计算机科学
模式识别(心理学)
小波
人工智能
离散小波变换
脑电图
帕斯卡(单位)
分类器(UML)
特征向量
多数决原则
小波变换
心理学
精神科
程序设计语言
作者
Gülay Taşçı,Hui Wen Loh,Prabal Datta Barua,Mehmet Bayğın,Burak Taşçı,Şengül Doğan,Türker Tuncer,Elizabeth Palmer,Ru San Tan,U. Rajendra Acharya
标识
DOI:10.1016/j.knosys.2022.110190
摘要
Electroencephalogram (EEG)-based major depressive disorder (MDD) machine learning detection models can objectively differentiate MDD from healthy controls but are limited by high complexities or low accuracies. This work presents a self-organized computationally lightweight handcrafted classification model for accurate MDD detection using a reference subject-based validation strategy. We used the public Multimodal Open Dataset for Mental Disorder Analysis (MODMA) comprising 128-channel EEG signals from 24 MDD and 29 healthy control (HC) subjects. The input EEG was decomposed using multilevel discrete wavelet transform with Daubechies 4 mother wavelet function into eight low- and high-level wavelet bands. We used a novel Twin Pascal’s Triangles Lattice Pattern(TPTLP) comprising an array of 25 values to extract local textural features from the raw EEG signal and subbands. For each overlapping signal block of length 25, two walking paths that traced the maximum and minimum L1-norm distances from v1 to v25 of the TPTLP were dynamically generated to extract features. Forty statistical features were also extracted in parallel per run. We employed neighborhood component analysis for feature selection, a k-nearest neighbor classifier to obtain 128 channel-wise prediction vectors, iterative hard majority voting to generate 126 voted vectors, and a greedy algorithm to determine the best overall model result. Our generated model attained the best channel-wise and overall model accuracies. The generated system attained an accuracy of 76.08% (for Channel 1) and 83.96% (voted from the top 13 channels) using leave-one-subject-out(LOSO) cross-validation (CV) and 100% using 10-fold CV strategies, which outperformed other published models developed using same (MODMA) dataset.
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