Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

• Published in: Computational and mathematical methods in medicine Vol. 2016; no. 2016; pp. 1 - 7
• Main Authors: Umut, İlhan, Çentik, Güven
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2016
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Cluster Analysis; Electromyography; Female; Humans; Image Processing, Computer-Assisted; Leg - physiopathology; Machine Learning; Male; Middle Aged; Models, Statistical; Movement; Polysomnography; Reproducibility of Results; Restless Legs Syndrome - diagnostic imaging; Retrospective Studies; Signal Processing, Computer-Assisted; Software
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2016/2041467

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K -nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K -nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.