Abstract

Cardiac arrhythmia is characterized by irregular heartbeats caused by disruptions in the heart’s electrical activity. Electrocardiograms (ECGs) are commonly used by physicians to detect arrhythmias noninvasively; however, interpreting ECG signals can be time-consuming and challenging due to inherent noise, which complicates the detection process. To address this issue, an automated system capable of accurately detecting arrhythmias in real time was developed to assist medical professionals by reducing manual interpretation time and accelerating diagnosis. In this study, arrhythmias were automatically classified into five categories: normal (N), supraventricular (S), ventricular (V), fusion (F), and unknown (Q). The Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) Arrhythmia dataset was utilized, and class imbalance was addressed using the synthetic minority oversampling technique (SMOTE). To reduce noise in the ECG data, an infinite impulse response (IIR) filter was applied. The filtered signals were then processed using a deep learning (DL) model integrating one-dimensional convolutional neural networks (1D-CNNs) with bidirectional long short-term memory (Bi-LSTM) layers, enabling the extraction of both short-term and long-term temporal features. A soft attention mechanism was incorporated to enhance feature selection, while fuzzy dynamic time warping (FDTW) was employed to account for temporal variations in the ECG signals. The proposed model demonstrated strong performance across multiple evaluation metrics, including accuracy, sensitivity, and specificity. It achieved an average accuracy of 99.89%, indicating high robustness and precision in arrhythmia classification. This approach presents a reliable and efficient method for arrhythmia detection, surpasses existing techniques, and contributes to improved clinical decision-making by enhancing the efficiency and accuracy of ECG-based diagnosis. Additionally, advanced noise-reduction techniques improved signal quality and reduced the likelihood of diagnostic errors

Keywords

Cardiac arrhythmia detection, ECG signal processing, Deep learning, 1D-CNN and Bi-LSTM, Attention mechanism, Fuzzy dynamic time warping (FDTW).

Cite this article

K M, Syed K. Enhanced arrhythmia detection using a convolutional model and fuzzy dynamic time warping on filtered ECG data. International Journal of Advanced Technology and Engineering Exploration. 2025;12(132):1749-1767. DOI : 10.19101/IJATEE.2024.111100625

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