IoT-enabled ECG-based heart disease prediction using three-layer deep learning and meta-heuristic approach

IoT-enabled electrocardiogram (ECG) devices are used to identify abnormal heart activity in the ECG signals and predict heart disease. Patients wear these devices, which send the ECG data to a healthcare provider for analysis. To investigate the information and forecast the likelihood of heart ailme...

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Veröffentlicht in:	Signal, image and video processing Jg. 18; H. 1; S. 361 - 367
Hauptverfasser:	Mishra, Jyoti, Tiwari, Mahendra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.02.2024 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial neural networks Cardiac arrhythmia Cardiovascular disease Computer Imaging Computer Science Continuous wavelet transform Deep learning Electrocardiography Feature extraction Health care industry Heart Heart diseases Heart rate Heuristic methods Image Processing and Computer Vision Impulse response Internet of Things Machine learning Multimedia Information Systems Optimization models Original Paper P waves Pattern Recognition and Graphics Performance measurement Recurrent neural networks Segments Signal,Image and Speech Processing Software services Vision Wavelet transforms Three-layer framework Alpha spider customized dwarf mongoose optimizer (AS-DMO) Heart disease Improved MI
ISSN:	1863-1703, 1863-1711
Online-Zugang:	Volltext
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