Saghab Torbati Mehrnaz, Zandbagleh Ahmad, Daliri Mohammad Reza, Ahmadi Amirmasoud, Rostami Reza, Kazemi Reza
Neuroscience and Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran.
Max Planck Institute for Biological Intelligence, 82319 Seewiesen, Germany.
Diagnostics (Basel). 2025 Jan 29;15(3):316. doi: 10.3390/diagnostics15030316.
Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis.
尽管双相情感障碍（BD）普遍存在且病情严重，但目前的诊断方法在很大程度上仍具有主观性。本研究提出了一种使用脑电图（EEG）衍生的 Hjorth 参数（活动性、移动性和复杂性）的自动诊断框架，旨在建立用于 BD 检测的客观神经生理标志物，并深入了解其潜在的神经机制。利用从 20 名 BD 患者和 20 名健康对照（HCs）收集的静息闭眼 EEG 数据，我们基于跨多个频段提取的 Hjorth 参数开发了一种新颖的诊断方法。我们采用了严格的留一法交叉验证策略，以确保进行稳健的、独立于个体的评估，并结合可解释人工智能（XAI）来识别最具区分性的神经特征。我们的方法取得了显著的分类准确率（92.05%），其中来自β和γ频段的活动性 Hjorth 参数成为最具区分性的特征。XAI 分析表明，这些高频段的前脑区域对 BD 检测的贡献最为显著，为 BD 的神经生理标志物提供了新的见解。这项研究证明了 Hjorth 参数在 BD 检测中的卓越诊断效用，特别是在高频范围和前脑区域。我们的研究结果不仅为 BD 的自动化诊断建立了一个有前景的框架，还为双相及相关障碍的神经生理基础提供了有价值的见解。我们方法的稳健性能和可解释性表明其作为 BD 客观诊断临床工具的潜力。
