Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification. (November 2017)

• Record Type: Journal Article
• Authors: Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh
• Is Part Of: Computer methods and programs in biomedicine. Volume 151(2017)
• Page Start: 71
• Page End: 78
• Publication Date: 2017-11
• Subjects: Atrial Blood Pressure (ABP) -- Discrete Wavelet Transformation (DWT) -- Electrocardiogram (ECG) -- Fusion -- Multi-Layer Perceptron Neural Network (MLPNN); 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.2017.08.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel wavelet based fusion method is employed in this article. Fusion of ECG and ABP signal together causes increase the cardiac arrhythmias classification accuracy. Applying the proposed fusion technique, the classification accuracies increased considerably. Abstract: Background and Objective: Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. Methods: These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. Results: In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. Conclusions: It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments.