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Title: Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features
Authors: Peh, Wei Yan
Thomas, John
Bagheri, Elham
Chaudhari, Rima
Karia, Sagar
Rathakrishnan, Rahul
Saini, Vinay
Shah, Nilesh
Srivastava, Rohit
Tan, Yee-Leng
Dauwels, Justin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Peh, W. Y., Thomas, J., Bagheri, E., Chaudhari, R., Karia, S., Rathakrishnan, R., Saini, V., Shah, N., Srivastava, R., Tan, Y. & Dauwels, J. (2021). Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features. International Journal of Neural Systems, 31(6), 2150016-.
Project: NHIC-I2D-1608138 
Journal: International Journal of Neural Systems 
Abstract: Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.
ISSN: 0129-0657
DOI: 10.1142/S0129065721500167
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 World Scientific Publishing Company. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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