Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings

Accurate AF and PAC/PVC detection with minimal false positive detection can enhance clinical and public acceptance of smartwatch-based AF monitoring.

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Publication Details

Journal
IEEE Transactions on Biomedical Engineering
Published
2025-09-23
DOI
https://doi.org/10.1109/tbme.2025.3613471
Citations
4
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
2.70
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article

Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings

Tenes Paul, Om Ghetia, Timothy P. Fitzgibbons, Ji-Hye Moon et al.
4 citations
IEEE Transactions on Biomedical Engineering
Non-Invasive Vital Sign Monitoring
2.70
article

Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings

Tenes Paul, Om Ghetia, Timothy P. Fitzgibbons, Ji-Hye Moon, Edith Mensah Otabil, Alexander Hamel, Matthew Gottbrecht, Kamran Noorishirazi, Andrew Peitzsch, Jane S. Saczynski, Khanh‐Van Tran, Eric Ding, Ki H. Chon, Fahimeh Mohagheghian, David D. McManus, Youngsun Kong, Taylor Orwig, Luís Roberto Mercado Díaz, Syed Naeem, Dong Han, Emily L. Dickson, Nishat Nishita, Ziyue Wang, Danielle DiMezza, Darleen Lessard, Jordy Mehawej, Andreas Filippaios, Bruce Barton, Darren Chen, Devan Williams, Ohm Ghutadaria
article en
4 citations

Abstract

Accurate AF and PAC/PVC detection with minimal false positive detection can enhance clinical and public acceptance of smartwatch-based AF monitoring.

IEEE Transactions on Biomedical EngineeringVol. 73(4)
Northeastern University (US), University of Connecticut (US), University of Massachusetts Chan Medical School (US)
Openalex Percentile: Top 9%
Non-Invasive Vital Sign Monitoring
2.70
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