Mohd Nazri Ainul Khairiyah, Yahya Norashikin, Khan Danish M, Mohd Radzi Noor'Izni Zafirah, Badruddin Nasreen, Abdul Latiff Abdul Halim, Abdulaal Mohammed J
Maintenance Department, PETRONAS Gas Berhad, Kerteh, Terengganu, Malaysia.
Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
Front Neurosci. 2025 Jan 10;18:1524513. doi: 10.3389/fnins.2024.1524513. eCollection 2024.
Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.
The study proposes a novel EEG-based classification approach, focusing on effective connectivity (EC) derived from resting-state EEG signals in combination with support vector machine (SVM) algorithms. EC estimation is performed using the partial directed coherence (PDC) technique. The analysis is conducted on an EEG dataset comprising 35 individuals with AUD and 35 healthy controls (HCs). The methodology evaluates the efficacy of connectivity features in distinguishing between AUD and HC and subsequently develops and assesses an EEG classification technique using EC matrices and SVM.
The proposed methodology demonstrated promising performance, achieving a peak accuracy of 94.5% and an area under the curve (AUC) of 0.988, specifically using frequency bands 29, 36, 45, 46, and 52. Additionally, feature reduction techniques applied to the PDC adjacency matrices in the gamma band further improved classification outcomes. The SVM-based classification achieved an accuracy of 96.37 ± 0.45%, showcasing enhanced performance through the utilization of reduced PDC adjacency matrices.
These results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.
过量饮酒会对身心健康、生活方式和社会交往产生负面影响。长期酗酒会改变大脑结构，导致酒精使用障碍（AUD），这是一种需要早期诊断以进行有效管理的疾病。目前的诊断方法主要依赖主观问卷，若能采用客观测量方法将大有裨益。
该研究提出了一种基于脑电图的新型分类方法，重点关注从静息态脑电图信号中导出的有效连接性（EC），并结合支持向量机（SVM）算法。使用偏定向相干性（PDC）技术进行EC估计。对一个包含35名AUD患者和35名健康对照（HC）的脑电图数据集进行分析。该方法评估连接性特征在区分AUD和HC方面的功效，随后使用EC矩阵和SVM开发并评估一种脑电图分类技术。
所提出的方法表现出了良好的性能，在使用29、36、45、46和52频段时，峰值准确率达到94.5%，曲线下面积（AUC）为0.988。此外，应用于伽马波段PDC邻接矩阵的特征约简技术进一步改善了分类结果。基于SVM的分类准确率达到96.37±0.45%，通过使用约简后的PDC邻接矩阵展示了更高的性能。
这些结果凸显了所开发算法作为AUD检测的强大诊断工具的潜力，提高了诊断精度，超越了主观方法。纳入从脑电图信号中导出的EC特征可为量身定制的治疗策略提供依据，有助于改善AUD的管理。
