Using the HAAR Wavelet Transform and K-nearest Neighbour Algorithm to Improve ECG Detection and Classification of Arrhythmia

Khairuddin, A. M.; Azir, Ku Nurul Fazira Ku

doi:10.1007/978-3-030-79463-7_26

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Signals are sent from one neuron to another via the Synapse (the link between neurons). The most popular uses of ANN [46] are in speech recognition, regression, and learning algorithms. To get the most out of ANN, it is critical to focus on the learning process and parameter optimization.…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Khafaga¹,

El-kenawy²,

Karim³

et al. 2023

Computers, Materials &Amp; Continua

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Electrocardiogram (ECG) signal is a measure of the heart's electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).

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Section: Multi-layer Perceptronmentioning

confidence: 99%

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Khafaga¹,

El-kenawy²,

Karim³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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Development of a Variety of Fast Machine Learning Model for ECG-based Arrhythmia Classifier

Zhang

Shin

Jung

et al. 2022

2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

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ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Mathunjwa

Lin²,

Lin³

et al. 2022

Sensors

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In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.

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Using the HAAR Wavelet Transform and K-nearest Neighbour Algorithm to Improve ECG Detection and Classification of Arrhythmia

Cited by 3 publications

References 16 publications

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Development of a Variety of Fast Machine Learning Model for ECG-based Arrhythmia Classifier

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

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