Harati Sahar, Crowell Andrea, Mayberg Helen, Nemati Shamim
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5763-5766. doi: 10.1109/EMBC.2018.8513610.
Major Depressive Disorder (MDD) is a common psychiatric illness. Automatically classifying depression severity using audio analysis can help clinical management decisions during Deep Brain Stimulation (DBS) treatment of MDD patients. Leveraging the link between short-term emotions and long-term depressed mood states, we build our predictive model on the top of emotion-based features. Because acquiring emotion labels of MDD patients is a challenging task, we propose to use an auxiliary emotion dataset to train a Deep Neural Network (DNN) model. The DNN is then applied to audio recordings of MDD patients to find their low dimensional representation to be used in the classification algorithm. Our preliminary results indicate that the proposed approach, in comparison to the alternatives, effectively classifies depressed and improved phases of DBS treatment with an AUC of 0.80.
重度抑郁症（MDD）是一种常见的精神疾病。利用音频分析自动分类抑郁症严重程度有助于在对MDD患者进行深部脑刺激（DBS）治疗期间做出临床管理决策。利用短期情绪与长期抑郁情绪状态之间的联系，我们基于基于情绪的特征构建预测模型。由于获取MDD患者的情绪标签是一项具有挑战性的任务，我们建议使用辅助情绪数据集来训练深度神经网络（DNN）模型。然后将DNN应用于MDD患者的音频记录，以找到其低维表示，用于分类算法。我们的初步结果表明，与其他方法相比，所提出的方法能够有效地对DBS治疗的抑郁和改善阶段进行分类，曲线下面积（AUC）为0.80。
