Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques - Soraya Khanmohmmadi; Toktam Khatibi; Golnaz Tajeddin; Elham Akhondzadeh; Amir Shojaee (Scientific Reports)
Abstract Adequate sleep is crucial for maintaining a healthy lifestyle, and its deficiency can lead to various sleep-related disorders. Identifying these disorders early is essential for effective treatment, which traditionally relies on polysomnogram (PSG) tests. However, diagnosing sleep disorders with high accuracy based solely on electroencephalogram (EEG) signals, rather than using various signals in a complex PSG, can reduce the time and cost required, and the need for specialized signal devices, as well as increase accessibility and usability. Previous studies have focused on traditional machine learning (ML) methods such as K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), and ensemble learning methods for sleep disorders analysis. However, these models require manual methods for feature extraction, and the prediction accuracy greatly depends on the type of feature extracted. Additionally, the EEG signal datasets are small and heterogeneous, challenging traditional machine learning and deep learning models. The study proposes an innovative multi-task learning convolutional neural network with a partially shared structure that uses frequency-time images generated from EEG signals to address these limitations. The proposed technique makes two predictions using non-shared features from time-frequency images created through Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT), one prediction from shared features, and the final prediction is a combination of these three predictions. The weights for this combination were optimized using the genetic algorithm and the Q-learning algorithm, aiming to minimize loss and maximize accuracy. The study utilizes a dataset involving 26 participants to examine the impact of Partial Sleep Deprivation (PSD) on EEG recordings. The outcomes demonstrated that the multi-task learning model using these two optimization methods, attained 98% accuracy on the test data for predicting partial sleep deprivation. This automated diagnostic model is an efficient supporting tool for rapidly and effectively diagnosing sleep disorders. It swiftly and precisely evaluates sleep data, minimizing the time and effort required by the patient and the physician.