Open AccessRobust deep learning pipeline for PVC beats localization
Author(s) -
Bohdan Petryshak,
Illia Kachko,
Mykola Maksymenko,
Oles Dobosevych
Publication year - 2021
Publication title -
technology and health care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.281
H-Index - 44
eISSN - 1878-7401
pISSN - 0928-7329
DOI - 10.3233/thc-218045
Subject(s) - pipeline (software) , computer science , benchmarking , artificial intelligence , pattern recognition (psychology) , artificial neural network , task (project management) , encoder , f1 score , deep learning , signal (programming language) , machine learning , speech recognition , engineering , systems engineering , marketing , business , programming language , operating system
Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats.