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Objective: A clinical decision support tool that automatically interpretsEEGs can reduce time to diagnosis and enhance real-time applications such asICU monitoring. Clinicians have indicated that a sensitivity of 95% with aspecificity below 5% was the minimum requirement for clinical acceptance. Wepropose a highperformance classification system based on principles of big dataand machine learning. Methods: A hybrid machine learning system that useshidden Markov models (HMM) for sequential decoding and deep learning networksfor postprocessing is proposed. These algorithms were trained and evaluatedusing the TUH EEG Corpus, which is the world's largest publicly availabledatabase of clinical EEG data. Results: Our approach delivers a sensitivityabove 90% while maintaining a specificity below 5%. This system detects threeevents of clinical interest: (1) spike and/or sharp waves, (2) periodiclateralized epileptiform discharges, (3) generalized periodic epileptiformdischarges. It also detects three events used to model background noise: (1)artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deeplearning system can deliver a low false alarm rate on EEG event detection,making automated analysis a viable option for clinicians. Significance: The TUHEEG Corpus enables application of highly data consumptive machine learningalgorithms to EEG analysis. Performance is approaching clinical acceptance forreal-time applications.

Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures