ID: 4349938 AN AUTOMATED TOOL FOR EXTRACTION AND PARSING OF ECG WAVEFORMS FROM ECG REPORTS TO TRAIN MACHINE LEARNING MODELS
Manual analysis of ECG reports can be time-consuming and difficult to perform. Machine learning (ML) models trained on images rather than time-series data may associate non-physiologic features with desired outcomes, reducing generalizability.
Authors
- Albert J. Rogers (ORCID: https://orcid.org/0000-0001-6585-534X)
- Sanjiv M. Narayan (ORCID: https://orcid.org/0000-0001-7552-5053)
- Tina Baykaner (ORCID: https://orcid.org/0000-0002-0476-2972)
- Marco Valentin Perez (ORCID: https://orcid.org/0000-0003-2746-0202)
- Kelly Brennan
- Andy Liu (ORCID: https://orcid.org/0000-0003-3096-1788)
- Prasanth Ganesan (ORCID: https://orcid.org/0000-0002-1885-0690)
- Sabyasachi Bandyopadhyay (ORCID: https://orcid.org/0000-0003-4617-7832)
- Rayan Ansari
- Alexander C. Perino
Publication Details
- Journal
- Heart Rhythm O2
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1016/j.hroo.2025.07.086
- Primary Topic
- Environmental Monitoring and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00