A Survey on Arrhythmia Disease Detection Using Deep Learning Methods
- Title
- A Survey on Arrhythmia Disease Detection Using Deep Learning Methods
- Creator
- Lufiya G.C.; Thomas J.; Aswathy S.U.
- Description
- The Cardiovascular conditions are now one of the foremost common impacts on human health. Report from WHO, says that in India 45% of deaths are caused due to heart diseases. So, heart disease detection has more importance. Manual auscultation was used to diagnose cardiovascular problems just a few years ago. Nowadays computer-assisted technologies are used to identify diseases. Accurate detection of the disease can make recovery simpler, more effective, and less expensive. In this proposed work, 11years of research works on arrhythmia detection using deep learning are integrated. Moreover, here presents a comprehensive evaluation of recent deep learning-based approaches for detecting heart disease. There are a number of review papers accessible that focus on traditional methods for detecting cardiac disease. This article addresses some essential approaches for categorizing ECG signal images into desired classes, such as pre-processing, feature extraction, feature selection, and classification. However, the reviewed literatures consolidated details have been summarized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Source
- Lecture Notes in Networks and Systems, Vol-419 LNNS, pp. 55-64.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Artificial neural network; Decision tree; Heart disease; K nearest neighbor; Multilayer perceptron; Support vector machine
- Coverage
- Lufiya G.C., Department of Computer Science, CHRIST (Deemed To Be) University, Bangalore, India; Thomas J., Department of Computer Science, CHRIST (Deemed To Be) University, Bangalore, India; Aswathy S.U., Department of Computer Science, JYOTHI Engineering College Cheruthuruthy, Thrissur, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303096298-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Lufiya G.C.; Thomas J.; Aswathy S.U., “A Survey on Arrhythmia Disease Detection Using Deep Learning Methods,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20431.