Department of Biomedical Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey.
Department of Electrical and Electronics Engineering, Inonu University, Malatya, Turkey.
Int J Neural Syst. 2020 Sep;30(9):2050046. doi: 10.1142/S012906572050046X.
Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.
强迫症(OCD)是一种神经精神疾病,其特点是被这些想法激活的侵入性和迭代性的烦扰思想和心理态度。在最近的研究中,先进的信号处理技术被用于诊断 OCD。本研究提出了四种不同的测量方法;固有锁相值、固有相干性、固有同步似然和固有可视图表相似性,这些方法量化了脑电图(EEG)信号中的同步水平和复杂性。这种固有同步是通过利用多变量经验模态分解(MEMD)来实现的,MEMD 是一种数据驱动的方法,可以将非线性和非平稳数据分解为其固有模态函数。我们在这项研究中的固有技术表明,基于 MEMD 的同步分析比单独使用同步方法能提供更详细的知识。此外,考虑到 OCD 的异质性,非线性同步方法提供了更一致的结果。使用样本 t 检验和 Wilcoxon 秩和检验的统计评估表明了这种新方法的重要性。