Sleep/Wakefulness Detection Using Tracheal Sounds and Movements
Bibliographic record
Abstract
Nasim Montazeri Ghahjaverestan,1,2 Sina Akbarian,1,2 Maziar Hafezi,1,2 Shumit Saha,1,2 Kaiyin Zhu,1 Bojan Gavrilovic,1 Babak Taati,1– 3,* Azadeh Yadollahi1,2,* 1Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada; 2Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; 3Computer Science, University of Toronto, Toronto, ON, Canada*These authors contributed equally to this workCorrespondence: Azadeh YadollahiKite - Toronto Rehabilitation Institute, University Health Network, Room 12-106, 550 University Avenue, Toronto, ON M5G 2A2, CanadaTel +1 416 597 3422 Ext 7936Fax +1 416 597 8959Email Azadeh.Yadollahi@uhn.caPurpose: The current gold standard to detect sleep/wakefulness is based on electroencephalogram, which is inconvenient if included in portable sleep screening devices. Therefore, a challenge in the portable devices is sleeping time estimation. Without sleeping time, sleep parameters such as apnea/hypopnea index (AHI), an index for quantifying sleep apnea severity, can be underestimated. Recent studies have used tracheal sounds and movements for sleep screening and calculating AHI without considering sleeping time. In this study, we investigated the detection of sleep/wakefulness states and estimation of sleep parameters using tracheal sounds and movements.Materials and Methods: Participants with suspected sleep apnea who were referred for sleep screening were included in this study. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device, called the Patch, attached over the trachea. Each 30-second epoch of tracheal data was scored as sleep or wakefulness using an automatic classification algorithm. The performance of the algorithm was compared to the sleep/wakefulness scored blindly based on the polysomnography.Results: Eighty-eight subjects were included in this study. The accuracy of sleep/wakefulness detection was 82.3± 8.66% with a sensitivity of 87.8± 10.8 % (sleep), specificity of 71.4± 18.5% (awake), F1 of 88.1± 9.3% and Cohen’s kappa of 0.54. The correlations between the estimated and polysomnography-based measures for total sleep time and sleep efficiency were 0.78 (p< 0.001) and 0.70 (p< 0.001), respectively.Conclusion: Sleep/wakefulness periods can be detected using tracheal sound and movements. The results of this study combined with our previous studies on screening sleep apnea with tracheal sounds provide strong evidence that respiratory sounds analysis can be used to develop robust, convenient and cost-effective portable devices for sleep apnea monitoring.Keywords: sleep apnea, apnea/hypopnea index, principal component analysis, classification, imbalanced data
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".