A comparative investigation of PSG signal patterns to classify sleep disorders using machine learning techniques

Wongsirichot, Thakerng; Hanskunatai, Anantaporn

doi:10.1007/978-3-319-22180-9_50

A comparative investigation of PSG signal patterns to classify sleep disorders using machine learning techniques

Author(s)

Wongsirichot, Thakerng

Hanskunatai, Anantaporn

Date Issued

January 1, 2015

Type

Conference Paper

DOI

10.1007/978-3-319-22180-9_50

Abstract

Patients with Non-Communicable Diseases (NCDs) are increasing around the globe. Possible causes of the NCDs are continuously being investigated. One of them is a sleep disorder. In order to detect specific sleep disorders, the Polysomnography (PSG), is necessary. However, due to the lack of the PSG in many hospitals, researchers attempt to discover alternative approaches. This article demonstrates comparisons of sleep disorder classifications using machine learning techniques. Three main machine learning techniques have been compared including Classification And Regression Tree (CART), k-Mean Clustering (KMC) and Support Vector Machine (SVM). The SVM achieves the best classification results in NREM-1 and NREM-2. The CART performs superior in NREM-3 and REM. Implications in terms of medical diagnosis, there are two main selected features, SaO2 and Pulse, based on the CART in all of the sleep stages. The features may be pieces of evidences to predict various types of sleep disorders.

Citation

Lecture Notes in Computer Science, 9225, 510-521, 2015

Subjects

CART

Classification

K-Mean clustering

Sleep disorders

SVM