Determining the Accuracy of Sleep and Activity Patterns in Patients Undergoing Long-Term Ambulatory ECG Monitoring

Authors: Richard Bogan, MD, Elaine Yu, PhD, Vincent Mysliwiec, MD, Mitchell Miller, MD, Ardit Kacorri, MD, Yuriko Tamura, MS, Anthony Battisti, PhD, Vladimir Fokin, PhD, Evangelos Hytopoulos, PhD, Andrew Gilbert, PhD, Charlotte Bame, MBA, and Mintu Turakhia, MD.

Summary:

Prospective study of 81 participants across 3 sites evaluating an AI algorithm embedded in the Zio® ambulatory ECG patch to classify sleep, wake, and activity patterns using accelerometry, validated against polysomnography (PSG) and actigraphy.

Key Findings:

Zio sleep and activity detection feasible: positive predictive value (PPV) of 90.6% for sleep and 87% PPV for wake hours

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