کنفرانس مهندسی برق ایران

صفحه اصلی / سی و یکمین کنفرانس بین المللی مهندسی برق

An Ensemble Model for Sleep Stages Classification

نویسندگان :

Sahar Hassanzadeh Mostafaei¹ Jafar Tanha² Amir Sharafkhaneh³ Zohair Hassanzadeh Mostafaei⁴ Mohammed Hussein Ali Al-jaf⁵ Alireza Fakhim babaei⁶

1- دانشگاه تبریز 2- دانشگاه تبریز 3- Baylor College of Medicine 4- دانشگاه آزاد اسلامی واحد تبریز 5- دانشگاه تبریز 6- دانشگاه تبریز

کلمات کلیدی :

Ensemble Machine Learning،Weighted Averaging،Sleep Staging،Biological Signals،Polysomnography test،Sleep Heart Health Study

چکیده :

One of the most important parts of health is the quality of sleep. Sleep disorders can be diagnosed using a standard sleep test called polysomnography. Sleep staging is a task in the field of sleep study that determines sleep cycles. In recent years, machine-learning approaches are used to classify sleep stages using biological signals derived from PSGs. In this study, we propose an ensemble machine-learning method for sleep stage classification. We use nine biological signals from the SHHS1 dataset, including two-channel EEG, two-channel EOG, ECG, EMG, abdominal, thorax, and airflow signals. Then we extract different features such as RRI and RPE from the ECG signals and frequency features from EEG signals. Finally, we develop an ensemble model using Light Gradient Boosting (LGB) and eXtreme Gradient Boosting (XGB) algorithms. In the end, we evaluate the proposed ensemble method using different metrics and compare its performance with other state-of-the-art techniques. The results of the proposed method show that it achieves an overall accuracy of 0.8951 in the five-class classification of sleep stages including Wake, N1, N2, N3, and REM.

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