Automated Recognition of Sleep Apnea-Hypopnea Syndrome Using Continuous Wavelet Transform-Based Multiscale Dispersion Entropy of Single-Lead ECG Signal
Bibliographic record
Abstract
A new method is proposed for recognition of the sleep apnea-hypopnea syndrome (SAHS) using electrocardiograms (ECG) signal in order to find an alternative with the same performance of polysomnography (PSG). Heart rate variability (HRV) signals generated from an ECG signal are used to examine a wide range of indices. A novel aspect of this work is the use of a method to decompose the HRV spectrum into total power spectrum (TP), high frequency (HF), low frequency (LF) and very low frequency (VLF) sub-band signals, and correlates their energy content with sympathetic and parasympathetic activity. The HRV signal was decomposed using the continuous wavelet transform (CWT) followed by the inverse continuous wavelet transform (ICWT), and sub-band signals were extracted from 5-minute episodes. In this regard, the suggested technique provides novel indices based on the mean of small (1 to 5), medium (6 to 10) and large (11 to 20) time scales of multiscal dispersion entropy (MDE) for each sub-band signals. In order to choose the best classifier, the indices of the MDE are submitted to a t-test technique and categorized using three classifiers: decision trees (DT), support vector machines (SVM-RBF) and K-nearest neighbor (KNN). The proposed method is evaluated using the combination of Physionet Apnea–ECG database and the University College Dublin Sleep Apnea Database. Using a 10-fold cross validation technique, the SVM-RBF classification approach achieves an average sensitivity, specificity, and accuracy of 93.91%, 96.92% and 93.94%, respectively. The results demonstrate that the approach presented is as precise as the best contemporary methods investigated using the same ECG datasets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".