Work

ECG classification as a dynamic process based on a spiking neural network

Year: 2024

Type: article

Abstract: 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.

Source: Optoelectronic Information-Power Technologies

DOI: 10.31649/1681-7893-2024-48-2-68-77

Authors: D.A. Myloserdov, O.K. Kolesnytskyj, O.S. Volosovych, Sholpan Zhumagulova, O.O. Korolenko

Institutions: Vinnytsia National Technical University, Al-Farabi Kazakh National University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: diamond

OpenAlex ID: W4404496979

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