Open Access

ARTICLE

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

Yazeed Alkhrijah¹, Marwa Fahim², Syed Muhammad Usman³, Qasim Mehmood³, Shehzad Khalid^4,5,*, Mohamad A. Alawad¹, Haya Aldossary⁶

1 Department of Electrical Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
2 Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad, 44000, Pakistan
3 Department of Computer Science, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad, 44000, Pakistan
4 Computer and Information Sciences Research Center (CISRC), Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11564, Saudi Arabia
5 Department of Computer Engineering, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad, 44000, Pakistan
6 Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail, 31961, Saudi Arabia

* Corresponding Author: Shehzad Khalid. Email:

(This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)

Computer Modeling in Engineering & Sciences 2025, 145(2), 2339-2355. https://doi.org/10.32604/cmes.2025.071512

Received 06 August 2025; Accepted 23 October 2025; Issue published 26 November 2025

Abstract

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.

---

Keywords

---