Medicine (RMH) - Research Publications

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Author: GUBBI LAKSHMINARASIMHA, JAYAVARDHANA × Author: Sridhara Rao, Aravinda × Type: Journal Article ×

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    Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device
    Gubbi, J ; Kusmakar, S ; Rao, AS ; Yan, B ; O'Brien, T ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-07)
    Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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    Motor recovery monitoring using acceleration measurements in post acute stroke patients
    Gubbi, J ; Rao, AS ; Fang, K ; Yan, B ; Palaniswami, M (BMC, 2013-04-16)
    BACKGROUND: Stroke is one of the major causes of morbidity and mortality. Its recovery and treatment depends on close clinical monitoring by a clinician especially during the first few hours after the onset of stroke. Patients who do not exhibit early motor recovery post thrombolysis may benefit from more aggressive treatment. METHOD: A novel approach for monitoring stroke during the first few hours after the onset of stroke using a wireless accelerometer based motor activity monitoring system is developed. It monitors the motor activity by measuring the acceleration of the arms in three axes. In the presented proof of concept study, the measured acceleration data is transferred wirelessly using iMote2 platform to the base station that is equipped with an online algorithm capable of calculating an index equivalent to the National Institute of Health Stroke Score (NIHSS) motor index. The system is developed by collecting data from 15 patients. RESULTS: We have successfully demonstrated an end-to-end stroke monitoring system reporting an accuracy of calculating stroke index of more than 80%, highest Cohen's overall agreement of 0.91 (with excellent κ coefficient of 0.76). CONCLUSION: A wireless accelerometer based 'hot stroke' monitoring system is developed to monitor the motor recovery in acute-stroke patients. It has been shown to monitor stroke patients continuously, which has not been possible so far with high reliability.