A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.

Madan, Parul; Singh, Vijay; Singh, Devesh Pratap; Diwakar, Manoj; Pant, Bhaskar; Kishor, Avadh

Madan, Parul; Singh, Vijay; Singh, Devesh Pratap; Diwakar, Manoj; Pant, Bhaskar; Kishor, Avadh.

Afiliación

Madan P; Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.
Singh V; Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.
Singh DP; Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.
Diwakar M; Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.
Pant B; Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.
Kishor A; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India.

Bioengineering (Basel) ; 9(4)2022 Apr 02.

Article en En | MEDLINE | ID: mdl-35447712

Palabras clave

ECG; arrhythmia; classification; convolutional neural network (CNN); deep learning; long short-term memory (LSTM)

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioengineering (Basel) Año: 2022 Tipo del documento: Article País de afiliación: India

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