Comparative Study of Brain Signals for Early Detection of Sleep Disorder Using Machine and Deep Learning Algorithm

Sleep disorders are a common health concern affecting a significant portion of the global population. Early detection and intervention are crucial for preventing the detrimental impact of sleep disorders on an individual’s physical and mental health. This study presents a comparative analysis of the effectiveness of machine and deep learning algorithms in the early detection of sleep disorders using brain signals. The research leverages electroencephalogram (EEG) data collected from individuals with varying sleep disorders. We extract relevant features from EEG signals, including spectral, temporal, and spatial features, which provide insight into the brain’s activity during different sleep stages. These features serve as input to both machine learning and deep learning models. In the machine learning approach, we employ classical algorithms such as Support Vector Machines (SVM), Random Forest (RF), and K-nearest neighbors (KNN). In the deep learning approach, we use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process the EEG data. The study evaluates the performance of these algorithms in terms of accuracy, sensitivity, specificity, and precision while considering different sleep disorder categories, including insomnia, sleep apnea, and narcolepsy. Our findings indicate that deep learning models, particularly CNNs and RNNs, outperform traditional machine learning algorithms in accurately identifying sleep disorders. The deep learning models demonstrate the ability to capture intricate patterns and dependencies within EEG data, leading to more accurate and early detection.