End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

27 July 2018

Papers citing "End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables"

6 / 6 papers shown

Title
A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis Ciaran Bench Oskar Pfeffer Vivek Desai Mohammad Moulaeifard Loic Coquelin Peter H. Charlton Nils Strodthoff Nando Hegemann Philip Aston Andrew Thompson 117 0 0 31 Oct 2025
Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels N. Nasir Mustafa Sameer F. Barneih O. Alshaltone Muneeb Ahmed 226 3 0 23 May 2024
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques M. H. Chowdhury Md Nazmul Islam Shuzan M. Chowdhury Z. Mahbub Mohammad Monir Uddin Amith Khandakar M. Reaz 140 200 0 07 May 2020
Teacher-Student Domain Adaptation for Biosensor Models Lawrence Phillips David B. Grimes Yihan Li OOD 81 3 0 17 Mar 2020
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices J. Soto Euan A. Ashley 115 8 0 01 Jan 2020
Deep Learning in Cardiology Paschalis A. Bizopoulos D. Koutsouris MedIm 215 153 0 22 Feb 2019