A comprehensive review of SNN-based methods for physiological signal analysis across EEG, ECG, and EMG modalities is provided, identifying recurring architectural and methodological trends that indicate the dominance of convolution-based spiking architectures, the critical role of encoding strategies in determining energy efficiency, and the task-dependent nature of SNN training approaches.
Abstract
Physiological signals, such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG) play a critical role in modern clinical diagnosis, monitoring, and rehabilitation. While deep artificial neural networks (ANNs) have achieved strong performance in analyzing these signals, their high computational cost and energy demands limit deployment in real-time, wearable, and edge-based healthcare systems. Spiking Neural Networks (SNNs), inspired by biological neural computation, offer an event-driven alternative that naturally captures temporal dynamics and enables energy-efficient inference. This article provides a comprehensive review of SNN-based methods for physiological signal analysis across EEG, ECG, and EMG modalities. We survey neuron models, network architectures, encoding schemes, and training methodologies, and systematically review recent state-of-the-art applications in medical diagnosis and rehabilitation. Through analysis, we identify recurring architectural and methodological trends that indicate the dominance of convolution-based spiking architectures, the critical role of encoding strategies in determining energy efficiency, and the task-dependent nature of SNN training approaches. Despite demonstrating performance comparable to conventional deep learning models, often at significantly reduced computational cost, SNN research remains challenged by inconsistent evaluation protocols, limited benchmark standardization, and restricted clinical validation. We conclude by outlining key open challenges and future research directions, emphasizing the need for standardized benchmarks, encoding-aware training, and hybrid ANN–SNN systems. As neuromorphic hardware and event-driven learning methods continue to mature, SNNs are well-positioned to introduce scalable, low-power physiological signal processing for next-generation intelligent healthcare systems.
It is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems is proved.
Mesala Sravani, K. Kumari, S. M. Reddy· International Journal of Unc...· 0 citations
The automated recognition of epileptic events from long-term electroencephalogram (EEG) monitoring remains a clinically significant challenge, demanding high sensitivity and computational efficiency for real-world deployment. Spiking Neural Networks (SNNs) offer a biologically plausible and energy-efficient alternative to conventional deep learning. However, existing SNN architectures for seizure detection either lack spatial information across electrode channels or fail to model global dependencies across temporal positions. In this paper, we propose a dual-branch Spatio-Temporal Spiking Transformer (ST-Spikformer) to detect epileptic seizure segments from scalp long-term electroencephalogram (EEG) recordings. We evaluated on the CHB-MIT Scalp EEG dataset, which contains 23 recording cases. Experimental results demonstrate high performance against published SNN baselines, with an accuracy of 96.2%, sensitivity of 90%, and specificity of 99%. Our implementation is available at https://github.com/ngochien108/ST-Spikformer.
Hien N. Cao, T. V. Vũ, H. T. M. Tran et al.· 2026 11th International Conf...· 0 citations
Detecting muscle fatigue via surface electromyography (sEMG) is essential for applications in sports, rehabilitation, and wearable health monitoring. Accurate and timely detection of fatigue is crucial for preventing injuries, optimizing physical performance, and ensuring user safety during prolonged activity. However, existing deep learning models are often unsuitable for this task due to their high computational cost and dependence on large-scale data. In this work, we propose an energy-efficient framework for muscle fatigue detection based on Spiking Neural Networks (SNNs), which exploit sparse, event-driven computation and temporal modeling. We further introduce a quantization-compatible training scheme (SDH) that combines multiple regularization terms to improve robustness under noisy conditions. Evaluated on two public sEMG datasets against a broad set of baselines and under seven noise conditions including physically motivated perturbations, our quantized SNNs match or exceed strong baselines while remaining more stable under diverse noise and reducing estimated energy consumption by up to 201.77x. These results demonstrate the framework's strong potential for real-time deployment in low-power wearable systems.
Kaiwen Tang, Jiaqi Dong, Zhanglu Yan et al.· 0 citations
Continuous EEG monitoring for epilepsy is constrained by the limited power and memory budgets of wearable and implantable devices. Deep neural networks can detect seizures with high accuracy, but their computational cost and model size make them difficult to deploy on such platforms. In this work we use a single 1D CNN seizure detector on the CHB-MIT scalp EEG dataset as a common baseline, and then investigate three brain-inspired efficiency strategies: (i) conversion of the CNN into a spiking neural network (SNN) via parameter transfer, (ii) EEG channel pruning combined with 2:4 structured weight sparsity, and (iii) INT8 quantization using FX- and ONNX-based workflows, including quantization-aware training and operator fusion. The quantized CNN variants reduce stored model size from 1.63 MB to 0.44 MB, lower estimated energy per inference by up to 64%, and achieve as much as 2.8 times speedup in CPU latency while preserving, and in one case slightly improving, AUC. The pruned CNN halves the number of input channels and non-zero weights with only a modest accuracy drop, and the SNN conversion provides a spiking implementation with sparse temporal activity. Together, these experiments characterize three complementary efficiency directions for seizure detection.
A novel SNN model, Dynamic Gaussian-Weighted Evolving Spiking Neural Network (DyGENN) is developed and applied for the detection of Parkinson’s disease and epileptic seizure and outperforms existing models in both scenarios, suggesting its potential for broader application to other neurological conditions characterized by structured biomarker data.
Priya Das, S. Nanda, P. K. Sahoo et al.· Discover Computing· 0 citations
Living nervous systems sense the cardiovascular rhythm with a parsimony that engineered monitors cannot match: sensory receptors encode changes rather than absolute levels, neurons communicate through sparse all-or-none events, retinal circuits sharpen salient features through lateral inhibition, and attention is allocated by surprise. We translate these four principles into BioSpike-Net, a fully event-driven spiking neural network for cardiac-rhythm classification from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. A sensory-adaptive spike encoder (SASE) converts analogue waveforms into ON/OFF spike trains through a mechanoreceptor-inspired gain-control law; adaptive-threshold leaky integrate-and-fire layers integrate these events; a lateral-inhibition spiking convolution emphasises locally salient morphology; and a novelty-gated temporal attention mechanism concentrates computation on the most surprising portions of each beat. Evaluated on the MIT-BIH Arrhythmia Database, PTB-XL, CPSC-2018, and a PhysioNet-derived PPG corpus, BioSpike-Net achieved 97.6 ± 0.3% accuracy and 95.8 ± 0.4% macro-F1 on MIT-BIH five-class arrhythmia classification, and 0.982 ROC-AUC on PPG atrial-fibrillation detection, matching or exceeding strong recurrent, convolutional, and transformer baselines while requiring an estimated 6.4 µJ per inference—approximately 27-fold below the transformer baseline—owing to a mean activation density below 0.10 spikes per neuron per time step. Ablations show that each biological principle contributes a measurable and interpretable accuracy-versus-energy benefit, and the network degrades gracefully under additive noise and motion artefact. By grounding architecture in the economy of biological sensing, this work offers a route to sustainable, always-on cardiac monitoring.
C. Ding, Jia-Hao Tian· Biomimetics· 0 citations
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