The design & fabrication of an epileptic seizure detection watch

dc.contributor.authorKyei, Samantha
dc.date.accessioned2020-05-14T02:09:53Z
dc.date.available2020-05-14T02:09:53Z
dc.date.issued2019-04
dc.descriptionCapstone submitted to the Department of Engineering, Ashesi University in partial fulfilment of the requirements for the award of Bachelor of Science degree in Electrical & Electronic Engineering, April 2019.en_US
dc.description.abstractThis paper describes the design and fabrication of an epileptic seizure detection watch for the timely detection of Generalized Tonic-Clonic (GTC) seizures; using skin conductance (SC) signals. The watch’s circuit was designed in EasyEDA and implemented on a Breadboard to showcase the dispatch of a seizure event alert to a phone via a Bluetooth module; in the event of an ongoing seizure and vice versa. Due to the unavailability of SC signal databases, Electroencephalography (EEG) signals, acquired from a physiological database known as PhysioNet were used in showcasing the signal processing of incoming SC signals, temporal and spectral feature extraction of these signals, and the classification of these signals using a trained machine learning algorithm. Twenty-five machine learning algorithms provided by the MATLAB Classification Learner App were trained using 80 EEG signals (both seizure and non-seizure) and only two algorithms, namely the Medium Tree and Linear Support Vector Machine (SVM) had the highest training prediction accuracy. However, in determining their prediction accuracy with two different data sets, the Medium Tree model had the highest cumulative prediction accuracy of 76.7%; as compared to the Linear SVM model which had a cumulative prediction accuracy of 73.3%. Based on these results, the Medium Tree model was recommended as a good seizure detection algorithm to prevent fatal and non-fatal injuries; and even Sudden Unexpected Death in Epilepsy (SUDEP).en_US
dc.description.sponsorshipAshesi Universityen_US
dc.identifier.urihttp://hdl.handle.net/20.500.11988/551
dc.language.isoenen_US
dc.publisherAshesi Universityen_US
dc.subjectepileptic seizureen_US
dc.subjectdetection watchen_US
dc.subjectseizure detection watchen_US
dc.subjectGeneralized Tonic-Clonic seizureen_US
dc.subjectEasyEDAen_US
dc.subjectElectroencephalographyen_US
dc.subjectClassification Learner Appen_US
dc.subjectSupport Vector Machineen_US
dc.titleThe design & fabrication of an epileptic seizure detection watchen_US
dc.typeCapstone projecten_US

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