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

Content Provider	IEEE Xplore Digital Library
Author	Wulsin, D. Blanco, J. Mani, R. Litt, B.
Copyright Year	2010
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 characteristically anomalous. These findings show that Deep Belief Nets can facilitate human review of large amounts of clinical EEG as well as mining new EEG features that may be indicators of unusual activity.
Starting Page	436
Ending Page	441
File Size	358923
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.71
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	detection anomaly novelty Humans data mining EEG outlier Electroencephalography Deep Belief Nets Support vector machines Training Detectors Brain modeling Monitoring
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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