Shearlet and contourlet transforms for analysis of electrocardiogram signals. (July 2018)

• Record Type: Journal Article
• Authors: Amorim, Paulo; Moraes, Thiago; Fazanaro, Dalton; Silva, Jorge; Pedrini, Helio
• Is Part Of: Computer methods and programs in biomedicine. Volume 161(2018)
• Page Start: 125
• Page End: 132
• Publication Date: 2018-07
• Subjects: Electrocardiogram signals -- Heart rhythm -- Shearlets -- Contourlets -- Signal classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Detection of patterns in ECG signals with high accuracy. Development of a novel methodology based on curvelet and shearlet transforms. Extraction of a set of discriminative characteristics from the signals. Evaluation on a public data set. Results superior/comparable to the literature. Abstract: Background and Objective: Cardiac arrhythmia is an abnormal variation in the heart electrical activity that affects millions of people worldwide. Electrocardiogram (ECG) signals have been widely used to assess and diagnose cardiac abnormalities. Methods: A novel methodology based on shearlet and contourlet transforms for automatically classify an input ECG signal into different heart beat types is proposed and evaluated in this work. Classifiers are trained through a set of features extracted from these time-frequency coefficients. Results: Tests are conducted on MIT-BIH data set to demonstrate the effectiveness of the proposed classification method. The shearlet and contourlet transforms achieved high classification accuracy rates. Conclusions: The developed system can help cardiologists obtain structural and functional information of the heart by means of ECG patterns, improving their diagnostic tasks.