Melatonin Pattern: A New Method for Machine Learning-Based Classification of Sleep Deprivation

Abstract

Background: Pattern recognition and machine learning-based classification approaches are frequently used, especially in the health field. In this research, a new feature extraction model inspired by the melatonin hormone (sleep hormone) and named MelPat (melatonin pattern) has been developed. The developed model has been tested on an open access dataset. Materials and Methods: An open access sleep deprivation electroencephalography (EEG) dataset was tested to evaluate the MelPat method. There are two classes in the dataset. These are (a) sleep deprivation (SD) and (b) healthy control (HC) groups, respectively. In this study, EEG signals were divided into 15 s segments, thus obtaining 1377 SD and 1378 HC samples. In the next phase of the research, a new feature extraction model was proposed, and this model was named MelPat as it was inspired by the melatonin hormone. Additionally, the feature vector was expanded using the statistical moment approach. In the signal decomposition phase of the model, the Tunable Q-Wavelet Transform (TQWT) method was used. Thus, the signal was decomposed into sub-bands, and feature extraction was applied to each band. Neighborhood Component Analysis (NCA) and Chi2 methods were used together to reduce the dimension of the feature vector and select the most significant features. In this phase, the most significant features from both feature selection algorithms were combined, and the final feature vector was obtained. In the classification phase of the model, the Support Vector Machine (SVM) algorithm, which is a shallow classifier, was used. The dataset used in the research has 61 channels. Therefore, after obtaining channel-based results, the iterative majority voting (IMV) algorithm was applied to achieve higher classification performance and generalize the results, and the most accurate results were automatically selected. Results: With the proposed MelPat algorithm, a high classification success of 97.71% was achieved on the open access sleep deprivation dataset. Conclusions: The obtained results show that the MelPat-based new classification approach is highly effective on the dataset collected for SD detection. Moreover, the fact that the proposed method is inspired by the melatonin chemical, which is the sleep hormone, makes the method attractive and ironic.