Shitanshu Kusmakar, Jayavardhana Gubbi, Aravinda S. Rao, Bernard Yan, Terence J O’Brien, Marimuthu Palaniswami
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 586-589
Publication year: 2015

Abstract

A seizure is caused due to sudden surge of electrical activity within the brain. There is another class of seizures called psychogenic non-epileptic seizure (PNES) that mimics epilepsy, but is caused due to underlying psychology. The diagnosis of PNES is done using video-electroencephalography monitoring (VEM), which is a resource intensive process. Recently, accelerometers have been shown to be effective in classification of epileptic and non-epileptic seizures. In this work, we propose a novel feature called histogram of oriented motion (HOOM) extracted from accelerometer signals for classification of convulsive PNES. An automated algorithm based on HOOM is proposed. The algorithm showed a high sensitivity of (93.33%) and an overall accuracy of (80%) in classifying convulsive PNES.