Novel DERMA Fusion Technique for ECG Heartbeat Classification

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Mazin Abed Mohammed; Uzair Iqbal; Seifedine Kadry; Arnab Majumdar; Orawit Thinnukool

doi:10.3390/life12060842

Novel DERMA Fusion Technique for ECG Heartbeat Classification

Life (Basel). 2022 Jun 6;12(6):842. doi: 10.3390/life12060842.

Authors

Qurat-Ul-Ain Mastoi¹, Teh Ying Wah¹, Mazin Abed Mohammed², Uzair Iqbal³, Seifedine Kadry⁴, Arnab Majumdar⁵, Orawit Thinnukool⁶

Affiliations

¹ Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
² College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq.
³ Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad Chiniot-Faisalabad, Chiniot 35400, Pakistan.
⁴ Department of Applied Data Science, Noroff University College, NO-4608 Kristiansand, Norway.
⁵ Faculty of Engineering, Imperial College, London SW7 2AZ, UK.
⁶ College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand.

Abstract

An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People's Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.

Keywords: ECG heartbeat classification; ECG signal processing; cardiovascular disease; features extraction; machine learning.

Grants and funding

This research has partially received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (Grant number B16F640189), and Chiang Mai University Grant number R000029859.