Advanced signal analysis for the detection of periodic limb movements from bilateral ankle actigraphy

Abstract

SUMMARYActigraphy can assist in the detection of periodic limb movements in sleep. Although several actigraphs have been previously reported to accurately detect periodic limb movements, many are no longer available; of the existing actigraphs, most sample too infrequently to accurately detect periodic limb movements. The purpose of this study was to use advanced signal analysis to validate a readily available actigraph that has the capability of sampling at relatively high frequencies. We simultaneously recorded polysomnography and bilateral ankle actigraphy in 96 consecutive patients presenting to our sleep laboratory. After pre-processing and conditioning, the bilateral ankle actigraphy signals were then analysed for 14 simple time, frequency and morphology-based features. These features reduced the signal dimensionality and aided in better representation of the periodic limb movement activity in the actigraph signals. These features were then processed by a Na€ ıve-Bayes binary classifier for distinguishing between normal and abnormal periodic limb movement indices. We trained the Na€ ıve-Bayes classifier using a training set, and subsequently tested its classification accuracy using a testing set. From our experiments, using a periodic limb movement index cut-off of 5, we found that the Na€ ıve-Bayes classifier had a correct classification rate of 78.9%, with a sensitivity of 80.3% and a specificity of 73.7%. The algorithm developed in this study has the potential of facilitating identification of periodic limb movements across a wide spectrum of patient populations via the use of bilateral ankle actigraphy.

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

“… Routine of children diagnosed with autistic spectral disorder and ADHD (attention deficit hyperactivity disorder) [ 20 , 21 ]. Estimating the severity of sleep related movement-disorders such as periodic limb movements (PLMs) [ 7 , 22 , 23 ]. Therapeutic rehabilitation of joint disabilities in war veterans [ 24 , 25 ].…”

“…Estimating the severity of sleep related movement-disorders such as periodic limb movements (PLMs) [ 7 , 22 , 23 ].…”

“…A variety of actigraphs are currently available in the market (see Figure 3 ), and they are usually worn on wrist, waist or lower ankles for capturing human motor activity [ 27 ]. Typically, an actigraph is able to capture motion data with a sampling frequency in the range of 16–3200 Hz, coupled with an A-to-D quantization of 6–16 bits per sample, depending on the manufacturer [ 7 , 12 , 27 , 28 ] .…”

“…In recent times, many clinical studies have been conducted to explore the validity of using wearables for physiological data analysis for disease or disorder detection. For example, accelerometer-based wearables have been used to study daily activity monitoring in individuals suffering from neuromuscular disorders, and validate their outputs with clinical standards [ 7 ].…”

Calibrating actigraphy to improve sleep efficiency estimates

“… Ankle site was chosen to allow investigation of the detectability of periodic leg movements and in particular to compare anterior tibialis EMG with accelerometry for measuring limb myoclonus (cf. Athavale et al ., ). …”