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 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".