ECG classification as a dynamic process based on a spiking neural network
The article analyzes the ECG classifying methods. A method of dynamic ECG classification using spiking neural networks is proposed. Dynamic parameters are selected, which are a representation of the ECG signal in a time series. These parameters are fed into the input of a spiking neural network, which outputs both a single heartbeat and a full ECG study. The developed spiking neural network has fast learning and uses large amounts of data for training.
Authors
- Ольга Короленко (ORCID: https://orcid.org/0000-0002-0771-4298)
- Oleg K. Kolesnytskyj (ORCID: https://orcid.org/0000-0003-0336-4910)
- Oleksandr Volosovych (ORCID: https://orcid.org/0000-0002-5497-6805)
- Sholpan Zhumagulova (ORCID: https://orcid.org/0009-0006-3696-0021)
- Dmytro Myloserdov (ORCID: https://orcid.org/0009-0001-5216-3552)
Publication Details
- Journal
- Optoelectronic Information-Power Technologies
- Published
- 2024-11-19
- DOI
- https://doi.org/10.31649/1681-7893-2024-48-2-68-77
- Primary Topic
- Advanced Research in Systems and Signal Processing
- Type
- article
- Field-Weighted Citation Impact
- 0.00