Sleep Stage Classification: Scalability Evaluations of Distributed Approaches
This work addresses the challenge of handling massive clinical EEG data for diagnosing sleep disorders, but it is incremental as it applies existing methods to a new framework.
The authors tackled the problem of processing large-scale EEG data for sleep stage classification by proposing a big data framework using Spark MLlib, and they evaluated its scalability with existing classification algorithms on the PhysioNet dataset.
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.