NeuroSleep: Neuromorphic Event-Driven Single-Channel EEG Sleep Staging for Edge-Efficient Sensing
arXiv:2602.15888v1 Announce Type: cross 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 capt
arXiv:2602.15888v1 Announce Type: cross 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 Sleep-EDF Expanded dataset demonstrate that NeuroSleep achieves a mean accuracy of 74.2% with only 0.932 M parameters while reducing sparsity-adjusted effective operations by approximately 53.6% relative to dense processing. Compared with the representative dense Transformer baseline, NeuroSleep improves accuracy by 7.5% with a 45.8% reduction in computational load. By bridging neuromorphic encoding with state-aware modeling, NeuroSleep provides a scalable solution for always-on sleep analysis in resource-constrained wearable scenarios.
Executive Summary
This article proposes NeuroSleep, an event-driven sensing and inference system for energy-efficient sleep staging using EEG data. NeuroSleep utilizes a hierarchical inference architecture that incorporates event-based processing, local feature extraction, context aggregation, and state persistence to achieve a mean accuracy of 74.2% with a significant reduction in computational load. Compared to dense processing, NeuroSleep improves accuracy by 7.5% with a 45.8% reduction in computational load. The system's scalability and efficiency make it suitable for always-on sleep analysis in resource-constrained wearable scenarios. This innovation has the potential to revolutionize sleep monitoring and analysis, particularly in healthcare settings where accurate and continuous monitoring is critical. The authors' approach successfully bridges neuromorphic encoding with state-aware modeling, providing a scalable solution for real-world applications.
Key Points
- ▸ NeuroSleep is an event-driven sensing and inference system for energy-efficient sleep staging.
- ▸ The system utilizes a hierarchical inference architecture with event-based processing and state-aware modeling.
- ▸ NeuroSleep achieves a mean accuracy of 74.2% with a significant reduction in computational load compared to dense processing.
Merits
Strength in Energy Efficiency
NeuroSleep's event-driven approach significantly reduces computational load, making it suitable for resource-constrained wearable scenarios.
Innovative Neuromorphic Encoding
The authors' use of neuromorphic encoding and state-aware modeling provides a scalable solution for always-on sleep analysis.
Improved Accuracy
NeuroSleep achieves a mean accuracy of 74.2%, outperforming dense processing by 7.5%.
Demerits
Technical Complexity
The system's hierarchical inference architecture and event-based processing may be challenging to implement and optimize.
Limited Generalizability
The authors' results are based on a specific dataset, and it is unclear whether NeuroSleep will generalize to other EEG datasets or applications.
Potential for Overfitting
The system's state-aware modeling and event-based processing may lead to overfitting if not carefully regularized.
Expert Commentary
The article's authors have made significant contributions to the field of wearable health monitoring and neural processing and encoding. Their use of event-driven sensing and inference, neuromorphic encoding, and state-aware modeling provides a scalable solution for always-on sleep analysis in resource-constrained wearable scenarios. While the system's technical complexity and potential for overfitting are limitations, the authors' innovative approach has the potential to revolutionize sleep monitoring and analysis. Future work should focus on generalizing NeuroSleep to other EEG datasets and applications, as well as exploring its potential for other domains, such as neurological disorders and cognitive function assessment.
Recommendations
- ✓ Recommendation 1: Future research should focus on generalizing NeuroSleep to other EEG datasets and applications, as well as exploring its potential for other domains, such as neurological disorders and cognitive function assessment.
- ✓ Recommendation 2: The authors should investigate methods for reducing the system's technical complexity and potential for overfitting, such as regularization techniques and model simplification.
