Semi-automated annotation of signal events in clinical EEG data

Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483, Lopez, S., Golmohammadi, M., Obeid, I. and Picone, J. (2016) Semi-automated annotation of signal events in clinical EEG data. In: 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 3 Dec 2016, Philadelphia, PA, USA.

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Abstract

To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons, it is very difficult to justify the creation of large annotated corpora for these applications. Hence, automated annotation techniques become increasingly important. In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus. The algorithm is designed to annotate six types of EEG events. Two model training schemes, namely threshold-based and volume-based, are evaluated. In the threshold-based scheme the threshold of confidence scores is optimized in the initial training iteration, whereas for the volume-based scheme only a certain amount of data is preserved after each iteration. Recognition performance is improved 2% absolute and the system is capable of automatically annotating previously unlabeled data. Given that the interpretation of clinical EEG data is an exceedingly difficult task, this study provides some evidence that the proposed method is a viable alternative to expensive manual annotation.

Item Type: Conference or Workshop Item (Paper)
ISBN: 9781509067138
Identifier: 10.1109/SPMB.2016.7846855
Page Range: pp. 1-5
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Electroencephalography, Training, Brain models, Hidden Markov models, Data models, Sensitivity
Subjects: Medicine and health > Clinical medicine
Computing
Related URLs:
Depositing User: Su Yang
Date Deposited: 04 Jun 2021 15:19
Last Modified: 28 Aug 2021 07:15
URI: http://repository.uwl.ac.uk/id/eprint/7931

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