Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023

Ansari, Yaqoob; Mourad, Omar; Qaraqe, Khalid; Serpedin, Erchin

doi:10.3389/fphys.2023.1246746

Cited by 45 publications

(8 citation statements)

References 105 publications

(130 reference statements)

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“…The recent review work [25] reported that the recent capable deep-learning-based works perform 71~100% in sensitivity, 90.12~99.57% in specificity, and 96~99.53% in accuracy. It suggests that the proposed method performs better than or is comparable to the state of the art.…”

Section: Improvement Of Vf Detection With Orthogonality Features Thro...mentioning

confidence: 99%

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Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Oh,

Choi

2024

Electronics

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Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to detect abnormal electrocardiogram (ECG) signals associated with VF by measuring orthogonality between intrinsic mode functions (IMFs) derived from a data-driven decomposition method, namely, ensemble empirical mode decomposition (EEMD). The proposed method incorporates the decomposition of the ECG signal into its IMFs using EEMD, followed by the computation of the angles between subsequent IMFs, especially low-order IMFs, as the features to discriminate normal and abnormal ECG patterns. The proposed method was validated through experiments using a public MIT-BIH ECG dataset for its effectiveness in detecting VF ECG signals compared to conventional methods. The proposed method achieves a sensitivity of 99.22%, a specificity of 99.37%, and an accuracy of 99.28% with a 3 s ECG window and a support vector machine (SVM) with a linear kernel, which performs better than existing VF detection methods. The capability of the proposed method can provide a perspective approach for the real-time and practical computer-aided diagnosis of VF.

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Section: Improvement Of Vf Detection With Orthogonality Features Thro...mentioning

confidence: 99%

“…More recently, advanced machine-learning and deep-learning models in ECG analysis have been actively researched [24,25]. The use of deep learning might address the laborintensive issue of the handcrafted feature-extraction-based ECG analysis.…”

Section: Introductionmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Oh,

Choi

2024

Electronics

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“…Ribeiro et al [ 34 ] later used a residual network architecture, an architecture first developed by He et al [ 35 ] in the context of image classification, for the reliable diagnosis of 12-lead ECG signals. For a detailed review of deep learning applications in arrhythmia classification, we refer to the systematic reviews conducted by Xiao et al [ 36 ] and Ansari et al [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

Deep learning based ECG segmentation for delineation of diverse arrhythmias

Joung,

Kim,

Paik

et al. 2024

PLoS ONE

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Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy.

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“…Deep Learning (DL) is a branch of ML which has emerged as a powerful approach in the field of ECG and has earned significant attention due to its ability to extract complex features and model intricate relationships automatically, allowing for more nuanced and accurate assessments of cardiac health [8]. DL systems offer the potential for continuous, real-time monitoring and increased precision in interpreting ECG signals, thereby improving the likelihood of detecting intermittent arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

Vadillo-Valderrama,

Chaquet-Ulldemolins,

Goya-Esteban

et al. 2024

IEEE Access

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In recent years, a wide variety of Machine Learning (ML) algorithms, including Deep Learning (DL) methods, have been proposed for electrocardiogram (ECG) beat classification. However, accurately discerning ECG beat types faces challenges due to noise interference and inherent imbalances among different classes. Moreover, understanding mathematical models enclosed by black-box learning systems is an open issue today. Our study employed a manifold learning algorithm capable of mapping highdimensional data into a latent space to conduct a comprehensive analysis within a neural network learning framework. This approach involved the following studies: (1) Examining the intermediate high-dimensional latent space in simple architectures by studying its projection into a visualizable latent space; (2) Exploring the influence of class imbalance on the configuration of the latent space; (3) Evaluating and analyzing the compensatory effects of employing diverse DL architectures, such as modified autoencoders and Generative Adversarial Networks (GANs), specifically in generating data augmentation through synthetic beats. The experimental results demonstrated the effectiveness of our methodology in mitigating noise and addressing inter-class imbalance, notably enhancing the diagnostic Area Under the ROC Curve (AUC) in ECG signal analysis. Implementation of GAN data augmentation techniques resulted in a 2% improvement, elevating the AUC from 0, 9332 to 0, 9520 in the biclass dataset. Similarly, the AUC values increased by 2%, from 0, 9020 to 0, 9223, for the multiclass dataset. These findings highlight the impact of appropriate data augmentation techniques on AUC improvement. Furthermore, visualizing latent spaces during beat classifier design significantly contributes to the development of solid and principled multiclass beat-discriminating systems.

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Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023

Cited by 45 publications

References 105 publications

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Deep learning based ECG segmentation for delineation of diverse arrhythmias

Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

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