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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\u2019s performance is compared and analyzed against the existing methods.<\/jats:p>","DOI":"10.1007\/s44163-025-00290-0","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T13:43:33Z","timestamp":1748526213000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["FADLEC: feature extraction and arrhythmia classification using deep learning from electrocardiograph signals"],"prefix":"10.1007","volume":"5","author":[{"given":"Sumita","family":"Lamba","sequence":"first","affiliation":[]},{"given":"Satender","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Manoj","family":"Diwakar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"290_CR1","first-page":"398","volume":"10","author":"LA Raffee","year":"2020","unstructured":"Raffee LA. 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