Open AccessA Classification Framework for Depressive Episode Using R-R Intervals From Smartwatch
Author(s) -
Fenghua Li,
Guoxiong Liu,
Zhiling Zou,
Yang Yan,
Xin Huang,
Xuanang Liu,
Zhengkui Liu
Publication year - 2023
Publication title -
ieee transactions on affective computing
Language(s) - English
Resource type - Journals
ISSN - 1949-3045
DOI - 10.1109/taffc.2023.3343463
Subject(s) - computing and processing , robotics and control systems , signal processing and analysis
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 article, 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.