Novel DERMA fusion technique for ECG heartbeat classification

Mastoi, Qurat-ul-ain and Wah, Teh Ying and Mohammed, Mazin Abed and Iqbal, Uzair and Kadry, Seifedine and Majumdar, Arnab and Thinnukool, Orawit (2022) Novel DERMA fusion technique for ECG heartbeat classification. Life-Basel, 12 (6). ISSN 2075-1729, DOI

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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.

Item Type: Article
Funders: NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [Grant No:B16F640189], Chiang Mai University [Grant No:R000029859]
Uncontrolled Keywords: Cardiovascular disease; ECG signal processing; Features extraction; Machine learning; ECG heartbeat classification
Subjects: Q Science > QH Natural history > QH301 Biology
Q Science > QR Microbiology
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 13 Oct 2023 08:13
Last Modified: 13 Oct 2023 08:13

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