Multi-resolution Time-frequency Spectral Derivative Spike Detection for Episode Onset Detection using Passively Collected Sensor Data
Bibliographic record
Abstract
We analyzed data from 145 participants, the majority of which were diagnosed with BD I (92 (63.4%) and 53 (36.6%) with BD II. The participants have been enrolled in the study for a duration of 449±224 days until June 23, 2023. Participants were provided with an Oura smart sensor (Oura Health Oy, Generation 2, Oulu, Finland), a wearable ring that continuously measures activity (e.g., number of steps), sleep (e.g., total sleep duration), and cardiorespiratory variables (e.g., heart rate). The participants were mailed a sizing kit for individualized sensor size selection to ensure optimal skin-sensor contact and data quality. Additionally, participants received a secure e-mailed link asking them to complete a weekly Patient Health Questionnaire (PHQ-9) through a secure email link. Participants must complete all items on both self-rating scales to submit their ratings. For the analysis of activity data, we selected the number of daily steps as a representative variable. As for sleep data, we analyzed the minutes of total sleep per night. We performed multivariate oscillatory mode decomposition using the CEEMD-AN algorithm, followed by Hilbert-based instantaneous frequency computation, and data-driven spectral derivative spike detection. The total PHQ-9 self-rating scale at the vicinity of each detected spike in activity or sleep variability rate was used for labeling episodes of illness on a weekly basis. Using the daily step variable to represent sample activity data, the implementation of the MR-TF-SD 2 algorithm on our database showed decreasing levels of episode onset detection sensitivity with decreasing time resolution. Similarly, using the daily total sleep variable as sample sleep data, episode onset detection sensitivity decreased from day-to-day patterns to monthly total sleep patterns
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".