;Jia et al, 2020;He et al, 2021). Rahul et al have proposed an amplitude and interval threshold-based QRS-complex detection algorithm in (Rahul et al, 2021).…”

“…Moreover, the model demands high computational-memory, and it require a graphics processing unit to get implemented. Chen et al have proposed a hierarchical clustering-based R-peak detection and discrete wavelet transform-based T-peak detection algorithm in (Chen and Maharatna, 2020). In (Chen and Maharatna, 2020), the ECG signal is first denoised using Butterworth lowpass and highpass filters to expel the high frequency noises and baseline wonder noise out, respectively, and the amplitude of the denoised signal is normalized within [0, 1].…”

“…Chen et al have proposed a hierarchical clustering-based R-peak detection and discrete wavelet transform-based T-peak detection algorithm in (Chen and Maharatna, 2020). In (Chen and Maharatna, 2020), the ECG signal is first denoised using Butterworth lowpass and highpass filters to expel the high frequency noises and baseline wonder noise out, respectively, and the amplitude of the denoised signal is normalized within [0, 1]. Then the denoised ECG signal is broken into segments of length 1.2 s each and are processed sequentially.…”