Detection of Sleep Apnea from Single-Lead ECG: Comparison of Deep Learning Algorithms
Bibliographic record
Abstract
Apnea is a prevalent sleep disorder which has detrimental impacts on human health and quality of life. Accurate automatic algorithms for the detection of sleep apnea are needed for analyzing long-term sleep data and monitoring and management of its side effects and consequences. Among different approaches for automatic detection of sleep apnea from biosignals, deep learning algorithms are of particular interest as, unlike conventional machine learning algorithms, they do not rely on expert crafted features. In this paper, we developed and evaluated a number of different deep learning models for the detection of sleep apnea from a single-lead electrocardiogram (ECG) signal. ECG R-peak amplitude and R-R intervals were extracted, and power spectral analysis was performed to align the R-peak amplitude and the R-R intervals in frequency domain. Convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent unit, and deep hybrid models were implemented and analyzed. The performance of deep learning algorithms was evaluated on an apnea-ECG dataset of 70 recordings divided into a learning set of 35 records and a test of 35 records. The best accuracy, sensitivity, specificity, and F1-score on the test data were 80.67%, 75.04%, 84.13%, and 74.72%, respectively, with a hybrid CNN and LSTM network. The results show promise toward improved apnea detection using deep learning.
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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 | 0.002 | 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".