Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant's pre-trauma physical health status as features, a logistic regression classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.64 to estimate mental health outcomes. The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
翻译:抑郁症和创伤后应激障碍(PTSD)是通常与经历创伤事件有关的精神病病症,通过非侵入性技术(如基于活动的算法)来估计心理健康状况有助于确定早期干预的成功。在这项工作中,我们使用了从1113名穿戴研究级智能观察创伤后护理的个人中采集到的电动活体活动。从4周的活体数据中可以使用进化变异自动电解器(VAE)结构进行不受监督的特征提取。通过使用VAE潜在变量和参与者的创伤前身体健康状况作为特征,一个逻辑回归分类器在接收器操作特征曲线(0.64)下取得了一个区域,以估计心理健康结果。结果表明,VAE模型是长期研究中心理健康结果的活体数据分析很有希望的方法。