Sleep Disorder Diagnosis Through Complex-MorletWavelet Representation Using Bi-GRU and Self- Attention

Sleep disorders pose notable health risks, impacting memory, cognitive performance, and overall well-being. Traditional polysomnography (PSG) used for sleep disorder diagnosis are complex and inconvenient due to complex multi- class representation of signals. This study introduces an automated sleep-disorder-detection method using electrooculography (EOG) and electroencephalography (EEG) signals to address the gaps in automated, real-time, and noninvasive sleep-disorder diagnosis. Traditional methods rely on complex PSG analysis, whereas the proposed method simplifies the involved process, reducing reliance on cumbersome equipment and specialized settings. The preprocessed EEG and EOG signals are transformed into a two-dimensional time-frequency image using a complex-Morlet-wavelet (CMW) transform. This transform assists in capturing both the frequency and time characteristics of the signals. Afterwards, the features are extracted using a bidirectional gated recurrent unit (Bi-GRU) with a self-attention layer and an ensemble-bagged tree classifier (EBTC) to correctly classify sleep disorders and very efficiently identify them. The overall system combines EOG and EEG signal features to accurately classify people with insomnia, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid-eye-movement (RBD), sleep behavior disorder (SDB), and healthy, with success rates of 99.7%, 97.6%, 95.4%, 94.5%, 96.5%, 98.3%, and 94.1%, respectively. Using the 10-fold cross-validation technique, the proposed method yields 96.59% accuracy and AUC of 0.966 with regard to classification of sleep disorders into multistage classes. The proposed system assists medical experts for automated sleep-disorder diagnosis.