A Classification Framework for Depressive Episode using R-R Intervals from Smartwatch
文献类型:会议论文
作者 | Li, Fenghua4; Liu, Guoxiong3; Zou, Zhiling2; Yan, Yang4; Huang, Xin4; Liu, Xuanang1; Liu, Zhengkui4 |
出版日期 | 2023 |
会议名称 | IEEE Transactions on Affective Computing |
会议日期 | 2023 |
会议地点 | 不详 |
关键词 | depression detection depressive symptoms monitoring wearable device diurnal mood variation digital mental health |
DOI | 10.1109/TAFFC.2023.3343463 |
页码 | 1-15 |
英文摘要 | Depressive episode is key symptom collection of mood disorders. Early intervention can prevent it from happening or reduce its impact, and close monitoring can greatly improve medical management. However, most current monitoring methods are ex post facto, coarse in time granularity and resource consuming. In this study, we aimed to develop a cost-friendly and high usability depressive episode detection framework. In Phase I, we fitted instantaneous affective state models by using R-R intervals collected with photoplethysmogram sensors in smartwatches from laboratory experiments of 1107 participants. In Phase II we utilized the models from Phase I to record long-term affective experience of 2192 participants. Depressive episode models were fitted with affective experience time series. The best instantaneous affective states models achieved overall accuracies of 91% with 2 classes (neutral/ aroused) and 82% with 3 classes (joy/ neutral/ sadness), and the depressive episode models (less severe/ more severe) achieved an overall accuracy of 76% and a best accuracy of 88%. We investigated and discussed the performance differences of the models with multiple settings. We found person-based feature normalization is effective in improving model performance for subjective affect experience. We also found identification of diurnal mood variation may be critical in depressive episode detection. |
收录类别 | EI |
会议录 | IEEE Transactions on Affective Computing |
源URL | [http://ir.psych.ac.cn/handle/311026/46596] |
专题 | 心理研究所_中国科学院心理健康重点实验室 |
作者单位 | 1.Department of Psychology, University of Chinese Academy of Sciences, Beijing, China 2.Faculty of Psychology, Southwest University, Chongqing, China 3.School of Psychology, Nanjing Normal University, Nanjing, China 4.Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Li, Fenghua,Liu, Guoxiong,Zou, Zhiling,et al. A Classification Framework for Depressive Episode using R-R Intervals from Smartwatch[C]. 见:IEEE Transactions on Affective Computing. 不详. 2023. |
入库方式: OAI收割
来源:心理研究所
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