Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Abstract

Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.

Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data

“…The multi-model by stacking ensemble learning method has outstanding performance in many fields, and it also has good applicability in disease diagnosis tasks in the medical field [ 22 , 23 , 24 , 25 ]. Wang et al [ 22 ] proposed a stacking-based ensemble learning method that simultaneously constructed the diagnostic model and extracted interpretable diagnostic rules.…”

“…Hammam et al [ 24 ] proposed a stacking deep learning methodology to produce the best results of COVID-19 classification, which produced test accuracy of 98.6%. Ji et al [ 25 ] proposed a classification strategy of multi-feature combination and the Stacking-DWKNN algorithm, which consisted of four modules. The average accuracy obtained was 99.01%.…”

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia

“…Generally, the features of ECG signals extracted by researchers mainly include morphological features [ 7 ], interphase features [ 8 , 9 ], wavelet transform features [ 10 ], higher-order statistics (HOS) [ 9 , 11 ], Hermite basis function (HBF) [ 12 ], QRS amplitude vector [ 13 ], and QRS composite wave area [ 14 ]. Then machine-learning algorithms are used for classification, such as the KNN algorithm [ 15 ], support vector machine (SVM) [ 7 ], and random forest [ 9 ]. Zhu et al [ 7 ] extracted the ECG morphological features and used the SVM algorithm to classify the heartbeat, achieving a high classification accuracy.…”

“…Yang et al [ 9 ] extracted a variety of features, including RR interval, wavelet coefficient, and high-order statistics, and then used the random forest classifier based on an extreme learning machine to detect arrhythmias. Ji et al [ 15 ] proposed a multifeature combination and stacked DWKNN algorithm to classify arrhythmias. The effects of different characteristic combinations on the classification of the heartbeat were analyzed.…”

“…e calculation formula of tree regularization is shown in (15), where Path Length(tree, x n ) is the path length of the n sample and Ω(W) is the average path length, namely, the penalty term.…”