BROWSE
Detail
Conference Paper
Evaluating the Effectiveness of Multi-Modal Data in Classifying Depressive Symptoms: Insights from Actigraphy, HRV, and Demographic Data
Cited 0 time in 
Share 
Share
- Authors
- Sehwan Moon, Eunkyoung Jeon, Aram Lee, Min Jhon, Ju-Wan Kim, Jeong Eun Kim
- Issue Date
- 2024-12
- Citation
- International Conference on Bioinformatics and Biomedicine (BIBM) 2024, pp.7090-7092
- Language
- English
- Type
- Conference Paper
- Abstract
- Classifying depressive symptoms using actigraphy data presents challenges, as highlighted by previous research. In this study, we constructed the TREND-P dataset, comprising 3,313 subjects from Chonnam National University Hospital (2021-2023), to analyze the impact of varying depressive symptom ratios. The dataset includes actigraphy, demographic, and HRV (Heart Rate Variability) data.We assessed the classification performance on two tasks: a challenging task differentiating subjects with PHQ-9 scores below 5 from those with scores of 5 or higher, and an easier task distinguishing subjects with PHQ-9 scores of 10 or higher from those below 5, excluding mild symptoms.Our results reveal that integrating multimodal data (actigraphy, HRV, and demographic data) slightly enhanced performance, particularly in easier tasks, with improvements. These findings underscore the potential of multimodal data integration in improving the classification of depressive symptoms. However, there are still performance limitations in the more challenging task of distinguishing between subjects with PHQ-9 scores below 5 and those with scores of 5 or higher.
- KSP Keywords
- Classification Performance, Demographic data, Depressive symptoms, Enhanced performance, Performance limitations, data integration, heart rate variability, multimodal data