NDLI: Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal

Content Provider	IEEE Xplore Digital Library
Author	Kusmakar, S. Gubbi, J. Yan, B. O'Brien, T.J. Palaniswami, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Melbourne, VIC, Australia (Kusmakar, S.; Gubbi, J.; Palaniswami, M.) \|\| Dept. of Med., Univ. of Melbourne, Melbourne, VIC, Australia (Yan, B.; O'Brien, T.J.)
Abstract	Convulsive psychogenic non-epileptic seizure (PNES) can be characterized as events which mimics epileptic seizures but do not show any characteristic changes on electroencephalogram (EEG). Correct diagnosis requires video-electroencephalography monitoring (VEM) as the diagnosis of PNES is extremely difficult in primary health care. Recent work has demonstrated the usefulness of accelerometry signal taken during a seizure in classification of PNES. In this work, a new direction has been explored to understand the role of different muscles in PNES. This is achieved by modeling the muscle activity of ten different upper limb muscles as a resultant function of accelerometer signal. Using these models, the accelerometer signals recorded from convulsive epileptic patients were transformed into individual muscle components. Based on this, an automated algorithm for classification of convulsive PNES is proposed. The algorithm calculates four wavelet domain features based on signal power, approximate entropy, kurtosis and signal skewness. These features were then used to build a classification model using support vector machines (SVM) classifier. It was found that the transforms corresponding to anterior deltoid and brachioradialis results in good PNES classification accuracy. The algorithm showed a high sensitivity of 93.33% and an overall PNES classification accuracy of 89% with the transform corresponding to anterior deltoid.
Starting Page	582
Ending Page	585
File Size	998399
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318429
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Accelerometers Accuracy Support vector machines Mathematical model Wavelet transforms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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