CSEIT2062128 | Accepted : 20 April 2020 | Published : 29 April 2020 | March-April-2020 [ 6 (2) : 504-508] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2020 IJSRCSEIT | Volume 6 | Issue 2 | ISSN : 2456-3307 DOI : https://doi.org/10.32628/IJSRCSEIT 504 An Effective Approach for Sleep Stage Classification Based on PSG Recordings Mayuri A. Rakhonde, Prof. Ravi V. Mante, Dr. Kishor P. Wagh Master of Technology Scholar, CSE, GCOE, Amravati, Amravati, Maharashtra, India Assistant Professor, Computer Science & Engineering, GCOE, Amravati Amravati, Maharashtra, India Assistant Professor, Information Technology, GCOE, Amravati, Amravati, Maharashtra, India ABSTRACT A person spend his one-third of life in sleep. So, paying attention on sleep is necessary at present times. Appropriate scoring of sleep stages is essential part in recognition of particular sleep disorder. Sleep stage classification is the process of categorizing polysomnographic (PSG) recordings into different classes. PSG contains EEG, EMG, EOG signals. In proposed methodology, Power spectral density is used to extract power features of EEG and EMG signals. A machine learning model of Stochastic Gradient Descent algorithm is used for classifying extracted features into multi-class sleep stages. Keywords: Polysomnography, Sleep Stage, Stochastic Gradient Descent, Power Spectral Density I. INTRODUCTION Sleep is the essential part of life. Required amount of sleep is necessary for human being. Sleep has tremendous effect on our physical health, mental health and quality of life. Sleep analysis is the important factor for recognition of sleep disorders[1]. The common problem is occurred in people is having trouble in sleeping. Not getting sufficient sleep leads to the physical and mental illness. Sleep disorders includes sleep apnea, insomnia, narcolepsy, etc. And for identification of sleep disorders sleep analysis is important[2]. Sleep stage classification is the process of classifying physiological signals into different stages. Basically there are two types of sleep stages named as Rapid Eye Movement(REM) and Non-rapid Eye Movement(NREM). Sleep is a cyclical process. Each sleep cycle contains three NREM stages and one REM stage and it continues[3]. Typically, a sleeper goes through 4 to 6 sleep cycles each about 90 to 120 minutes. As specified by American Academy of Sleep Medicine(AASM), sleep stages are classified into five stages named as Awake, Sleep Stage 1(NREM1), Sleep Stage 2(NREM2), Sleep Stage 3(NREM3) and REM. In sleep stage 1, a person is in light sleep and move towards sleep stage 2. In sleep stage 2, a person is in deep relaxation state and move towards sleep stage 3. In sleep stage 3 a person is in deep sleep where no eye movement exist. In REM sleep stage, the movement of eyes occurred and brain gets active. In this stage dreams occurs. And cycle continues[4]. Physiological information is recorded for sleep analysis which contains continuous time signals and it is called as Polysomnography (PSG). Polysomnographic recordings includes Electroencephalogram (EEG), electrooculograms (EOG), electromyograms (EMG), electrocardiograms (ECG), oxygen saturation and respiration. PSG signals are divided into short period of time called sleep epoch. Each sleep epoch is of 30- second. Among all of these signals, EEG signal and EMG signals has more significance. EEG signals has different characteristics at different sleep stages in