A new trained ECG signal Classification method using Modified Spline Activated Neural Network
- Title
- A new trained ECG signal Classification method using Modified Spline Activated Neural Network
- Creator
- Kumar G.R.
- Description
- An ECG (Electrocardiogram) records the electrical activity of the heart and assess heart arrhythmia. Cardiac arrhythmia is an irregular heartbeat caused by unbalanced rhythm. In the past, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, a modified spline activated neural network, a new approach for cardiac arrhythmia classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. The MIT-BIH arrhythmia database was used and experimented for testing and training. 2018 IEEE.
- Source
- Proceedings of the 2nd International Conference on Computing Methodologies and Communication, ICCMC 2018, pp. 317-321.
- Date
- 2018-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Arrhythmia Classification; ECG; feed forward neural network; MIT-BIH ECG data; Multilayer perceptron; RR interval
- Coverage
- Kumar G.R., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-153863452-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kumar G.R., “A new trained ECG signal Classification method using Modified Spline Activated Neural Network,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20886.