Method of Electrocardiograms Classification Using Spiking Neural Networks
Keywords:
spiking neural networks, neural networks, time series, ECG, Leaky integrate-and-fire neuron, ime series classificationAbstract
The article is devoted to the theoretical development of the ECG classification method using SNN for heart failure recognition. The introduction considers the relevance of the problem, the advantages of SNN over traditional networks, the goal of the study — creating a theoretical basis for an energy-efficient model — and the tasks, including the analysis of the foundations of SNN, ECG features and model architecture. The structure of the article covers the theoretical fundamentals, classification features, proposed architecture, analysis of advantages and conclusions.
The section on the theoretical fundamentals of spiking neural networks describes biological models of neurons, such as Leaky integrate-and-fire mathematical modeling of spike dynamics through differential equations, liquid state machine network types. The Leaky integrate-and-fire model balances biological plausibility with computational efficiency, allowing processing of temporal ECG patterns with low power consumption.
Features of ECG classification using spiking neural networks include signal structure analysis, noise, variability and real-time processing issues. Spiking neural networks can use different training approaches: STDP, gradient methods and approximation. This provides robustness to artifacts and high accuracy, as shown in the reviewed works.
The proposed model architecture consists of three layers: input (ECG encoding into spikes using Leaky integrate-and-fire), reservoir (1000 neurons with recurrent connections and balance of excitatory/inhibitory synapses) and output (decoding for 5 classes through spike counting). The theoretical robustness analysis is based on dynamical systems, ensuring efficiency for wearable devices.
The analysis of advantages highlights the energy efficiency and biomimicry of spiking neural networks compared to others, the challenges of computational complexity and prospects in clinical practice, including telemedicine and personalized diagnostics.
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Нормальна ЕКГ: зубці, сегменти, інтервали. [Електронний ресурс]. Режим доступу: https://therapy.odmu.edu.ua/ecg-online-course/normal-ecg-waves-intervals .
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