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HeartBeam’s Deep Learning Algorithms Demonstrate High Rates of Accuracy for Detecting Arrhythmias

Data from HeartBeam’s deep learning algorithms, including data from this study, are planned to be used to support future FDA submissions to enhance forthcoming product offering

HeartBeam, Inc. (NASDAQ: BEAT), a medical technology company focused on transforming cardiac care by providing powerful personalized insights, today announced new study data demonstrating that there were no significant differences in the detection of atrial fibrillation, atrial flutter and sinus rhythm when HeartBeam’s deep learning algorithms were applied to the HeartBeam System or to standard 12-lead ECGs.

The new data was presented by Dr. Joshua Lampert, Cardiac Electrophysiologist and Medical Director of Machine Learning at Mount Sinai Heart in the Mount Sinai Hospital, during HRX Live 2025 in Atlanta.

In the study, ECG recordings were taken with both the HeartBeam System (which captures the heart’s electrical signals from three distinct directions) and a standard 12-lead ECG in 201 consecutive patients. Deep learning algorithms previously developed from over 10,000 standard 12-lead ECGs were applied to both sets of recordings and compared against diagnoses made by an expert panel of three electrophysiologists. The study found:

  • No significant differences in multiple accuracy measures between the HeartBeam group and the standard 12-lead ECG group across diagnosis of 131 sinus rhythm, 57 atrial fibrillation, and 13 atrial flutter rhythms.
  • High accuracy rates for detecting atrial fibrillation, atrial flutter and sinus rhythm in both groups (94.5% HeartBeam vs. 95.5% standard 12-lead ECG).

“This study represents an exciting step forward in making advanced cardiac monitoring more user-friendly and widespread,” noted Rob Eno, Chief Executive Officer of HeartBeam. “The comparable performance of deep learning algorithms applied to HeartBeam’s credit card-sized device with 3D, non-coplanar signals and traditional 12-lead ECG systems for detecting common arrhythmias like atrial fibrillation and flutter opens new avenues for patient care, particularly in settings where a full standard 12-lead ECG might be impractical.”

Data from HeartBeam’s deep learning algorithms, including data from this study, are planned to be used to support future FDA submissions to enhance forthcoming product offerings.

Read other clinical trials here.