Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

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Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data.Manual sleep staging requires human expertise, which is occasionally prone to error and variation.In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification.In this paper, a hybrid deep learning model is proposed for classifying sleep and solo jec 10 wake states based on a single-channel electroencephalogram (EEG) signal.

The model combines an artificial neural network (ANN) and ds durga hand soap a convolutional neural network (CNN) trained using mixed-input features.The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch.The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded.The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.