Multi-Label Classification of Multi-lead ECG Based on Deep 1D Convolutional Neural Networks With Residual and Attention Mechanism

Liu, Yamin; Xie, Hanshuang; Cao, Qineng; Yan, Jiayi; Wu, Fan; Zhu, Huaiyu; Pan, Yun

doi:10.23919/cinc53138.2021.9662873

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…ClementVirgeniya and Ramaraj (2021) tried to feed the data into the adaptive synthetic (ADASYN) (He et al, 2008) based sampling model, which utilized a weighted distribution for different minority class samples depending upon the learning stages of difficulty, instead of using synthetic models such as synthetic minority oversampling technique (SMOTE). Liu et al (2021) augmented the ECG data by using a band-pass filter, noise addition, time-frequency transformation, and data selection. Data augmentation is also a good method to deal with imbalanced ECG dataset (Qiu et al, 2022a).…”

Section: Related Workmentioning

confidence: 99%

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GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

Zhu¹,

Jielin²,

Yang³

et al. 2022

Preprint

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There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a

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Section: Related Workmentioning

confidence: 99%

“…Kantorovich rediscovered it under a different formalism, namely the Linear Programming formulation of OT. With the development of scalable solvers, OT is widely applied to many real-world problems (Zhu et al, 2021;Flamary et al, 2021).…”

Section: Data Augmentation For Robust Electrocardiogram Predictionmentioning

confidence: 99%

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

Zhu¹,

Jielin²,

Yang³

et al. 2022

Preprint

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“…•Noise addition: The ECG signal x is modified by adding Gaussian random noise n. The noise n is generated by a random generator with a mean of 0 and a standard deviation of σ. Mathematically, the generated signal can be expressed as: x + n[19,[38][39][40][41][42][43][44][45]]. •Scaling: Each lead of the ECG signal is scaled by a random factor that is drawn from a normal distribution[21,25,[38][39][40]42,46].…”

mentioning

confidence: 99%

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

Rahman

Rivolta

Badilini

et al. 2023

Sensors

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AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.

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Reduced lead ECG multi-label classification with higher generalization using 2D SEResnets with self attention

Sheikh,

Verma,

Chauhan

2024

Multimed Tools Appl

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Multi-Label Classification of Multi-lead ECG Based on Deep 1D Convolutional Neural Networks With Residual and Attention Mechanism

Cited by 7 publications

References 9 publications

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

Reduced lead ECG multi-label classification with higher generalization using 2D SEResnets with self attention

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