Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Discover Artificial Intelligence

DOI: 10.1007/s44163-025-00290-0

Authors: Sumita Lamba, Satender Kumar, Manoj Diwakar

Institutions: Quantum University, Graphic Era Hill University, Graphic Era University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,590