Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features

Electronics

Koichi Tanno¹  Hiroki Tamura²  EditaRosana Widasari³ 

[1] Department of Electrical and System Engineering, University of Miyazaki, Miyazaki 889-2192, Japan;Department of Environmental Robotics, University of Miyazaki, Miyazaki 889-2192, Japan;Interdisciplinary Graduate School of Agriculture and Engineering, Department of Material and Informatics, University of Miyazaki, Miyazaki 889-2192, Japan;

关键词: sleep disorders in the elderly;    ecg signal;    sleep stage;    dtb-svm;    sleep quality;    ensemble of bagged tree;   

DOI  :  10.3390/electronics9030512

来源: DOAJ

【 摘 要 】

Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG.

【 授权许可】

Unknown