Non-Intrusive Sleep Staging with Integration of Ballistocardiography and Audio Signals
Bibliographic record
Abstract
This paper investigates the use of audio analysis alongside non-intrusive sensors to measure cardiorespiratory parameters for sleep stage classification. A ballistocardiogram (BCG) detected cardiorespiratory signals through vibrations on the mattress, while an Apple Watch Ultra 2 used optical sensors on the wrist for measurement. The cardiorespiratory signals were processed with non-linear methods and autocorrelation functions to create a dataset from the entire system, which was then trained in a long short-term memory (LSTM) model to classify sleep stages. The output was validated using a polysomnography (PSG) system, showing an agreement of 81% for REM and non-REM sleep stages. Good results were also obtained with audio and BCG, with approximately 76% sleep stage agreement, demonstrating a fully non-contact environment viable for sleep detection. Furthermore, incorporating audio features proved effective in evaluating sleep stages with a non-intrusive setup, ensuring minimum disturbance to participants' natural sleep cycle.Clinical Relevance-Evaluating sleep quality is an inconvenient process for patients due to the extensive setup and discomfort of a clinical PSG system. A more convenient, minimal contact system that effectively assesses sleep quality without disrupting the patient's natural sleep could provide clinicians with measurements that better reflect real-world sleep patterns. This would enable more accurate diagnoses of sleep disorders without inducing symptoms associated with the intensive clinical study experience.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".