Healthy
Conditions
Keywords
electrocardiogram, wearable, artificial intellegence, Apple Watch
Brief summary
The purpose of this study is to show the artificial intelligence enhanced single-lead ECG Apple Watch has similar, robust performance comparable to an AI enhanced 12 lead ECG and AI enhanced single lead (LI) of a 12 lead ECG.
Detailed description
1. Ambulatory patients undergoing ECG recording in the Mayo Clinic outpatient ECG lab will be asked to consent for this study. 2. Those who consent for the study will be asked to record a ECG using a single-lead watch-based (Apple Watch series 5) recording at a visit for a clinically scheduled 12 lead ECG recording. 3. This watch-based ECG data will be recorded and analyzed in comparison to the near-simultaneously recorded outpatient 12 Lead ECG
Interventions
None listed
Sponsors
Mayo Clinic
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 89 Years
Healthy volunteers
Yes
Inclusion criteria
* Age ≥ 18 years and ≤ 89. * Able to give verbal consent. * Able to complete routine clinical 12 lead ECG tracing and single lead Apple Watch ECG tracing.
Exclusion criteria
* Individuals \< 18 and \> 89 years of age. * Unable to given verbal consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of 12 lead ECG features to single-lead watch-based ECG features | 12 months | The ECG interval differences (in milliseconds) between 12 Lead and collected single-lead watch-based ECG for PR, QRS, QT intervals will be determined and compared for each patient. |
| Arrhythmia comparison of 12 lead ECG to single-lead watch-based ECG | 12 months | A physician interpretation of patients' 12 lead ECG and single-lead watch-based ECG will be performed to determined underlying rhythm (i.e. sinus rhythm, atrial fibrillation etc) from each, and the results from these modalities will be compared. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Arrhythmia classification by physician overread of single-lead watch-based ECG | 12 months | A physician interpretation of the patient's single-lead watch-based ECG will occur as described in Outcome 2. The results of this ECG interpretation (i.e. sinus rhythm, atrial fibrillation, or inconclusive) will be compared to the watch/app-based rhythm auto-classification for each recorded single-lead watch-based ECG. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Artificial intelligence determination of patient age by single-lead watch-based ECG | 12 months | A previously developed AI algorithm to predict patient age from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield an ECG-predicted age. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and determine ECG age for each individual patient. This single-lead ECG age will be compared to the AI ECG age result determined from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system. |
| Artificial intelligence detection of heart failure by single-lead watch-based ECG | 12 months | A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of heart failure which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of heart failure (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system. |
| Artificial intelligence detection of amyloidosis by single-lead watch-based ECG | 12 months | A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of amyloidosis which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of amyloidosis (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system. |
| Artificial intelligence detection of silent/paroxysmal atrial fibrillation by single-lead watch-based ECG | 12 months | A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of silent/paroxysmal atrial fibrillation which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of silent/paroxysmal atrial fibrillation (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system. |
| Artificial intelligence detection of aortic stenosis by single-lead watch-based ECG | 12 months | A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo single-lead watch-based ECG recording. This neural network uses PQRST complexes to yield a probability of aortic stenosis which may not be readily apparent via manual review. Each recorded single-lead watch-based ECG will undergo evaluation by this neural network and will produce a probability of aortic stenosis (0-100%) for each individual patient. This probability will be compared to the AI ECG result (probability 0-100%) from the patient's recently recorded 12 lead ECG which is routinely available for all patients with a recorded 12 lead ECG at our medical system. |
Countries
United States
Outcome results
None listed