O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classificati...

Full description

Saved in:

Bibliographic Details
Published in:	Complex & intelligent systems Vol. 9; no. 3; pp. 2685 - 2698
Main Authors:	Jangra, Manisha, Dhull, Sanjeev Kumar, Singh, Krishna Kant, Singh, Akansha, Cheng, Xiaochun
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.06.2023 Springer Nature B.V Springer
Subjects:	Algorithms Arrhythmia Artificial neural networks Cardiac arrhythmia Classification CNN Complexity Computational Intelligence Data Structures and Information Theory Deep learning Depthwise separable convolution ECG Engineering Feature maps Global optimization Original Original Article Wavelet transform Deep learning ECG CNN Wavelet transform Depthwise separable convolution Arrhythmia
ISSN:	2199-4536, 2198-6053, 2198-6053
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!