Sleep Stage Classification: Scalability Evaluations of Distributed Approaches

Processing and analyzing of massive clinical data
are resource intensive and time consuming with
traditional analytic tools. Electroencephalogram
(EEG) is one of the major technologies in detecting
and diagnosing various brain disorders, and
produces huge volume big data to process. In this
study, we propose a big data framework to diagnose
sleep disorders by classifying the sleep stages from
EEG signals. The framework is developed with
open source SparkMlib Libraries. We also tested
and evaluated the proposed framework by
measuring the scalabilities of well-known
classification algorithms on physionet sleep records.

 

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