Hybrid hierarchical method for electrocardiogram heartbeat classification

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.

Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks

“…In contrast, in this study, the proposed approaches using only data from lead 1 achieved better results than other studies did, proving the robustness of the proposed approach. It is worth mentioning that other studies, such as [3] and [43], achieved their average precision by using data from leads 1 and 2, as well as developing a fusion step to make an accurate final decision, which dramatically increased the computational time.…”

“…Furthermore, the heartbeats of the minor classes need to be classified precisely because high-risk patients usually belong to these classes. In contrast, although [3], [43] considered the precision for the classes, they utilized data from leads 1 and 2, in addition to developing a fusion step to improve the results, which increased the computational time. Most studies also do not handle the imbalance problem in the MIT-BIH dataset, which negatively affects the achieved accuracy for classes with few heartbeats.…”

“…In this study, the beats of utilized records from lead 1only were divided into training and testing sets. For comparison's sake, the data division in [3] and [43] has been followed. The percentages of training and testing sets were not the same for all classes because the numbers of beats for the classes were not equally distributed.…”

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

“…In particular, the normal and abnormal classes in the testing dataset were only 24 and 20 heartbeats, respectively. In practice, filtering noise using the WT method is applied for feature extraction of the ECG signals for the purpose of improving the efficiency in heart disease classifiers [15]- [17]. It is obvious that different wavelet families have been employed for filtering noises or decomposing ECG signals for enhancing classification accuracy.…”

An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

“…A linear procedure using the DWT feature extraction method and the principal component analysis (PCA) feature reduction method in order to obtain ECG signal classification was applied in [ 14 ]. Some of the recently proposed works about the ECG signal analysis that utilized PCA for feature dimension reduction are given in [ 15 , 16 , 17 ]. The linear methods in ECG analyses provide good classification accuracy in free noise conditions but in the presence of noise, these linear techniques cannot acquire the maximum of accuracy [ 18 ].…”