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Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase

Mingzhe Sun1, Aaron Zhou1, Naize Yang1

  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada M5S 2E4.

IEEE International Symposium on Circuits and Systems Proceedings. IEEE International Symposium on Circuits and Systems
|April 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel closed-loop sleep modulation system using a lightweight deep learning model on an FPGA. It improves sleep stage classification accuracy, overcoming limitations of wired systems and enhancing sleep disorder treatments.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Closed-loop sleep modulation shows promise for treating sleep disorders but faces challenges.
  • Wired connections and limited real-time sleep stage classification hinder current systems.
  • Developing non-invasive, accurate, and portable sleep modulation technology is crucial.

Purpose of the Study:

  • To develop a novel, on-device closed-loop sleep modulation system.
  • To overcome limitations of wired instrumentation and improve sleep stage classification accuracy.
  • To enable enhanced sleep benefits and treatment for sleep disorders.

Main Methods:

  • Developed a lightweight deep learning (DL) model for sleep stage classification using single-channel EEG.
  • Accelerated the DL model using a low-power field-programmable gate array (FPGA) for on-device processing.
  • Employed convolutional neural networks (CNNs) and a bidirectional long-short-term memory (LSTM) network with 8-bit quantization.

Main Results:

  • Achieved state-of-the-art sleep stage classification accuracy of 85.8% and a F1-score of 79% on a public dataset.
  • Demonstrated the DL model's potential for generalization across different channels and input data lengths.
  • Successfully demonstrated closed-loop in-phase auditory stimulation on a test bench.

Conclusions:

  • The developed system overcomes key barriers in closed-loop sleep modulation, enabling portable and effective sleep treatments.
  • The lightweight DL model on FPGA offers efficient and accurate real-time sleep stage classification.
  • This technology paves the way for advanced, personalized sleep modulation therapies.