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• W2922756788 abstract "ABSTRACTDiagnostic efforts to identify alcoholism are frequently subjective; however, recent technological advances have allowed the automated assessment of objective signals. To improve the performance of the automated classification of patients with alcohol dependence (AD) and controls, we analyzed features of event-related potentials (ERPs) in 72 subjects with AD and 42 controls while the subjects underwent three different visual oddball tasks. In addition to such commonly used morphological features (MFs) as time-domain amplitude and latency components as well as discrete wavelet transform coefficient entropies (WEs), we analyzed Hu moment invariants (HMIs) in this study. Smaller P3 and P2 amplitudes, longer latencies, lower WE values, and lower HMI values were observed for AD subjects relative to those observed for controls. We used a Naive Bayes algorithm to compare classification accuracies among MFs, WEs, HMIs, combinations of MFs/WEs, and combinations of all three characteristics. We found that H..." @default.
• W2922756788 created "2019-04-01" @default.
• W2922756788 creator A5075082893 @default.
• W2922756788 date "2019-03-21" @default.
• W2922756788 modified "2023-09-26" @default.
• W2922756788 title "Alcoholism detection based on Hu moment invariants of event-related potentials" @default.
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• W2922756788 doi "https://doi.org/10.1080/02533839.2019.1584764" @default.
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