PicoSleepNet: An Ultra Lightweight Sleep Stage Classification by Spike Neural Network Using Single-Channel EEG Signal

This study introduces PicoSleepNet, an ultra-lightweight sleep stage classification method that utilizes a spiking neural network (SNN) with single-channel electroencephalogram (EEG) signals. Traditional methods use multi-bit Nyquist sampling and dense computing, which result in high complexity and power consumption, hindering their deployment on wearable devices. To address these limitations, we propose an innovative pipeline combining single-bit sub-Nyquist level-crossing sampling (LCS) and sparse computing based on SNN. First, LCS adaptively encodes EEG signals into event-driven spike sequences, reducing data volume by 6.98× while preserving essential signal characteristics compared to Nyquist sampling. Second, a sparse recurrent spiking neural network (RSNN) architecture, optimized by the masked backpropagation and sparse regularization (Masked-BPSR) technique, improves performance and reduces computational costs. Third, quantization-aware training (QAT) ensures that the model maintains high accuracy with low-bitwidth quantization, significantly reducing computational power consumption and enabling hardware-friendly deployment. Compared with current state-of-the-art sleep staging approaches, PicoSleepNet achieves competitive performance on three public datasets (Sleep-EDF-20, Sleep-EDF-78, and ISRUC-Sleep) with accuracies of 83.5%, 77.9%, 79.4% and macro-F1 scores of 75.2%, 68.1%, 77.2%, respectively. Meanwhile, by leveraging the computational sparsity design of RSNN and the joint optimization of Masked-BPSR and QAT, PicoSleepNet achieves an ultra-lightweight model with only 14.0-25.8 K parameters (reduced by nearly 2×) and 681.4-842.0 K operations (reduced by 27×), reducing computational power consumption by 1480×. This approach demonstrates the feasibility of deploying ultra-lightweight sleep staging systems in wearable devices and neuromorphic hardware, paving the way for broader applications in real-time health monitoring.