Bibliographic record
Abstract
Sleep Apnea-Hypopnea Syndrome (SAHS) is one of the common sleep disorders which cause hypertension, coronary artery disease, stroke, and diabetes mellitus, as well as the increment of vehicle collisions. Polysomnography is a traditional way of diagnosing sleep disorder which requires multiple sensors for producing multiple physiological signals. Traditional Polysomnography causes huge costs for diagnosing SAHS because it requires numerous sensors as well as time. This study has developed a model by using deep learning techniques to minimize the cost and time for SAHS diagnosing. This study has utilized the SpO2 signal by using a Convolutional Neural Network (CNN) as a deep learning technique to detect SAHS in any individuals. The sleep disorder depends on the amount of blood in the body which is detected by the SpO2 signal. The proposed CNN model consists of eight layers: three convolution layers, three max-pooling layers, one fully connected layer, and one softmax layer. Two datasets were used: the Apnea-ECG and UCD databases; the first has eight subjects, and the last has 25 subjects. In carrying out the tests, our model achieved an accuracy of 95.5% with the Apnea-ECG database and 90.2% with the UCD database. The suggested technique has provided a cost-effective and efficient way of identifying SAHS in any individual.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".