Actigraphic detection of periodic limb movements: development and validation of a potential device-independent algorithm. A proof of concept study. Issue 9 (13th June 2019)

• Record Type: Journal Article
• Authors: Athavale, Yashodhan; Krishnan, Sridhar; Raissi, Afsaneh; Kirolos, Nardin; Jairam, Trevor; Murray, Brian J; Boulos, Mark I
• Is Part Of: Sleep. Volume 42:Issue 9(2019)
• Publication Date: 2019-06-13
• Subjects: actigraphy -- sleep -- periodic limb movements -- long-term monitoring -- signal processing -- feature extraction -- polysomnography -- machine learning; Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsz117
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: We propose a unique device-independent approach to analyze long-term actigraphy signals that can accurately quantify the severity of periodic limb movements in sleep (PLMS). Methods: We analyzed 6–8 hr of bilateral ankle actigraphy data for 166 consecutively consenting patients who simultaneously underwent routine clinical polysomnography. Using the proposed algorithm, we extracted 14 time and frequency features to identify PLMS. These features were then used to train a Naïve–Bayes learning tool which permitted classification of mild vs. severe PLMS (i.e. periodic limb movements [PLM] index less than vs. greater than 15 per hr), as well as classification for four PLM severities (i.e. PLM index < 15, between 15 and 29.9, between 30 and 49.9, and ≥50 movements per hour). Results: Using the proposed signal analysis technique, coupled with a leave-one-out cross-validation method, we obtained a classification accuracy of 89.6%, a sensitivity of 87.9%, and a specificity of 94.1% when classifying a PLM index less than vs. greater than 15 per hr. For the multiclass classification for the four PLM severities, we obtained a classification accuracy of 85.8%, with a sensitivity of 97.6%, and a specificity of 84.8%. Conclusions: Our approach to analyzing long-term actigraphy data provides a method that can be used as a screening tool to detect PLMS using actigraphy devices from various manufacturers and will facilitate detection of PLMS in an ambulatory setting.