Abstract
Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden Markov models (HMMs) for sequential decoding of EEG events with deep learning-based post-processing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts, and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application.
Highlights
Electroencephalograms (EEGs) are used in a broad range of health care institutions to monitor and record electrical activity in the brain using electrodes placed on the scalp
Features from each epoch are identified using a feature extraction technique described in Section Data: The TUH-EEG Event Short Set
4.81 8.81 4.26 11.55 20.32 40.21 2.43 3.86 1.05 14.49 9.02 25.64 2.81 5.95 1.15 17.63 13.69 33.33 tradeoff (DET) curve (Martin et al, 1997) we report a 2way classification result in which we collapse the data into a target class (TARG) and a background class (BCKG)
Summary
Electroencephalograms (EEGs) are used in a broad range of health care institutions to monitor and record electrical activity in the brain using electrodes placed on the scalp. EEGs are essential in diagnosis of clinical conditions such as epilepsy, depth of anesthesia, coma, encephalopathy, and brain death (Yamada and Meng, 2017). Manual scanning and interpretation of EEGs is time-consuming since these recordings may last hours or days. It is an expensive process as it requires highly trained experts. High performance automated analysis of EEGs can reduce time to diagnosis and enhance real-time applications by flagging sections of the signal that need further review. Many methods have been developed over the years (Ney et al, 2016) including time-frequency digital signal processing techniques (Osorio et al, 1998; Gotman, 1999), wavelet analysis (Sartoretto and Ermani, 1999), multivariate techniques based on simulated leaky integrate-and-fire
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