Clincosm LogoSign in Get a demo

Automated Diagnostic Performance of Smartwatch ECG for Arrhythmia Detection Using the PulseAI Neural Network.

Sponsor
PulseAI Ltd (Industry)
Overall Status
Recruiting
CT.gov ID
NCT05668377
Collaborator
Beacon Hospital Research Institute, Dublin, Ireland (Other)
300
Enrollment
1
Location
12
Anticipated Duration (Months)
25
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

The study is to evaluate the performance of the PulseAI neural network technology at interpreting ECG data recorded using a single-lead Smartwatch (Apple Watch).

Condition or Disease Intervention/Treatment Phase
  • Device: PulseAI ECG Platform

Detailed Description

Under subject consent, subjects will have a 12-lead ECG immediately followed by a smartwatch ECG. The heart rhythm and ECG interval measurements will be compared between the 12-lead ECG and smartwatch ECG.

The arrhythmias will include:

  • Atrial Fibrillation/Flutter

  • Tachycardia

  • Bradycardia

  • Premature Atrial Contractions

  • Premature Ventricular Contractions

Study Design

Study Type:
Observational
Anticipated Enrollment :
300 participants
Observational Model:
Cohort
Time Perspective:
Prospective
Official Title:
Automated Diagnostic Performance of Smartwatch ECG for Arrhythmia Detection Using the PulseAI Neural Network.
Anticipated Study Start Date :
Jan 1, 2023
Anticipated Primary Completion Date :
Jan 1, 2024
Anticipated Study Completion Date :
Jan 1, 2024

Outcome Measures

Primary Outcome Measures

  1. Evaluate the performance of arrhythmia detection. [12 months]

    Evaluate the performance of arrhythmia detection by the PulseAI neural network applied to ECG data collected via the Smartwatch compared to physicians' interpretation of the 12-lead ECG.

Secondary Outcome Measures

  1. Evaluate the performance of the Apple Watch ECG interpretation Software compared to the PulseAI software [12 months]

    Evaluate the performance of the Apple Watch ECG interpretation Software compared to the PulseAI neural network

  2. Evalaute the performance of QTc interval measurement [12 months]

    Evaluate the performance of QTc interval measurement by the PulseAI neural network applied to the Smartwatch ECG data compared to physicians' measure of the QTc interval from the 12-lead ECG.

Eligibility Criteria

Criteria

Ages Eligible for Study:
22 Years to 80 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
Yes
Inclusion Criteria:

  • Patient aged 22 or older, able and willing to participate in the study

  • Patient admitted to hospital for ablation, cardioversion, or cardiac electrophysiological exploration.

  • Patient who has read the information note and has given their consent before any procedure related to the study.

Exclusion Criteria:
  • Patient with a pacemaker, implantable defibrillator or cardiac resynchronisation therapy device.

Contacts and Locations

Locations

Site City State Country Postal Code
1 Beacon Hospital Research Institute Dublin Ireland

Sponsors and Collaborators

  • PulseAI Ltd
  • Beacon Hospital Research Institute, Dublin, Ireland

Investigators

  • Principal Investigator: David Burke, PhD, Beacon Hospital Research Institute, Dublin

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
PulseAI Ltd
ClinicalTrials.gov Identifier:
NCT05668377
Other Study ID Numbers:
  • 1
First Posted:
Dec 29, 2022
Last Update Posted:
Dec 29, 2022
Last Verified:
Dec 1, 2022
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
Yes
Product Manufactured in and Exported from the U.S.:
No
Additional relevant MeSH terms:
Arrhythmias, Cardiac

Study Results

No Results Posted as of Dec 29, 2022