Journal Article

Quantum Genetic Algorithm Based Ensemble Learning for Detection of Atrial Fibrillation Using ECG Signals

Mehmood, Qasim, Aldossary, Haya, Usman, Syed Muhammad, Khalid, Shehzad, Alkhrijah, Yazeed, Fahim, Marwa, Alawad, Mohamad A.

2025

Overview

Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a 5-s sliding window with 50% overlap, followed by bandpass filtering between 0.5 and 45 Hz for noise removal. After preprocessing, both time and frequency-domain features are extracted, and a custom one-dimensional Convolutional Neural Network—Bidirectional Long Short-Term Memory (1D CNN-BiLSTM) architecture is introduced. Handcrafted and automated features are concatenated into a unified feature vector and classified using Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) models. A Quantum Genetic Algorithm (QGA) optimizes weighted averages of the classifier outputs for multi-class classification, distinguishing among AF, noisy, normal, and other rhythms. Evaluated on the PhysioNet 2017 Cardiology Challenge dataset, the proposed method achieved an accuracy of 94.40% and an F1-score of 92.30%, outperforming several state-of-the-art techniques.

• Publisher: Tech Science Press
• Subject: Artificial neural networks / Bandpass filters / Cardiac arrhythmia / Cardiology / Classification / Deep learning / Electrocardiography / Ensemble learning / Feature extraction / Fibrillation / Genetic algorithms / Machine learning / Preprocessing / Remote regions / Support vector machines