Medicine (RMH) - Research Publications

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http://hdl.handle.net/11343/193

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End date: 2022 × Type: Conference Paper × Author: GUBBI LAKSHMINARASIMHA, JAYAVARDHANA ×

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    A Pilot Study on the use of Accelerometer Sensors for Monitoring Post Acute Stroke Patients
    Gubbi, J ; Kumar, D ; Rao, AS ; Yan, B ; Palaniswami, M (IEEE, 2013)
    The high incidence of stroke has raised a major concern among health professionals in recent years. Concerted efforts from medical and engineering communities are being exercised to tackle the problem at its early stage. In this direction, a pilot study to analyze and detect the affected arm of the stroke patient based on hand movements is presented. The premise is that the correlation of magnitude of the activities of the two arms vary significantly for stroke patients from controls. Further, the cross-correlation of right and left arms for three axes are differentiable for patients and controls. A total of 22 subjects (15 patients and 7 controls) were included in this study. An overall accuracy of 95.45% was obtained with sensitivity of 1 and specificity of 0.86 using correlation based method.
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    Classification of Convulsive Psychogenic Non-epileptic Seizures Using Histogram of Oriented Motion of Accelerometry Signals
    Kusmakar, S ; Gubbi, J ; Rao, AS ; Yan, B ; O'Brien, TJ ; PALANISWAMI, M (IEEE, 2015)
    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.