Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier

Pandeyz, Saroj Kumar; Janghel, Rekh Ram; Gupta, Vaibhav

doi:10.1002/9781119769262.ch8

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Wavelet transform can maximize the local characteristics of the signal through mathematical transformation and realize time-frequency localization analysis. The mathematical transform is used to decompose the signal data on multiple scales, to process the high and low frequencies of the signal separately [ 20 ], to satisfy the adaptive analysis of various frequencies, and then to realize the local analysis of any part of the signal, which overcomes the difficult problem of Fourier transform in signal processing. Wavelets are obtained from fundamental wavelets by mathematical transformation “stretching translation”.…”

Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

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Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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“…Celin [5] conducted a comparative study of various machine learning classifiers and found that the plain Bayes classifier performed well in terms of accuracy. [6] Employed an XGBoost classifier and a multi-stage processing technique, which included steps such as data acquisition, noise filtering, and feature extraction, for 45 feature descriptors. This approach significantly enhanced the classification accuracy of ECG signals.…”

Section: Introductionmentioning

confidence: 99%

Classification of ECG Signals Based on Hilbert-Huang Transform and 1D Convolution Neural Network

Tang

2024

AETR

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Arrhythmia is a common phenomenon in cardiovascular diseases and its accurate diagnosis typically relies on a thorough analysis of electrocardiogram (ECG). Therefore, accurate identification and classification of ECG signals play a crucial role in the effective treatment of cardiac diseases. In this study, we propose a deep learning model based on Hilbert-Huang Transform (HHT) and one-dimensional convolutional neural network (1D-CNN), aiming to enhance the accuracy of ECG signal classification. Specifically, we first perform time-frequency analysis and feature extraction of the signal using the Hilbert-Huang Transform, after which we further extract features and classify the signal using 1D Convolutional Neural Network. The experimental results show that the model proposed in this paper performs well in classifying four types of ECG signals, with average classification accuracies of 97.73%, 99.16%, 99.50%, and 99.88%, respectively. This not only proves the effectiveness of our proposed method but also provides important technical support for the diagnosis and treatment of cardiovascular diseases, which has far-reaching clinical application value.

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“…Owing to data booming from more robust data acquisition and efficient algorithms, machine learning (ML) has been proven as an effective tool for regression and classification tasks, and has recently attracted great interests in many fields including sciences, engineering, health science; see for examples [37,38] and references therein. ML has provided variety of methods to classification and prediction [39,40].…”

Section: Introductionmentioning

confidence: 99%

Formation energy prediction of neutral single-atom impurities in 2D materials using tree-based machine learning

Kesorn,

Hunkao,

Na Talang

et al. 2024

Mach. Learn.: Sci. Technol.

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We applied tree-based machine learning algorithms to predict the formation energy of impurities in 2D materials, where adsorbates and interstitial defects are investigated. Regression models based on random forest (RF), gradient boosting regression (GBR), histogram-based gradient-boosting regression (HGBR), and light gradient-boosting machine (LightGBM) algorithms are employed for training, testing, cross validation, and blind testing. We utilized chemical features from fundamental properties of atoms and supplemented them with structural features from the interaction of the added chemical element with its neighboring host atoms via the Jacobi-Legendre (JL) polynomials. Overall, the prediction accuracy yields optimal $\text{MAE} \approx 0.518$, $\text{RMSE} \approx 1.14$, and $R^2 \approx 0.855$. When trained separately, we obtained lower residual errors RMSE and MAE, and higher $R^2$ value for predicting the formation energy in the adsorbates than in the interstitial defects. In both cases, the inclusion of the structural features via the JL polynomials improves the prediction accuracy of the formation energy in terms of decreasing RMSE and MAE, and increasing $R^2$. This work demonstrates the potential and application of physically meaningful features to obtain physical properties of impurities in 2D materials that otherwise would require higher computational cost.

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Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier

Cited by 4 publications

References 21 publications

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Classification of ECG Signals Based on Hilbert-Huang Transform and 1D Convolution Neural Network

Formation energy prediction of neutral single-atom impurities in 2D materials using tree-based machine learning

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