Periodic Limb Movements Detection through Actigraphy Signal Analysis

Periodic limb movements (PLM) are caused by a wide range of medical conditions and medication exposures and may increase cardiovascular morbidity. Therefore, predicting elevated PLMs in the absence of restless legs syndrome remains an important clinical challenge. Accurately PLM detection is perform...

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Bibliographic Details
Published in:	2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) pp. 1 - 5
Main Authors:	Zaretskiy, A.P., Mityagin, K.S., Tarasov, V.S., Moroz, D.N.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2019
Subjects:	actigraphy classification Digital filters Feature extraction filtering Frequency response linear discriminant analysis (LDA) Low-pass filters non-contact monitoring periodic limb movement (PLM) Signal processing algorithms Sleep
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Summary:	Periodic limb movements (PLM) are caused by a wide range of medical conditions and medication exposures and may increase cardiovascular morbidity. Therefore, predicting elevated PLMs in the absence of restless legs syndrome remains an important clinical challenge. Accurately PLM detection is performed by advanced signal analysis with feature extraction from actigraphic recordings. These features aided in better representation of the periodic limb movement activity and were processed by linear discriminant analysis (LDA) to classify normal and abnromal PLM. The proposed signal processing algorithm have relatively high performance and faciliate identification of limb movement.
DOI:	10.1109/FarEastCon.2019.8934102