Hybrid hierarchical method for electrocardiogram heartbeat classification. Issue 4 (1st June 2018)

• Record Type: Journal Article
• Authors: El‐Saadawy, Hadeer; Tantawi, Manal; Shedeed, Howida A.; Tolba, Mohamed F.
• Is Part Of: IET signal processing. Volume 12:Issue 4(2018)
• Page Start: 506
• Page End: 513
• Publication Date: 2018-06-01
• Subjects: electrocardiography -- discrete wavelet transforms -- feature extraction -- principal component analysis -- support vector machines -- trees (mathematics) -- signal classification -- medical signal processing; electrocardiogram heartbeat classification -- automatic reliable two‐stage hybrid hierarchical method -- heart rate variability -- discrete wavelet transform -- morphological features -- extracted features -- principle component analysis -- RR features -- support vector machine -- SVM -- stacked generalisation algorithm -- MIT BIH database -- decision trees classifier; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2017.0108
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : This paper proposes an automatic reliable two‐stage hybrid hierarchical method for ECG heartbeat classification. The heartbeats are segmented dynamically to avoid the consequences of the heart rate variability. Discrete Wavelet Transform (DWT) is utilized to extract morphological features. The extracted features are then reduced by using Principle Component Analysis (PCA). Subsequently, the resulted features along with four RR features are fed into Support Vector Machine (SVM) to classify five categories. Thereafter, the heartbeats are further classified to one of the classes belonging to the assigned category. Two different strategies for classification have been investigated: One versus All and One versus One. The proposed method has been applied on data from lead 1 and lead 2. A new fusion step is introduced, where stacked generalisation algorithm is applied and different types of classifiers have been examined. Experiments have been carried out using a MIT_BIH database. The best overall and average accuracies obtained by the first stage are 98.40% and 97.50% respectively. For the second stage, 94.94% and 93.19% are the best overall and average accuracies obtained respectively. The best results are achieved using SVM with one versus one classification strategy for both stages and decision trees classifier for the fusion step.