ECG Electrode Placements for Magnetohydrodynamic Voltage Suppression

Gregory, T. S.; Oshinski, John N.; Tse, Zion Tsz Ho

doi:10.3390/jimaging4070094

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…R-peak detection remains a preliminary step and conditions the rest of automated ECG analyses. State-of-the-art VCG-based approaches [49] are not fully reliable in magnetic fields of 3T or higher, forcing clinicians to move electrodes or use other techniques [50], potentially reducing MRI quality. In the worst case, the exam is not possible or interpretable by the physician.…”

Section: Discussionmentioning

confidence: 99%

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

Mehri,

Calmon,

Odille

et al. 2023

Sensors

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Recently, deep learning (DL) models have been increasingly adopted for automatic analyses of medical data, including electrocardiograms (ECGs). Large, available ECG datasets, generally of high quality, often lack specific distortions, which could be helpful for enhancing DL-based algorithms. Synthetic ECG datasets could overcome this limitation. A generative adversarial network (GAN) was used to synthesize realistic 3D magnetohydrodynamic (MHD) distortion templates, as observed during magnetic resonance imaging (MRI), and then added to available ECG recordings to produce an augmented dataset. Similarity metrics, as well as the accuracy of a DL-based R-peak detector trained with and without data augmentation, were used to evaluate the effectiveness of the synthesized data. Three-dimensional MHD distortions produced by the proposed GAN were similar to the measured ones used as input. The precision of a DL-based R-peak detector, tested on actual unseen data, was significantly enhanced by data augmentation; its recall was higher when trained with augmented data. Using synthesized MHD-distorted ECGs significantly improves the accuracy of a DL-based R-peak detector, with a good generalization capacity. This provides a simple and effective alternative to collecting new patient data. DL-based algorithms for ECG analyses can suffer from bias or gaps in training datasets. Using a GAN to synthesize new data, as well as metrics to evaluate its performance, can overcome the scarcity issue of data availability.

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Section: Discussionmentioning

confidence: 99%

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

Mehri,

Calmon,

Odille

et al. 2023

Sensors

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“…The ECG signal is recorded through electrocardiography in which 12 leads are placed on the body. For correct recording of the ECG, the placement of the leads is very important [ 2 ]. The correct positions of the leads and the components of the ECG signal are shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

Jamil

Rahman

2022

J. Imaging

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Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques.

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Verifying the Accuracy of Hemodynamic Analysis Using High Spatial Resolution 3D Phase-contrast MR Imaging on a 7T MR System: Comparison with a 3T System

Tajima,

Isoda,

Fukunaga

et al. 2023

magn reson med sci

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ECG Electrode Placements for Magnetohydrodynamic Voltage Suppression

Cited by 3 publications

References 29 publications

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

Verifying the Accuracy of Hemodynamic Analysis Using High Spatial Resolution 3D Phase-contrast MR Imaging on a 7T MR System: Comparison with a 3T System

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