Electronics and Electrical Engineering Dept, Indian Institute of Technology Guwahati, Assam, India.
Electrical Engineering Dept, Indian Institute of Technology Dharwad, Dharwad, Karnataka, India.
PLoS One. 2022 Aug 11;17(8):e0272659. doi: 10.1371/journal.pone.0272659. eCollection 2022.
Depression is one of the significant mental health issues affecting all age groups globally. While it has been widely recognized to be one of the major disease burdens in populations, complexities in definitive diagnosis present a major challenge. Usually, trained psychologists utilize conventional methods including individualized interview assessment and manually administered PHQ-8 scoring. However, heterogeneity in symptomatic presentations, which span somatic to affective complaints, impart substantial subjectivity in its diagnosis. Diagnostic accuracy is further compounded by the cross-sectional nature of sporadic assessment methods during physician-office visits, especially since depressive symptoms/severity may evolve over time. With widespread acceptance of smart wearable devices and smartphones, passive monitoring of depression traits using behavioral signals such as speech presents a unique opportunity as companion diagnostics to assist the trained clinicians in objective assessment over time. Therefore, we propose a framework for automated depression classification leveraging alterations in speech patterns in the well documented and extensively studied DAIC-WOZ depression dataset. This novel tensor-based approach requires a substantially simpler implementation architecture and extracts discriminative features for depression recognition with high f1 score and accuracy. We posit that such algorithms, which use significantly less compute load would allow effective onboard deployment in wearables for improve diagnostics accuracy and real-time monitoring of depressive disorders.
抑郁症是影响全球各年龄段人群的重大心理健康问题之一。尽管它已被广泛认为是人群中主要疾病负担之一，但明确诊断的复杂性带来了重大挑战。通常，经过培训的心理学家会利用包括个体访谈评估和手动 PHQ-8 评分在内的传统方法。然而，症状表现的异质性，从躯体到情感抱怨都存在很大的主观性，这使得其诊断变得更加复杂。由于在医生办公室就诊期间，偶发性评估方法具有横断面性质，因此诊断准确性进一步复杂化，尤其是因为抑郁症状/严重程度可能随时间而演变。随着智能可穿戴设备和智能手机的广泛接受，使用行为信号（如语音）被动监测抑郁特征为伴随诊断提供了一个独特的机会，以帮助受过训练的临床医生进行随时间的客观评估。因此，我们提出了一种利用在经过充分记录和广泛研究的 DAIC-WOZ 抑郁数据集中文本模式变化的自动化抑郁分类框架。这种新颖的基于张量的方法需要一个结构简单得多的实现架构，并利用具有高 f1 分数和准确性的判别特征来识别抑郁。我们假设，这种使用计算负载显著较少的算法将允许在可穿戴设备中进行有效部署，以提高诊断准确性并实时监测抑郁障碍。
