Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder

Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardia...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 9230 - 23
Hauptverfasser:	Shah, Ameet, Singh, Dhanpratap, Mohamed, Heba G., Bharany, Salil, Rehman, Ateeq Ur, Hussen, Seada
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 18.03.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166/985 692/4019/592 Algorithms Arrhythmia Arrhythmias, Cardiac - classification Arrhythmias, Cardiac - diagnosis Arrhythmias, Cardiac - physiopathology Artificial Intelligence Atrial fibrillation Automation Cardiac arrhythmia Cardiovascular diseases Classification Deep learning classification EKG Electrocardiography Electrocardiography - methods Heart diseases Humanities and Social Sciences Humans multidisciplinary Neural networks Neural Networks, Computer Prediction Science Science (multidisciplinary) Self-attention mechanism Signal Processing, Computer-Assisted Cardiac arrhythmia Self-attention mechanism Dense neural network Atrial fibrillation Kalman filter Prediction Deep learning classification Artificial intelligence
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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