Two Recently Described Methods of Recording Multi-Lead ECGs with an Apple Watch
Bibliographic record
Abstract
I would like to bring the readers' attention to 2 recent articles that describe how to record multiple-lead electrocardiograms (ECGs) using an Apple watch.1Avila C.O. Novel use of Apple Watch 4 to obtain 3-lead electrocardiogram and detect cardiac ischemia.Perm J. 2019; 23: 99-102Crossref Scopus (16) Google Scholar,2Cobos Gil M.A. Standard and precordial leads obtained with an Apple Watch.Ann Intern Med. 2020; 172: 436-437Crossref PubMed Scopus (15) Google Scholar The Apple watch series 4 and 5 models come with a native application called the ECG app. This app uses hardware built into the wearable device to record a single-lead rhythm strip. The software takes a 30-s recording and analyzes the recording for arrhythmias. The ECG app on the watch then pairs with the health app on an iPhone to display the data. The health app produces an image of the electrical activity and automatically generates 1 of 5 possible interpretations: sinus rhythm, atrial fibrillation, low heart rate, high heart rate, or inconclusive. Apple states that in a trial of 600 patients the ECG app demonstrated 100% specificity for normal sinus rhythm and 98% specificity for atrial fibrillation.3Apple SupportTaking an ECG with the ECG app on Apple watch series 4 or later.https://support.apple.com/en-us/HT208955Date accessed: April 3, 2020Google Scholar The Apple watch detects cardiac electrical signals using electrodes built into the watch's case. A negative electrode is built into the digital crown, and multiple positive electrodes are in the back of the watch. To make a recording, Apple directs the user to wear the watch on one wrist and touch the digital crown with a finger from the opposite hand. When used this way, the ECG app produces a recording in the lead I orientation. Both of the articles highlighted in this letter describe recording multiple-lead ECGs by moving the watch into different positions on the body to record various leads in a sequential fashion. The first article describes a method of recording a 3-lead ECG.1Avila C.O. Novel use of Apple Watch 4 to obtain 3-lead electrocardiogram and detect cardiac ischemia.Perm J. 2019; 23: 99-102Crossref Scopus (16) Google Scholar Lead I is recorded as just described. Lead II is recorded by placing the back of the watch onto the midabdomen and touching the digital crown with the right index finger. Lead III is recorded by placing the watch on the midabdomen and touching the crown with the left index finger (Figure 1). The author describes using this method to record 3-lead ECGs in 2 patients with signs of an ST-elevation myocardial infarction (STEMI) on traditional 12-lead ECGs. Both patients presented with ST-segment elevation in leads III/aVF and reciprocal ST-segment depression in leads I/aVL. Copies of the ECGs are provided in the article. In both cases the images generated using the Apple watch accurately matched the waveforms of the 12-lead ECG. Both of these patients underwent cardiac catherization and had confirmed occlusions in their right coronary arteries. The second article describes a similar process for recording a 9-lead ECG using an Apple watch.2Cobos Gil M.A. Standard and precordial leads obtained with an Apple Watch.Ann Intern Med. 2020; 172: 436-437Crossref PubMed Scopus (15) Google Scholar Lead I is again recorded as previously described. Leads II and III are recorded by placing the back of the watch onto an ankle and then touching the digital crown with a finger from the right hand or left hand, respectively. The article then describes recording bipolar chest-right arm (CR) leads as a substitute for unipolar precordial (V) leads. Research dating back to the 1990s has shown that the recordings from bipolar CR leads closely resemble recordings by unipolar V leads.4De Gasperi R.N. Duncan R.C. Lowery M.H. Chakko C.S. Sequeira R.F. Comparison of digitized ECGs simultaneously recorded with CR and V leads.J Electrocardiol. 1995; 28: 287-295Crossref PubMed Scopus (2) Google Scholar,5De Gasperi R.N. McCulloh D.H. CR Leads in cardiac emergencies. A preliminary study.Chest. 1991; 99: 904-910Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar The author recorded CR leads by placing the back of the watch in each of the 6 standard precordial locations and touching the crown with a finger from the right hand. The article presents the recordings from 3 individuals who had both a 9-lead ECG with an Apple watch and a traditional 12-lead ECG completed. One individual is an asymptomatic adult, the second is a patient diagnosed with an acute NSTEMI, and the third is a patient diagnosed with an acute STEMI. The images generated using the watch closely resemble the recordings from the 12-lead ECG. The methods described in these articles have some significant limitations. Each lead must be recorded sequentially, so each lead represents the electrical activity of the heart at a different moment in time. The health app will not display multiple recordings at the same time, so a clinician is unable to look at information from multiple leads simultaneously. The software does not measure any characteristics of the electrical activity (eg, segment and interval lengths). The Apple watch has a limited battery life and requires daily recharging if the device is left on throughout the day. Both an Apple watch and an iPhone are required to generate an image of the electrical activity. The Apple watch will not pair with Android devices. Finally, and most importantly, both of these reports involve very small sample sizes and the methods must be further validated before they could be incorporated into clinical practice. Notwithstanding these limitations, the ability to measure multiple-lead ECGs with limited and highly portable equipment would be an exciting development for clinicians who work in the wilderness and other low-resource environments. With the clinical information gained from measuring multiple leads, practitioners could improve patient care in many ways, including making better informed decisions around the timing and mode of patient evacuation, initiating early targeted interventions in the field, and activating advanced medical personnel and resources at higher levels of care prior to patient arrival. I believe we can reasonably expect to see more innovations of this kind in the near future as the market for wearable health and fitness technology grows and as more advanced technology is incorporated into these devices. I am looking forward to seeing how clinicians continue to adapt this technology to improve patient care, especially in the wilderness and remote setting.
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".