Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review

JD Huang, J Wang, E Ramsey, G Leavey, TJA Chico… - Sensors, 2022 - mdpi.com
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant
interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as …

[HTML][HTML] Analysis of various techniques for ECG signal in healthcare, past, present, and future

T Anbalagan, MK Nath, D Vijayalakshmi… - Biomedical Engineering …, 2023 - Elsevier
Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …

A new 12-lead ECG signals fusion method using evolutionary CNN trees for arrhythmia detection

MN Meqdad, F Abdali-Mohammadi, S Kadry - Mathematics, 2022 - mdpi.com
The 12 leads of electrocardiogram (ECG) signals show the heart activities from different
angles of coronal and axial planes; hence, the signals of these 12 leads have functional …

Robust electrocardiogram delineation model for automatic morphological abnormality interpretation

S Nurmaini, A Darmawahyuni, MN Rachmatullah… - Scientific Reports, 2023 - nature.com
Abstract Knowledge of electrocardiogram (ECG) wave signals is one of the essential steps
in diagnosing heart abnormalities. Considerable performance with respect to obtaining the …

[HTML][HTML] Identification of atrial fibrillation with single-lead mobile ECG during normal sinus rhythm using deep learning

J Kim, SJ Lee, B Ko, M Lee, YS Lee… - Journal of Korean …, 2024 - ncbi.nlm.nih.gov
Background The acquisition of single-lead electrocardiogram (ECG) from mobile devices
offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial …

[HTML][HTML] Machine learning for detecting atrial fibrillation from ecgs: Systematic review and meta-analysis

C Xie, Z Wang, C Yang, J Liu, H Liang - Reviews in Cardiovascular …, 2024 - imrpress.com
Background: Atrial fibrillation (AF) is a common arrhythmia that can result in adverse
cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) …

M-XAF: Medical explainable diagnosis system of atrial fibrillation based on medical knowledge and semantic representation fusion

Z Li, Y Jin, Y Tian, J Liu, M Wang, X Wei, L Zhao… - … Applications of Artificial …, 2024 - Elsevier
Background Electrocardiogram (ECG) is a critical diagnostic tool used for screening atrial
fibrillation (AF). In recent years, several deep learning-based ECG automatic diagnosis …

Delineation of 12-lead ECG representative beats using convolutional encoder–decoders with residual and recurrent connections

V Krasteva, T Stoyanov, R Schmid… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
The aim of this study is to address the challenge of 12-lead ECG delineation by different
encoder–decoder architectures of deep neural networks (DNNs). This study compares four …

ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs

L Chen, Z Jiang, J Barker, H Zhou… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays
an important role in cardiovascular diagnosis and care. Whilst deep delineation frameworks …

Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform Variations Related to P Wave and PR Interval

R Fatima, S Younis, F Shaikh, H Imran, H Sultan… - arXiv preprint arXiv …, 2024 - arxiv.org
The reliable diagnosis of cardiac conditions through electrocardiogram (ECG) analysis
critically depends on accurately detecting P waves and measuring the PR interval. However …