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<p>Classifying arrhythmia is an essential step in the diagnosis and monitoring of cardiovascular illness. Deep learning (DL) models are trained on the electro-cardiogram recordings found in the ECG signal dataset to accurately classify arrhythmia into five groups: Normal (N), Fusion (F), Supraventricular (S), Ventricular (V), and Unknown (Q). In the proposed work, Ant Colony Optimization (ACO) to fine-tune the hyperparameter of two potent Deep Learning (DL) architectures, Bidirectional Long Short-Term Memory (Bi-LSTM) and Fully Convolutional Network (FCN) is utilised. Initially, ECG signals are pre-processed, where Multi-Resolution Wavelet-based techniques are applied for noise removal. Afterwards, the Stationary Wavelet-Hilbert transform (SW-HT) is applied for feature extraction. Next, training, validation, and testing sets are created from the extracted feature set. After performing data balancing using the SMOTE (Synthetic Minority Over-sampling Technique) algorithm, classification using optimized deep learning models is performed. With an overall accuracy of 98.9% (ACoBi-LSTM) and 99.1% (ACoFCN) on the 5-Class (N, S, V, F, Q) arrhythmia classification in the MIT-BIH dataset, the proposed model’s performance is compared and analyzed against the existing methods.</p>

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FADLEC: feature extraction and arrhythmia classification using deep learning from electrocardiograph signals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月29日

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  • Sumita Lamba,
  • Satender Kumar,
  • Manoj Diwakar

摘要

Classifying arrhythmia is an essential step in the diagnosis and monitoring of cardiovascular illness. Deep learning (DL) models are trained on the electro-cardiogram recordings found in the ECG signal dataset to accurately classify arrhythmia into five groups: Normal (N), Fusion (F), Supraventricular (S), Ventricular (V), and Unknown (Q). In the proposed work, Ant Colony Optimization (ACO) to fine-tune the hyperparameter of two potent Deep Learning (DL) architectures, Bidirectional Long Short-Term Memory (Bi-LSTM) and Fully Convolutional Network (FCN) is utilised. Initially, ECG signals are pre-processed, where Multi-Resolution Wavelet-based techniques are applied for noise removal. Afterwards, the Stationary Wavelet-Hilbert transform (SW-HT) is applied for feature extraction. Next, training, validation, and testing sets are created from the extracted feature set. After performing data balancing using the SMOTE (Synthetic Minority Over-sampling Technique) algorithm, classification using optimized deep learning models is performed. With an overall accuracy of 98.9% (ACoBi-LSTM) and 99.1% (ACoFCN) on the 5-Class (N, S, V, F, Q) arrhythmia classification in the MIT-BIH dataset, the proposed model’s performance is compared and analyzed against the existing methods.