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Electroencephalography (EEG) is an extensively-used and well-studiedtechnique in the field of medical diagnostics and treatment for braindisorders, including epilepsy, migraines, and tumors. The analysis andinterpretation of EEGs require physicians to have specialized training, whichis not common even among most doctors in the developed world, let alone thedeveloping world where physician shortages plague society. This problem can beaddressed by teleEEG that uses remote EEG analysis by experts or by localcomputer processing of EEGs. However, both of these options are prohibitivelyexpensive and the second option requires abundant computing resources andinfrastructure, which is another concern in developing countries where thereare resource constraints on capital and computing infrastructure. In this work,we present a cloud-based deep neural network approach to provide decisionsupport for non-specialist physicians in EEG analysis and interpretation. Named`neurology-as-a-service,' the approach requires almost no manual interventionin feature engineering and in the selection of an optimal architecture andhyperparameters of the neural network. In this study, we deploy a pipeline thatincludes moving EEG data to the cloud and getting optimal models for variousclassification tasks. Our initial prototype has been tested only in developedworld environments to-date, but our intention is to test it in developing worldenvironments in future work. We demonstrate the performance of our proposedapproach using the BCI2000 EEG MMI dataset, on which our service attains 63.4%accuracy for the task of classifying real vs. imaginary activity performed bythe subject, which is significantly higher than what is obtained with a shallowapproach such as support vector machines.

Neurology-as-a-Service for the Developing World