REAL- TIME MINING TECHNIQUES - LATENCY MITIGATION ALGORITHMS FOR SEIZURE DETECTION

Abstract

Speed is the name of the game in today’s world. Speed of change drives our everyday’s life, and hence in this ever changing and agile environment it has become critical for systems to extract useful information in real time. Techniques used in sifting through streaming data to evaluate and extract useful information are called real time data analytics. The ability of these techniques to paint the picture of the changes as and when they happen makes them a vital tool in analysis and visualization of medical data especially of the neurological nature as it is very dynamic. The fact that all the evaluation and analysis needs to be done on the go make the challenges enormous. However, this very challenge if successfully met can be used to analyze neurological data related to disorders such as epilepsy, Alzheimer’s, strokes and even headaches. According to WHO these ailments affects close to I billion people worldwide. However it is extremely difficult for human analysts to collate, evaluate, manage and assimilate large volumes of data related to such alements. This is where real time data analytic can step in to meet the challenges. Already we have CAD (Computer-Aided Diagnosis) that automatically detects neurological abnormalities like seizures using medical big data. The next big step will be to bring in real time analytics that have computational algorithms with the capability of predicting periods of high seizure probability with reliability with minimum latency. This not only helps patients from enjoying life threatening activities during such periods, but would also regulate medication to be used only during high seizure probability windows. This paper attempts to highlight a big data processing framework under the cloud computing platform for real time seizure prediction with the minimum of time delays.