A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques (original) (raw)
2021, SN Computer Science
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Sleep-related disorders have one of the challenging health issues across the world. Identifying sleep irregularities will be one important primary step of treatment for any types of sleep diseases. To perform day-to-day activities, proper healthy sleep is required for an individual’s life. This also plays one of the vital roles in human life to maintain the proper health both physically and mentally wise and which alternatively smoothly maintain our quality of life. The main objective of this research work is to propose a simple and efficient automated sleep stage classification methods based on a single channel of electroencephalogram (EEG) signal using machine learning techniques. Both time and frequency domain features are applied for that analysis of sleep quality and classifying the sleep stages for identification of sleep abnormality during sleep at night. Total 28 features are extracted from 750 epochs with 3000 sample points through the C3-A2 channel of EEG signal. We have c...
2020
INTRODUCTION: Sleep stage classification is an important task for the timely diagnosis of sleep-related disorders, which are one the most common indicator of illness. OBJECTIVE: An automated sleep scoring implementation with promising generalization capabilities is presented, aiding towards eliminating the tedious procedure of manual sleep scoring. METHODS: Two Electroencephalogram (EEG) channels and the Electrooculogram (EOG) channel are utilized as inputs for feature extraction both in the time and frequency domain, while temporal feature changes are utilized in order to capture contextual information of the signals. An ensemble tree-based and a neural network approach are presented at the classification process. RESULTS: A total of 66 subjects belonging to three different groups (healthy, placebo, drug intake) were included in the study. The tree-based classification method outperforms the neural network at all cases. CONCLUSION: State of the art results are achieved, while it is...
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