News | ECG Monitoring Services | May 16, 2019

Preventice Solutions Presents Real-World Performance Data on BodyGuardian Remote Monitoring System With AI

Wearable ECG monitoring technology uses deep learning algorithms to detect atrial fibrillation

Preventice Solutions Presents Real-World Performance Data on BodyGuardian Remote Monitoring System With AI

May 16, 2019 — Preventice Solutions presented clinical data validating its BodyGuardian Remote Monitoring System with the BeatLogic deep learning platform at Heart Rhythm 2019, the Heart Rhythm Society’s (HRS) 40th Annual Scientific Sessions, May 8-11 in San Francisco. This technology leverages machine learning and artificial intelligence (AI) for detection of atrial fibrillation (AF) and was validated using clinician adjudicated data.

The BodyGuardian Remote Monitoring System is designed to create a constant connection to monitor cardiovascular data in patients outside the clinic while they go about their daily activities. The data was presented by Hamid Ghanbari, M.D., MPH, FACC from University of Michigan in Ann Arbor, and Ben Teplitzky, Ph.D., and Mike McRoberts, from the Preventice data science team.

"One of the exciting advances in the diagnosis of AF is the use of machine learning techniques and deep learning technology because it can allow physicians to manage the massive amount of data that is collected," said Ghanbari, a cardiovascular electrophysiologist at the University of Michigan, where he treats patients who have arrhythmias. "Sensor technologies are creating so much data it's not feasible for physicians to be able to manage and review all of it. With accurate artificial intelligence to identify AF episodes, physicians can focus more on how their patients are feeling and the treatment approach they should take in each case. Artificial intelligence is freeing up the human potential with remote monitoring technologies."

Results from the study demonstrate how the BeatLogic deep learning platform is used to accurately detect the beginning and end of arrhythmias, ensuring accurate burden calculations and maximizing clinical value. The platform leverages multiple deep neural networks to detect AF episodes at rates that meet or exceed the best reported values within the literature. Perfect detection performance was achieved for AF episodes lasting more than one minute.

The study evaluated the AF detection performance of the Preventice BeatLogic platform using real-world clinician adjudicated data. The BeatLogic platform consists of multiple deep neural networks, which were trained using data from 10,946 BodyGuardian Heart patients. Performance was measured using real-world BodyGuardian Heart data from 512 patients that was annotated and then adjudicated by three board certified electrophysiologists. Specific results showed:

  • AF duration sensitivity (Se) and a positive predictive value (PPV) were 95.9 percent and 99.2 percent, respectively;
  • Episode detection Se and PPV were 96.7 percent; and
  • Episode detection Se and PPV increased to 100 percent for AF episodes with duration >1 minute.

Wearable patch electrocardiogram (ECG) monitoring quantifies AF burden using a combination of algorithms and trained technicians. New deep learning algorithms have improved the performance with respect to accurately detecting the presence of AF using algorithms. By leveraging multiple deep learning networks, the Preventice system is capable of accurately capturing the beginning and end of AF episodes, providing physicians with important clinical context for determining the appropriate treatment approach.

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