A robust fusion algorithm of LBP and IMF with recursive feature elimination-based ECG processing for QRS and arrhythmia detection

Lee, Miran; Lee, Joo‐Ho

doi:10.1007/s10489-021-02368-5

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…In this case, automatic and computerized systems can be more useful. A traditional automatic arrhythmia recognition system includes (i) preprocessing [ 2 ], (ii) features extraction such as beat segmentation [ 3 ], QRS complex [ 4 ], R-peak or R-R interval [ 5 ], wavelet transform (WT) [ 5 ], time-frequency [ 6 ], morphological learning [ 6 ], and (iii) classification such as artificial neural network (ANN) [ 7 ], support vector machine (SVM) [ 8 ], decision tree (DT) [ 9 , 10 ], and random forest (RF) [ 8 ] steps. However, despite a good number of shallow learning methods (features engineering techniques) with promising results for identifying arrhythmias from ECG signals, these are unable to properly describe the optimal features of signals and are prone to overfitting [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Ullah

Heyat

Akhtar

et al. 2022

Computational Intelligence and Neuroscience

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Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.

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Section: Introductionmentioning

confidence: 99%

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Ullah

Heyat

Akhtar

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Based on various feature selection strategies, three categories of feature selection algorithms can be distinguished: Filter [3][4][5][6][7][8], Wrapper [9][10][11][12][13][14][15], and Embedded [16][17][18][19][20][21][22][23][24][25][26]. The Filter method primarily relies on a vast variety of statistical metrics or informationtheoretic measures to assess the usefulness or duplication of data aspects and thereafter choose the most advantageous variables for modeling.…”

mentioning

confidence: 99%

A More Flexible and Robust Feature Selection Algorithm

Tu,

Su,

Tang

et al. 2023

IEEE Access

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With the rising number of actual data, the challenges of huge model operations and poor generalization capacity arise, making the selection of the right feature set a significant issue. This study proposes ImprovedRFECV, an enhanced feature selection approach for cross-validated recursive feature elimination (RFECV). The algorithm first increases the robustness of the optimal feature subset by randomly sampling different data, building multiple models, and comparing the scores. Simultaneously, L1 regularization term and L2 regularization term scores are introduced to thoroughly evaluate the value of each feature, reducing the influence on the anti-interference term and further improving the algorithm's accuracy and stability. Additionally, a multi-model ensemble learning framework is utilized to promote generalization ability and effectively prevent overfitting. Finally, a both-end expansion removal strategy is adopted to solve the problem of strong covariance of features while enhancing the flexibility of the algorithm. The experimental results show that, when compared to the RFECV algorithm, the ImprovedRFECV algorithm obtains fewer optimal average features and performs better on the optimal feature subset on five datasets from five different domains, demonstrating the algorithm's high robustness and generalization ability.

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A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

2022

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A robust fusion algorithm of LBP and IMF with recursive feature elimination-based ECG processing for QRS and arrhythmia detection

Cited by 9 publications

References 45 publications

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

A More Flexible and Robust Feature Selection Algorithm

A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

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