mSleep: Multistage Human Sleep Behavior Prediction Using Enhanced MaxViT Convolution-Transformer

The intersection of deep learning (DL) techniques and electroencephalogram (EEG) signals to predict multiple human sleep behaviors is integrated in this study. In fact, the detection of multiple sleep behaviors is a critical task for mental health professionals. To address this issue, an advanced analytical framework based on DL is developed in this study. After pre-processing the signals, this study developed a novel approach on 1D-EEG signals, which are converted to the series of (3x times) stack images using ensemble of short-time Fourier transform (STFT), continuous wavelet transform (CWT), and mel-frequency cepstral coefficients (MFCCs) techniques. Later one, the improved MaxViT model is improved to extract features from 3D-cum-stacked signal images. To capture the temporal dynamics of sleep architecture, we then implemented a gated recurrent unit (GRU) model. The AdaBoost classifier was finally applied for the classification of multiclass sleep behaviors. The performance of the proposed system (mSleep) is validated by using PhysioNet dataset. Our study introduces a novel multi-modal feature extraction approach by combining STFT, CWT, and MFCCs to create 3D-stacked EEG representations, enabling comprehensive temporal-spectral analysis. We develop a hybrid DL pipeline integrating Enhanced MaxViT for feature extraction, GRU for temporal modeling, and AdaBoost for multi-class classification, achieving superior accuracy and generalization. Additionally, we address class imbalance using SMOTE and enhance model interpretability through AdaBoost, ensuring clinical applicability and robust performance on the PhysioNet Sleep-EDF dataset. The mSleep system has broad implications for sleep research and neural signal processing, as it provides information that can be useful in the design of individualized therapy plans or technological improvement plans. The code and dataset can be downloaded from https://www.github.com/qaisar256/sleepdisorder1.0/.