A comprehensive survey on NSGA-II for multi-objective optimization and applications

H Ma, Y Zhang, S Sun, T Liu, Y Shan - Artificial Intelligence Review, 2023 - Springer
In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-
II) has attracted extensive research interests, and it is still one of the hottest research …

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

I Olier, S Ortega-Martorell, M Pieroni… - Cardiovascular …, 2021 - academic.oup.com
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …

Empirical analysis of machine learning algorithms on imbalance electrocardiogram based arrhythmia dataset for heart disease detection

S Ketu, PK Mishra - Arabian Journal for Science and Engineering, 2022 - Springer
Living beings are subjected to many hazards during their course of life. Owing to high
mortality rate, heart disease (HD) is among leading hazards for living being. It is world's one …

[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks

S Nurmaini, AE Tondas, A Darmawahyuni… - Future Generation …, 2020 - Elsevier
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …

A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network

J Wang - Future Generation Computer Systems, 2020 - Elsevier
Atrial fibrillation (AF) is one of the main causes of life-threatening heart disease. Its detection
and diagnosis have been highly concerned by physicians in recent years. However, the …

Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG

H Serhal, N Abdallah, JM Marion, P Chauvet… - Computers in Biology …, 2022 - Elsevier
Atrial fibrillation (AF) is the most common supraventricular cardiac arrhythmia, resulting in
high mortality rates among affected patients. AF occurs as episodes coming from irregular …

Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal

E Ebrahimzadeh, M Kalantari, M Joulani… - Computer methods and …, 2018 - Elsevier
Abstract Background and Objective Paroxysmal Atrial Fibrillation (PAF) is one of the most
common major cardiac arrhythmia. Unless treated timely, PAF might transform into …

[HTML][HTML] Prediction of paroxysmal atrial fibrillation using new heart rate variability features

A Parsi, M Glavin, E Jones, D Byrne - Computers in Biology and Medicine, 2021 - Elsevier
Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart
failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any …

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

M Salvi, MR Acharya, S Seoni, O Faust… - … : Data Mining and …, 2024 - Wiley Online Library
Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most
prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent …

Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications

M Surucu, Y Isler, M Perc, R Kara - Chaos: An Interdisciplinary Journal …, 2021 - pubs.aip.org
In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so
that patients can take precautions before putting their and others' lives in potentially life …