Detection of Common Arrhythmias by the Watch-PAT: Expression of Electrical Arrhythmias by Pulse Recording
Bibliographic record
Abstract
Background: The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea and is widely used worldwide as an ambulatory diagnostic tool. While it records peripheral arterial tone (PAT) and not electrocardiogram (ECG), the ability of it to detect arrhythmias is unknown and was not studied previously. Common arrhythmias such as atrial fibrillation (AF) or premature beats may be uniquely presented while recording PAT/pulse wave. Purpose: To examine the potential detection of common arrhythmias by analyzing the PAT amplitude and pulse rate/volume changes. Patients and Methods: Patients with suspected sleep disordered breathing (SDB) were recruited with preference for patients with previously diagnosed AF or congestive heart failure (CHF). They underwent simultaneous WP and PSG studies in 11 sleep centers. A novel algorithm was developed to detect arrhythmias while measuring PAT and was tested on these patients. Manual scoring of ECG channel (recorded as part of the PSG) was blinded to the automatically analyzed WP data. Results: A total of 84 patients aged 57±16 (54 males) participated in this study. Their BMI was 30±5.7Kg/m2. Of them, 41 had heart failure (49%) and 17 (20%) had AF. The sensitivity and specificity of the WP to detect AF segments (of at least 60 seconds) were 0.77 and 0.99, respectively. The correlation between the WP derived detection of premature beats (events/min) to that of the PSG one was 0.98 (p<0.001). Conclusion: The novel automatic algorithm of the WP can reasonably detect AF and premature beats. We suggest that when the algorithm raises a flag for arrhythmia, the patients should shortly undergo ECG and/or Holter ECG study.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".