Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency
Shahab Haghayegh1,2, Kun Hu1,2, Katie Stone3
1Harvard Medical School, Boston, MA, United States.
Journal of Medical Internet Research
|February 10, 2023
Summary
A new convolutional neural network, SleepInceptionNet, enables single-epoch sleep classification using just one electroencephalogram (EEG) channel. This advance simplifies automated sleep staging for potential real-time applications.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Artificial Intelligence
Background:
- Current automated sleep staging often requires multimodal data and multiple epochs.
- This limits real-time analysis and application in free-living conditions.
Purpose of the Study:
- To develop and evaluate a novel convolutional neural network (CNN) for single-epoch sleep classification using single-channel electroencephalogram (EEG) data.
- To systematically assess the impact of various EEG preprocessing techniques and CNN architectures on sleep staging performance.
Main Methods:
- Evaluated 883 participants' polysomnography data (937,975 epochs).
- Tested raw EEG, power spectral density, continuous wavelet transform, and short-time Fourier transform inputs.
- Compared 7 sequential deep neural networks and 16 image classifier CNNs, including InceptionV3.
Main Results:
- The SleepInceptionNet model, utilizing continuous wavelet transform on central EEG channels fed into InceptionV3, achieved a Cohen κ of 0.705 (SD 0.077).
- This demonstrates high agreement with the gold standard polysomnography.
Conclusions:
- SleepInceptionNet facilitates real-time automated sleep staging with single-channel EEG.
- This innovation holds potential for on-demand interventions during specific sleep stages in free-living settings.
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