State of the art of mobile health technologies use in clinical arrhythmia care.

The rapid growth in consumer-facing mobile and sensor technologies has created tremendous opportunities for patient-driven personalized health management. The diagnosis and management of cardiac arrhythmias are particularly well suited to benefit from these easily accessible consumer health technologies. In particular, smartphone-based and wrist-worn wearable electrocardiogram (ECG) and photoplethysmography (PPG) technology can facilitate relatively inexpensive, long-term rhythm monitoring.

A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

Background: Automated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF.

Methods: 24,123 consecutive 12-lead ECGs recorded over 6 months were interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas® + physician), and 3) Cardiologs® algorithm.