Work

Hybrid Bi-LSTM-Spiking Neural Network for Energy-Efficient ECG Arrhythmia Classification

Year: 2026

Type: article

Abstract: Wearable ECG monitoring requires a classifier that can operate within a limited power budget. This study therefore combined a bidirectional long short-term memory (Bi-LSTM) layer, which processed the waveform in both temporal directions, with two leaky integrate-and-fire (LIF) spiking layers. The recurrent output was converted into sparse events, while a fast-sigmoid surrogate gradient enabled training through the nondifferentiable spike function using backpropagation through time. Twenty hyperparameter configurations were evaluated using 60-40, 70-30, and 80-20 training-testing partitions. The best configuration, Configuration 3 using the 80-20 partition, comprised one 64-unit Bi-LSTM layer, a membrane decay parameter of 0.85, and a learning rate of 0.00020. It achieved a training accuracy of 97.64% and a test accuracy 96.88%, exceeding the reporting accuracy of the standalone SNN by 5.21 percentage points. Thus, the model maintained high ECG classification performance while shifting its later-stage processing to event-driven spike activity.

Source: Journal of Telecommunication Electronic and Computer Engineering (JTEC)

DOI: 10.54554/jtec.2026.18.03.002

Authors: Nor Amalia Dayana Mohamad Noor, Wong Yan Chiew, Zarina Mohd Noh, Ranjit Singh Sarban Singh, Cheng Xiang

Institutions: Universiti Teknikal Malaysia Melaka, Sunway University, Lingnan Normal University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Ministry of Higher Education, Malaysia, Universiti Teknikal Malaysia Melaka