Multi-Leads ECG Premature Ventricular Contraction Detection using Tensor Decomposition and Convolutional Neural Network

Tung Hoang, Nicolas Fahier, Wai Chi Fang*

*此作品的通信作者

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

Premature Ventricular Contraction refers to irregular heartbeat and is one common symptom to several heart diseases. Currently, physiological databases are not only large in volume but also complex in dimensional aspect, so that intelligent systems that can process multi-dimensional data to detect Premature Ventricular Contraction (PVC) are highly needed. In this paper, we propose novel models of combinations of multi-leads ECG from the 12 lead ECG St. Petersburg Arrhythmias database to detect PVCs and optimize the required data pre-processing resources for Convolutional Neural Network(CNN) implemented on wearable devices. Although exhibiting fewer performances than previous works, the proposed method is able to perform automatic features extraction, reduce the CNN complexity and is scalable to be applied to 3-Lead to 16-Lead ECG systems. The combination scenarios include Wavelet fusion method and Tucker-decomposition before CNN is deployed as a classifier. The achieved accuracy to detect PVC for tensor-based feature extraction, the most optimized processing technique, is 90.84% with a sensitivity of 78.60% and a specificity of 99.86%.

原文English
主出版物標題BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781509006175
DOIs
出版狀態Published - 10月 2019
事件2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, 日本
持續時間: 17 10月 201919 10月 2019

出版系列

名字BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019
國家/地區日本
城市Nara
期間17/10/1919/10/19

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