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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Reliable electroencephalography (EEG) seizure detection is crucial for clinical monitoring and wearable technology.
  • Spiking neural networks (SNNs) offer event-driven processing suitable for real-time applications, but achieving competitive performance with low complexity is challenging.

Purpose of the Study:

  • To introduce a novel hybrid spike encoding scheme and spiking neural network architectures for efficient and accurate real-time EEG seizure detection.
  • To evaluate the performance and computational practicality of these models for continuous inference.

Main Methods:

  • Developed a hybrid spike encoding scheme combining Delta-Sigma and stochastic rate representations.
  • Designed two spiking architectures: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN with depthwise-separable convolutions and temporal self-attention.
  • Tested models on the CHB-MIT EEG dataset for seizure detection accuracy, F1-score, and false alarm rates.

Main Results:

  • The HybridSNN achieved 91.8% accuracy and an F1-score of 0.834.
  • The ConvSNN improved performance to 94.7% accuracy and an F1-score of 0.893.
  • Both models demonstrated low inference latencies (approx. 1.2 ms per 0.5s window) and low daily false alarm rates (0.82 for HybridSNN, 0.62 for ConvSNN).

Conclusions:

  • Hybrid spike encoding enables spiking architectures of controlled complexity to achieve seizure detection performance comparable to larger deep learning models.
  • The developed models are suitable for real-time clinical and wearable EEG monitoring due to their low latency and high accuracy.