Qayyum Abdul, Razzak Imran, Tanveer M, Mazher Moona, Alhaqbani Bandar
IEEE/ACM Trans Comput Biol Bioinform. 2023 Jul-Aug;20(4):2587-2597. doi: 10.1109/TCBB.2023.3257175. Epub 2023 Aug 9.
Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly..
抑郁症是一种精神障碍，其特征为持续的抑郁情绪或对进行活动失去兴趣，导致日常生活受到严重损害。可能的病因包括心理、生物和社会方面的困扰源。临床抑郁症是抑郁症的更严重形式，也称为重度抑郁症或重度抑郁障碍。最近，脑电图和语音信号已被用于抑郁症的早期诊断；然而，它们关注的是中度或重度抑郁症。我们将音频频谱图和多个频率的脑电图信号相结合，以提高诊断性能。为此，我们融合了不同层次的语音和脑电图特征以生成描述性特征，并在语音和脑电图频谱上应用了视觉变换器和各种预训练网络。我们在用于精神障碍分析的多模态开放数据集（MODMA）上进行了广泛的实验，结果表明，对于轻度阶段的患者，抑郁症诊断的性能有显著提高（精确率、召回率和F1分数分别为0.972、0.973和0.973）。此外，我们使用Flask提供了一个基于网络的框架，并公开了源代码。
