ECG Heartbeat Classification Using CONVXGB Model

ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has al...

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Veröffentlicht in:Electronics (Basel) 2022-08, Vol.11 (15), p.2280
Hauptverfasser: Rawi, Atiaf A., Elbashir, Murtada K., Ahmed, Awadallah M.
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creator Rawi, Atiaf A.
Elbashir, Murtada K.
Ahmed, Awadallah M.
description ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.
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subjects Accuracy
Algorithms
Arrhythmia
Artificial neural networks
Back propagation
Back propagation networks
Cardiac arrhythmia
Classification
Datasets
Deep learning
Discriminant analysis
Electrocardiography
Feature extraction
Machine learning
Neural networks
Signal classification
Signal processing
Support vector machines
title ECG Heartbeat Classification Using CONVXGB Model
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