HeartBeam, Inc. announced new data demonstrating that applying the company?s artificial intelligence (AI) algorithms to vectorcardiography (VCG) showed considerably improved performance in the detection of atrial flutter over single-lead electrocardiograms (ECGs) and similar performance to 12-lead ECGs, the standard for diagnosing atrial flutter. This marks the first scientific presentation on the company?s deep learning algorithm, HeartBeam AI. The data was presented by Vivek Reddy, MD, Director of Cardiac Arrhythmia Services at The Mount Sinai Hospital, during the European Heart Rhythm Association (EHRA) conference in Berlin, Germany.

In the study, HeartBeam AI with VCG demonstrated a 28% improvement over single-lead ECG in the detection of atrial flutter cases (sensitivity of 91.0% for VCG vs. 71.2% for single-lead ECG) without sacrificing the ability to identify those individuals without atrial flutter (specificity of 98.7% for VCG vs. 96.9% for single-lead ECG).

Smartwatches have become increasingly popular for detecting and monitoring abnormalities in the timing or pattern of heartbeats but only offer a single-lead ECG, which greatly limits their ability to detect a broad range of cardiac irregularities. Atrial flutter is a common irregularity, or arrhythmia, that typically requires a healthcare professional to administer a 12-lead ECG in a medical setting which is not always practical or even possible at the time of a cardiac event. HeartBeam?s core vectorelectrocardiography (3D VECG) technology captures the heart?s signals in three projections (X, Y, Z), similar to VCG, and synthesizes a 12-lead ECG.

The technology is designed to be used in HeartBeam?s small, portable, patient-friendly devices that allow for remote cardiac monitoring. The Company?s first planned application of the 3D VECG platform technology is the HeartBeam AIMIGo?, a credit card-sized device for patient use at home or anywhere, which is currently under review with FDA.