Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models
- Title
- Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models
- Creator
- Teja N.B.; Ajay H.K.; Kumar R.S.; Deepa S.; Jayapriya J.; Vinay M.
- Description
- Arrhythmias, or irregular heart rhythms, are a major global health concern. Since arrhythmias can cause fatal conditions like cardiac failure and strokes, they must be rapidly identified and treated. Traditional arrhythmia diagnostic techniques include manual electrocardiogram (ECG) image interpretation, which is time consuming and frequently required for expertise. This research automates and improves the identification of heart problems, with a focus on arrhythmias, by utilizing the capabilities of deep learning, an advanced machine learning technique that performs well at recognizing patterns in data. Specifically, we implement and compare Custom CNN, VGG19, and Inception V3 deep learning models, which classify ECG images into six categories, including normal heart rhythms and various types of arrhythmias. The VGG19 model excelled, achieving a training accuracy of 95.7% and a testing accuracy of 93.8%, showing the effectiveness of deep learning in the comprehensive diagnosis of heart diseases. 2023 IEEE.
- Source
- IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Arrhythmia; Custom CNN; Deep Learning; Electrocardiogram; Inception V3; Machine Learning; VGG19
- Coverage
- Teja N.B., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Ajay H.K., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Kumar R.S., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Deepa S., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Jayapriya J., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Vinay M., Christ Deemed to Be University, Department of Computer Science, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835031646-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Teja N.B.; Ajay H.K.; Kumar R.S.; Deepa S.; Jayapriya J.; Vinay M., “Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19716.