Decision Fusion in Automated Sleep Apnea Classification Using Multple Polysomnography Sensors and Convolutional Neural Networks
Bibliographic record
Abstract
We propose a decision fusion approach using a multi-input Convolutional Neural Network (CNN) for sleep apnea detection. Four sensor modalities were analyzed using a nasal pressure transducer (NP), oronasal thermal airflow (TH), electrocardiogram (ECG), and oximeter (SpO2), signals from polysomnography (PSG) data from 130 patients attending a sleep clinic. The best-performing model, utilizing all four sensors, achieved over 94% accuracy, precision, specificity, and over 92% on recall. Combining the less obtrusive ECG with any airflow sensor resulted in improvements compared to ECG or airflow sensors alone, with accuracy reaching over 94%. The results highlight the potential for decision fusion methods to improve sleep apnea detection by incorporating alternative sensors and leveraging the features automatically extracted by the CNN for each modality. This approach can optimize sensor usage for different monitoring environments, such as clinical settings or at- home monitoring, and help to find a balance between accuracy and unobtrusive monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".