科研之友
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摘要
Actigraphy 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 NaiveBayes binary classifier for distinguishing between normal and abnormal periodic limb movement indices. We trained the Naive-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 Naive-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.
- 出版日期2017-2
