A Binary Sleep Classification of BCG Signal using Random Search-Optimized Random Forest

다국어 초록 (Multilingual Abstract)

Sleep has an important role in human daily activities. Lack of sleep might cause fatigue, loss of focus, and furthermore lead to various sleep-related diseases. Research on human sleep has been conducted in many ways starting from high-cost and complex process (polysomnography) to low-cost and simpler process such as actigraphy. Several different methods were used during the experiment such as (EEG), electrocardiography (ECG), and ballistocardiography (BCG) to automate the process for sleep analysis. A machine learning algorithm called random forest has been used to classify awake and sleep states. Additionally, the random forest algorithm was optimized with random search to improve the classification performances. The result showed the accuracy of the optimized random forest was higher (81.35 %) than the result without using the optimization method (79.82 %).

목차 (Table of Contents)

• Abstract
• I. Introduction
• II. Method
• Ⅲ. Results
• Ⅳ. Conclusion
• References