@article{10.11648/j.ajcem.20251303.13,
  author = {Hang Li and Jing Li and Xin Zhong and Guo Chen and Ruiqi Peng and Liang Zhang and Juqiang Han and Xiaomin Luo},
  title = {Depression Predictive Model Using In-Context Learning Based on HRV with PPG Derived Validity Label
},
  journal = {American Journal of Clinical and Experimental Medicine},
  volume = {13},
  number = {3},
  pages = {45-53},
  doi = {10.11648/j.ajcem.20251303.13},
  url = {https://doi.org/10.11648/j.ajcem.20251303.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20251303.13},
  abstract = {Background: Traditional diagnostic approaches for major depression disorder (MDD) or clinical depression rely on subjective assessment of clinical symptoms while heart rate variability (HRV) metrics provide an objective alternative to support clinical assessments and facilitate early depression detection. However, the imperceptibility of non-stationarity and unpredictability in noticing a factor for its HRV outcome highlight the challenges in modelling of predictive AI. Methods: In this study, totally 139 participants were recruited including 40 patients and 99 healthy controls. Only 28 of the 40 depression patients and 34 of the 99 healthy controls were enrolled for HRV data collection according to inclusion criteria. Our experiment provided evidence for evaluation of the validation method using a photoplethysmography (PPG) derived parameter representing beat-to-beat stress-induced vascular response in terms of labelling performance and applicability. Results: The results demonstrated the link between depression and the autonomic nervous system (ANS) measured using HRV both in statistical analysis and AI-driven classification, as seen in the GPT-4-based LLM outperformed baseline models across multiple data sets. The validity labeling contributed significantly to model performance and robustness, especially in small-sample scenarios. Although small sample size was used in HRV-based depression prediction training via a large language model (LLM) with in-context learning (ICL), the performance was definitely improved with validity labeling activated compared to labeling disabled. Conclusions: Through comparison of observational accuracy in predictive models, the reliability of HRV recordings is crucial for improving AI-driven depression prediction and aligning AI analysis with the expectations on physiological and psychological effects. Among factors that could cause HRV value to change in unexpected ways, stationarity is a prerequisite for short-term HRV (ST-HRV), thus validation strategy, a labeling method capable of identifying and rejecting recordings of false signals, is necessarily needed.
},
 year = {2025}
}

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TY  - JOUR
T1  - Depression Predictive Model Using In-Context Learning Based on HRV with PPG Derived Validity Label

AU  - Hang Li
AU  - Jing Li
AU  - Xin Zhong
AU  - Guo Chen
AU  - Ruiqi Peng
AU  - Liang Zhang
AU  - Juqiang Han
AU  - Xiaomin Luo
Y1  - 2025/06/11
PY  - 2025
N1  - https://doi.org/10.11648/j.ajcem.20251303.13
DO  - 10.11648/j.ajcem.20251303.13
T2  - American Journal of Clinical and Experimental Medicine
JF  - American Journal of Clinical and Experimental Medicine
JO  - American Journal of Clinical and Experimental Medicine
SP  - 45
EP  - 53
PB  - Science Publishing Group
SN  - 2330-8133
UR  - https://doi.org/10.11648/j.ajcem.20251303.13
AB  - Background: Traditional diagnostic approaches for major depression disorder (MDD) or clinical depression rely on subjective assessment of clinical symptoms while heart rate variability (HRV) metrics provide an objective alternative to support clinical assessments and facilitate early depression detection. However, the imperceptibility of non-stationarity and unpredictability in noticing a factor for its HRV outcome highlight the challenges in modelling of predictive AI. Methods: In this study, totally 139 participants were recruited including 40 patients and 99 healthy controls. Only 28 of the 40 depression patients and 34 of the 99 healthy controls were enrolled for HRV data collection according to inclusion criteria. Our experiment provided evidence for evaluation of the validation method using a photoplethysmography (PPG) derived parameter representing beat-to-beat stress-induced vascular response in terms of labelling performance and applicability. Results: The results demonstrated the link between depression and the autonomic nervous system (ANS) measured using HRV both in statistical analysis and AI-driven classification, as seen in the GPT-4-based LLM outperformed baseline models across multiple data sets. The validity labeling contributed significantly to model performance and robustness, especially in small-sample scenarios. Although small sample size was used in HRV-based depression prediction training via a large language model (LLM) with in-context learning (ICL), the performance was definitely improved with validity labeling activated compared to labeling disabled. Conclusions: Through comparison of observational accuracy in predictive models, the reliability of HRV recordings is crucial for improving AI-driven depression prediction and aligning AI analysis with the expectations on physiological and psychological effects. Among factors that could cause HRV value to change in unexpected ways, stationarity is a prerequisite for short-term HRV (ST-HRV), thus validation strategy, a labeling method capable of identifying and rejecting recordings of false signals, is necessarily needed.

VL  - 13
IS  - 3
ER  - 

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