Title: Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures,
Abstract: Objective: A clinical decision support tool that automatically interprets
EEGs can reduce time to diagnosis and enhance real-time applications such as
ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a
specificity below 5% was the minimum requirement for clinical acceptance. We
propose a highperformance classification system based on principles of big data
and machine learning. Methods: A hybrid machine learning system that uses
hidden Markov models (HMM) for sequential decoding and deep learning networks
for postprocessing is proposed. These algorithms were trained and evaluated
using the TUH EEG Corpus, which is the world's largest publicly available
database of clinical EEG data. Results: Our approach delivers a sensitivity
above 90% while maintaining a specificity below 5%. This system detects three
events of clinical interest: (1) spike and/or sharp waves, (2) periodic
lateralized epileptiform discharges, (3) generalized periodic epileptiform
discharges. It also detects three events used to model background noise: (1)
artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deep
learning system can deliver a low false alarm rate on EEG event detection,
making automated analysis a viable option for clinicians. Significance: The TUH
EEG Corpus enables application of highly data consumptive machine learning
algorithms to EEG analysis. Performance is approaching clinical acceptance for
real-time applications.