IEEE J Biomed Health Inform. 2019 Nov;23(6):2265-2275. doi: 10.1109/JBHI.2019.2938247. Epub 2019 Aug 29.
Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study explored from physiological and behavioral perspectives simultaneously and fused pervasive electroencephalography (EEG) and vocal signals to make the detection of depression more objective, effective and convenient. After extraction of several effective features for these two types of signals, we trained six representational classifiers on each modality, then denoted diversity and correlation of decisions from different classifiers using co-decision tensor and combined these decisions into the ultimate classification result with multi-agent strategy. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed multi-modal depression detection strategy is superior to the single-modal classifiers or other typical late fusion strategies in accuracy, f1-score and sensitivity. This work indicates that late fusion of pervasive physiological and behavioral signals is promising for depression detection and the multi-agent strategy can take advantage of diversity and correlation of different classifiers effectively to gain a better final decision.
目前，抑郁症已成为一种常见的精神障碍，也是全球主要致残原因之一。由于个体差异引起的抑郁症状不同，如何设计全面有效的抑郁检测方法成为迫切需求。本研究从生理和行为两个方面同时进行探索，融合普及型脑电图（EEG）和声音信号，使抑郁检测更加客观、有效和便捷。对这两种信号提取出若干有效特征后，我们在每种模态上训练了 6 种代表性分类器，然后使用协同决策张量表示不同分类器决策的多样性和相关性，并采用多代理策略将这些决策组合为最终分类结果。在 170 名受试者（81 名抑郁患者和 89 名正常对照组）上的实验结果表明，所提出的多模态抑郁检测策略在准确性、f1 分数和灵敏度方面均优于单模态分类器或其他典型的后期融合策略。这项工作表明，普及型生理和行为信号的后期融合对于抑郁检测具有广阔前景，多代理策略可以有效利用不同分类器的多样性和相关性，从而做出更好的最终决策。
