Abstract
In this paper, we present the concept and the prototype implementation of our novel “Smart Observatory of Involuntary Medical Seizures (SOIMS)”. SOIMS merges Wireless Body Area Networks (WBAN), Internet of Things (IoT) and Machine Learning (ML) as an intelligent platform for the prediction and modelling of involuntary seizures. The prediction process is elaborated with our proposed algorithms, namely, Qualifying Linear Regression Algorithm (QuLRA), Selective Clustering Algorithm (SeCA) and Real Time Clusters Correlation Algorithm (RT2CA). The assessment of the proposed system is validated based on the Physionet ECG patients’ dataset. The implementation of the prototype involves an IoT/WEB proxy security embedded for translation between nodes CoAP/DTLS protocol and Hospital Information System (HIS) HTTP/TLS protocol. Our proposed solution outperforms existing schemes in the literature at different levels, namely: a) it uses a hierarchical combination of machine learning and prediction algorithms; b) it is open-source, interoperable and user friendly; c) it is a secured prototype implementation; and d) it reaches a higher rate of accuracy according to the correlation criterion.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
