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[2602.15888] NeuroSleep: Neuromorphic Event-Driven Single-Channel EEG Sleep Staging for Edge-Efficient Sensing
Summary
NeuroSleep presents a neuromorphic event-driven system for efficient EEG sleep staging, achieving high accuracy with reduced computational load, making it suitable for wearable health monitoring.
Why It Matters
This research addresses the challenge of energy-efficient EEG processing for sleep monitoring, crucial for long-term health applications. By improving accuracy while minimizing computational demands, NeuroSleep could enhance wearable technology's viability in continuous health monitoring.
Key Takeaways
- NeuroSleep utilizes a neuromorphic approach for EEG sleep staging.
- Achieves 74.2% accuracy with only 0.932 million parameters.
- Reduces computational load by approximately 53.6% compared to dense processing.
- Implements a hierarchical inference architecture for effective feature extraction.
- Offers a scalable solution for always-on sleep analysis in resource-constrained environments.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.15888 (eess) [Submitted on 6 Feb 2026] Title:NeuroSleep: Neuromorphic Event-Driven Single-Channel EEG Sleep Staging for Edge-Efficient Sensing Authors:Boyu Li, Xingchun Zhu, Yonghui Wu View a PDF of the paper titled NeuroSleep: Neuromorphic Event-Driven Single-Channel EEG Sleep Staging for Edge-Efficient Sensing, by Boyu Li and 1 other authors View PDF Abstract:Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often computationally prohibitive under tight energy budgets. To address this bottleneck, this paper proposes NeuroSleep, an integrated event-driven sensing and inference system for energy-efficient sleep staging. NeuroSleep first converts raw EEG into complementary multi-scale bipolar event streams using Residual Adaptive Multi-Scale Delta Modulation (R-AMSDM), enabling an explicit fidelity-sparsity trade-off at the sensing front end. Furthermore, NeuroSleep adopts a hierarchical inference architecture that comprises an Event-based Adaptive Multi-scale Response (EAMR) module for local feature extraction, a Local Temporal-Attention Module (LTAM) for context aggregation, and an Epoch-Leaky Integrate-and-Fire (ELIF) module to capture long-term state persistence. Experimental results using subject-independent 5-fold cross-validation on the...
