Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 3; pp. 664 - 675
• Main Authors: Kiranyaz, Serkan, Ince, Turker, Gabbouj, Moncef
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Convolutional Neural Networks; Databases, Factual; Electrocardiography; Electrocardiography - methods; Feature extraction; Humans; Kernel; Monitoring; Neural networks; Neural Networks (Computer); Neurons; Patient-specific ECG classification; Precision Medicine; real-time heart monitoring; Signal Processing, Computer-Assisted; Training; real-time heart monitoring; Convolutional neural networks (CNNs); patient-specific ECG classification

Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learnin...