  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.
