ID: 4348275 VECTORCARDIOGRAPHY-BASED DEEP LEARNING APPLIED TO A NOVEL CREDIT CARD SIZED DEVICE FOR ATRIAL FLUTTER AND ATRIAL FIBRILLATION DETECTION
A vectorcardiogram (VCG) represents 3-dimensional cardiac electrical activity in X, Y, and Z axes. A novel credit card sized device (NCCD) (HeartBeam, Santa Clara, CA, USA) was developed to simplify VCG acquisition, which may be advantageous as current standard and portable 12-lead ECG systems require multi-electrode configurations. Yet, it is unclear how a deep learning algorithm applied to signals from this device compares to a standard 12-lead ECG device in detecting atrial fibrillation (AF), atrial flutter (AFL), and sinus rhythm (SR).
Authors
- Peter Zimetbaum (ORCID: https://orcid.org/0000-0002-7958-7558)
- Branislav Vajdić
- Dejan Vukajlović
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Vladimir Atanasoski (ORCID: https://orcid.org/0000-0003-1695-6341)
- Vivek Y. Reddy (ORCID: https://orcid.org/0000-0002-5638-4993)
- Joshua Lampert (ORCID: https://orcid.org/0000-0003-0081-0356)
- Alexei Shvilkin (ORCID: https://orcid.org/0000-0003-3662-9938)
- Deepak Bhatt
- Abdulhalik Oguz
Publication Details
- Journal
- Heart Rhythm O2
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1016/j.hroo.2025.07.034
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00