Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding.
Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding.
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DOI：
10.1109/jbhi.2017.2676878
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发表时间：
2018-03
影响因子：
7.7
通讯作者：
Cohn JF
中科院分区：
文献类型：
--
作者：
Dibeklioglu H;Hammal Z;Cohn JF
抑郁症是世界上最常见的精神疾病之一，有超过3.5亿人受到影响。目前筛查和评估抑郁症的方法几乎完全依赖于临床访谈和自我报告量表。虽然有用，但这些措施缺乏客观，系统和有效的方法来纳入行为观察，这些行为观察是抑郁症存在和严重程度的强有力指标。利用面部和头部运动以及发声的动态，我们训练分类器来检测抑郁症的三个严重程度。参与者是被诊断患有重度抑郁症的社区样本。在21周的时间内，每隔7周进行一次临床访谈（汉密尔顿抑郁量表，HRSD）。在每次访谈中，他们被HRSD评分为中度至重度抑郁、轻度抑郁或缓解。Logistic回归分类器使用留一参与者的验证进行了比较，面部运动，头部运动，声乐韵律单独和组合。从面部运动动力学的抑郁症严重程度测量的准确性高于头部运动动力学;和每一个都大大高于语音韵律。使用所有三种模式相结合的准确性仅略高于面部和头部相结合。这些研究结果表明，抑郁症的严重程度，患者的行为指标的自动检测是可行的，多模式的措施提供最强大的检测。
Depression is one of the most common psychiatric disorders worldwide, with over 350 million people affected. Current methods to screen for and assess depression depend almost entirely on clinical interviews and self-report scales. While useful, such measures lack objective, systematic, and efficient ways of incorporating behavioral observations that are strong indicators of depression presence and severity. Using dynamics of facial and head movement and vocalization, we trained classifiers to detect three levels of depression severity. Participants were a community sample diagnosed with major depressive disorder. They were recorded in clinical interview (Hamilton Rating Scale for Depression, HRSD) at 7-week intervals over a period of 21 weeks. At each interview, they were scored by HRSD as moderately to severely depressed, mildly depressed, or remitted. Logistic regression classifiers using leave-one-participant-out validation were compared for facial movement, head movement, and vocal prosody individually and in combination. Accuracy of depression severity measurement from facial movement dynamics was higher than that for head movement dynamics; and each was substantially higher than that for vocal prosody. Accuracy using all three modalities combined only marginally exceeded that of face and head combined. These findings suggest that automatic detection of depression severity from behavioral indicators in patients is feasible and that multimodal measures afford most powerful detection.