Identification of Adolescents With Major Depressive Disorder Using Random Forest Based on Nocturnal Heart Rate Variability
Bibliographic record
Abstract
ABSTRACT Major depressive disorder (MDD) in adolescents is often underdiagnosed, with the current diagnosis predominantly relying on subjective assessment. Sleep disturbance and reduced heart rate variability (HRV) have been typically observed in adolescents with MDD. This study aimed to develop an automatic classification model based on nocturnal HRV features to identify adolescent MDD. Sixty‐three subjects, including depressed adolescents and healthy controls, participated in the study and completed a three‐night sleep electrocardiogram (ECG) monitoring, yielding 160 overnight RR interval time series and 7520 5‐min short‐term segments for analysis. Nineteen HRV features were extracted from the time domain, frequency domain, and nonlinear dynamics. The Bayesian‐optimized random forest (BO‐RF) algorithm was applied as the classifier, with performance evaluated using ten‐fold cross‐validation. The impact of data accumulation on the reliability of identification using short‐term data and the importance of features were also examined. The BO‐RF classifier based on long‐term features achieved a noteworthy predictive accuracy of 80.6%, and the performance of the classifier using short‐term data showed a significant improvement when more segment outcomes from the same night were included, ultimately achieving an accuracy of 75.0%. The Poincaré plot‐derived features, especially heart rate asymmetry (HRA) features such as C1 d , significantly contributed to distinguishing depressed adolescents from healthy subjects. Nocturnal HRV features can effectively differentiate adolescents with MDD from healthy controls. This study provides a promising diagnostic approach for adolescent MDD, with the potential to be integrated into wearable devices for broader application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".