Actigraphic detection of periodic limb movements: development and validation of a potential device-independent algorithm. A proof of concept study

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.

Actigraphy Signal Analysis System for Sleep and Wake Studies

“…• Connecting devices using networking protocols to the internet • Data transmission and exchange from sensors • Store and analyze incoming data using DBMS (database management systems), cloud computing and big data modules • Enable semantic and middleware applications for inter-and intra-device operability and connectivity. 6 3. Application Layer: This layer is usually visible to the user in the form of service-based, event-based and user-specific applications.…”

“…Some studies were also focused on assessing daily activity in subjects experiencing Parkinson's and Alzheimer's disease [5,6,[55][56][57][58][59][60][61][62][63][64][65][66].…”

“…Ideally this process must be done automatically and with great speed and accuracy, considering the large amounts of data that need to be processed. In many prior studies, machine learning methods have been implemented to detect the type and level of human activity captured using actigraphs [5][6][7]92]. In most of these works, features were extracted from the raw signals at pre-defined times and then fed into a learning tool for classification.…”

Accelerometer-measured physical activity and its impact on sleep quality in patients suffering from restless legs syndrome

“…Regarding sleep quality measurements, accelerometers can be placed at the ankle or arches of both feet to detect limb movements during sleep. The advantages of accelerometry over polysomnography include the possibility to record sleep parameters over periods of more than 24 h without the need to replace the sensors or batteries [31]. Furthermore, an individual's sleep quality can be determined while sleeping in their natural home environment [7].…”

“…Furthermore, an individual's sleep quality can be determined while sleeping in their natural home environment [7]. The accuracy of accelerometry, however, is restricted in comparison to polysomnography [31].…”