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

Tung Hoang, Nicolas Fahier, Wai Chi Fang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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%.

Original languageEnglish
Title of host publicationBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006175
DOIs
StatePublished - Oct 2019
Event2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, Japan
Duration: 17 Oct 201919 Oct 2019

Publication series

NameBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019
CountryJapan
CityNara
Period17/10/1919/10/19

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