DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices

Autor: Soto, Jessica Torres, Ashley, Euan

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wear

Externí odkaz: http://arxiv.org/abs/2001.00155

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Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy ^.

Autor: Soto JT; Department of Biomedical Data Science, Stanford University, USA., Weston Hughes J; Department of Computer Science, Stanford University, USA., Sanchez PA; Department of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA., Perez M; Department of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA., Ouyang D; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, USA.; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, USA., Ashley EA; Department of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA.

Publikováno v: European heart journal. Digital health [Eur Heart J Digit Health] 2022 May 23; Vol. 3 (3), pp. 380-389. Date of Electronic Publication: 2022 May 23 (Print Publication: 2022).