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Multi-resolution Time-frequency Spectral Derivative Spike Detection for Episode Onset Detection using Passively Collected Sensor Data
  • Ramzi Halabi ,
  • Arend Hintze ,
  • Abigail Ortiz
Ramzi Halabi
Centre for Addiction and Mental Health

Corresponding Author:[email protected]

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Arend Hintze
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Abigail Ortiz
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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-SD2 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