Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets

Abstract--Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background, making them difficult to reliably detect. We show that Deep Belief Nets (DBNs), a type of multi-layer generative neural network, can be used effectively for such EEG anomaly detection. We compare this technique to the state-of-the-art, a one-class Support Vector Machine (SVM), showing that the DBN outperforms the SVM by the F1 measure for our EEG dataset. We also show how the outputs of a DBN-based detector can be used to aid visualization of anomalies in large EEG data sets and propose a method for using DBNs to gain insight into which features of signals are characterist...

• Type: Journal
• Year: 2010
• Where: ICMLA
• Authors: Drausin Wulsin, Justin Blanco, Ram Mani, Brian Litt
• Keywords: Deep Belief Nets, EEG Waveforms, ICMLA 2010, Machine Learning, Pathologic Eeg Waveforms