eprintid: 17428
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/28
datestamp: 2023-12-19 03:23:49
lastmod: 2023-12-19 03:23:49
status_changed: 2023-12-19 03:08:02
type: conference_item
metadata_visibility: show
creators_name: Jawed, S.
creators_name: Malik, A.S.
creators_name: Abd Rashid, R.B.
creators_name: Mohamad Saad, M.N.
title: Deep learning-based diagnosis of Alcohol use disorder (AUD) using EEG
ispublished: pub
keywords: Computer aided diagnosis; Convolutional neural networks; Deep learning; Electrophysiology; Learning systems; Neuroimaging, Alcohol addiction; Alcohol use disorder; Convolutional neural network; Daily lives; Deep learning; F1 scores; Healthy controls; Learning methods; Self-assessment; Self-ratings, Electroencephalography
note: cited By 0; Conference of 12th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2022 ; Conference Date: 2 September 2022 Through 6 September 2022; Conference Code:184262
abstract: Alcohol use disorder (AUD) involves people who have difficulty controlling their drinking habits. This results in significant distress and also affects functioning normally in their daily life. The challenge in screening AUD patients using subjective measures is the dependency of this method on self-assessment, which is unreliable in the case of alcoholics as they may lie or not correctly remember facts because access to alcohol use can affect memory. The solution is to use neuroimaging modalities such as electroencephalography (EEG), which looks into the brain patterns and does not involve self-rating. This study proposes a deep learning (DL) method to classify alcoholics and healthy controls. The proposed deep learning method applies EEG feature extraction automatically and classifies the participants into relevant groups. The participants included 30 AUD patients (mean age 56.70 ± 15.33 years) and 15 healthy controls (mean 42.67 ± 15.90 years) who were recruited to acquire EEG data. The data were recorded during 10 minutes of eyes closed (EC) and eyes open (EO) conditions. The proposed analysis utilizes 1D CNN to fit and evaluate the classification model. From EEG data, features were extracted and classified using a convolutional neural network (CNN). The results show that the CNN has achieved the performance rendering a classification accuracy of (93), specificity (89), and sensitivity (89) with an f1 score of 0.94 for the AUD group. In addition, for the healthy control group, the specificity of (100), the sensitivity of (100), and the f1 score of 0.91 are achieved. In conclusion, the results implicated significant neurophysiological differences between alcoholics and control. © 2022 IEEE.
date: 2022
publisher: IEEE Computer Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142368588&doi=10.1109%2fICCE-Berlin56473.2022.9937134&partnerID=40&md5=74479cd9169349dba2afac041894f330
id_number: 10.1109/ICCE-Berlin56473.2022.9937134
full_text_status: none
publication: IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
volume: 2022-S
refereed: TRUE
isbn: 9781665456760
issn: 21666814
citation:   Jawed, S. and Malik, A.S. and Abd Rashid, R.B. and Mohamad Saad, M.N.  (2022) Deep learning-based diagnosis of Alcohol use disorder (AUD) using EEG.  In: UNSPECIFIED.