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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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. |
doi_str_mv | 10.3390/electronics11152280 |
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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.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics11152280</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Electronics (Basel), 2022-08, Vol.11 (15), p.2280</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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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.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Arrhythmia</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Cardiac arrhythmia</subject><subject>Classification</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Discriminant analysis</subject><subject>Electrocardiography</subject><subject>Feature extraction</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Signal classification</subject><subject>Signal processing</subject><subject>Support vector machines</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkDFPwzAUhC0EElXpL2CJxBywn-3YHiEqKVKhC0VskeM8o1QhKbY78O9JVQYGbrkbPt1JR8g1o7ecG3qHPboUxqFzkTEmATQ9IzOgyuQGDJz_yZdkEeOOTjKMa05nhC7LKluhDalBm7KytzF2vnM2deOQbWM3fGTl5uXtvXrInscW-yty4W0fcfHrc7J9XL6Wq3y9qZ7K-3XuQELKBboCi6YBrqQ0tlCN11SDQC5QaCkalF4JZRUHLqClrXeNQQYFs6A1eD4nN6fefRi_DhhTvRsPYZgma1CUSl5IqSaKnygXxhgD-nofuk8bvmtG6-M79T_v8B_Y71hA</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Rawi, Atiaf A.</creator><creator>Elbashir, Murtada K.</creator><creator>Ahmed, Awadallah M.</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-3431-6604</orcidid><orcidid>https://orcid.org/0000-0002-8763-9725</orcidid></search><sort><creationdate>20220801</creationdate><title>ECG Heartbeat Classification Using CONVXGB Model</title><author>Rawi, Atiaf A. ; 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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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics11152280</doi><orcidid>https://orcid.org/0000-0003-3431-6604</orcidid><orcidid>https://orcid.org/0000-0002-8763-9725</orcidid><oa>free_for_read</oa></addata></record> |
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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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