Technical Sciences Vocational School, Gaziantep University, 27310, Gaziantep, Turkey.
Phys Eng Sci Med. 2021 Dec;44(4):1201-1212. doi: 10.1007/s13246-021-01055-6. Epub 2021 Sep 10.
Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.
偏头痛是一种反复发作、长期存在、致残或使大脑虚弱的主要神经血管疾病。本研究采用脑电图（EEG）信号进行偏头痛诊断，并提出了一种计算机辅助诊断系统以支持专家意见。提出了一种基于可调 Q 因子小波变换（TQWT）的方法来分析 EEG 信号的振荡结构。使用 TQWT，将 EEG 信号分解为子带。然后，从这些频带中统计计算特征。使用 Kruskal Wallis 检验来评估获得的特征在偏头痛患者和健康对照组之间的区分能力。使用著名的集成学习技术对从每个子带获得的特征值进行分类，并测试它们的分类性能。在所评估的分类器中，使用旋转森林算法并结合子带 2 获得的特征，最高分类性能达到 89.6%。这些结果表明，该研究作为一种支持偏头痛诊断的专家意见的工具具有潜力。
