Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records

Liu, Ran; Zhao, Zhengbo; Chen, Xiao; Rong, Yu; Qiang, Sheng

doi:10.1016/j.cmpb.2020.105639

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…3,5,10,24 Other studies investigated the use (variational) auto-encoders 12-lead ECGs in smaller and showed that VAEs can be useful for compression of ECGs, data augmentation, clustering, and feature generation. [25][26][27][28] Interestingly, Kuznetsov et al 28 also deterthat factors are needed to encode a single or beat Our work makes the latent space of a VAE (i.e. FactorECG) clinically useful and explainable to physicians, by linking the ECG factors with known ECG measurements and diagnostic statements (Figures 4 and Tables 2 and 3), (ii) providing sive visualizations offline (Figure 2) and using an tool (https:// decoder.ecgx.ai), and (iii) showing that the ECG factors have equate predictive power in various downstream tasks.…”

Section: Discussionmentioning

confidence: 99%

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders

Leur

Bos

Taha

et al. 2022

European Heart Journal - Digital Health

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Aims Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to ‘black box’ DNNs in conventional ECG interpretation (AUROC 0.94 vs 0.96), detection of reduced EF (AUROC 0.90 vs 0.91) and prediction of one-year mortality (AUROC 0.76 vs 0.75). Contrary to the ‘black box’ DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.

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Section: Discussionmentioning

confidence: 99%

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders

Leur

Bos

Taha

et al. 2022

European Heart Journal - Digital Health

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“…Human experts typically perform better at ECG morphological recognition than deep learning methods, due to the fact that there are an insufficient amount of positive samples. In [ 139 ], a pipeline is used that involves the VQ-VAE to generate new positive samples for data augmentation purposes. A classifier was then trained using this additional synthetic data to identify ten ECG morphological abnormalities, which resulted in an increase in the F1 score for the classifier.…”

Section: Applicationsmentioning

confidence: 99%

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Singh

Ogunfunmi

2021

Entropy

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Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.

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“…Many statistical models, such as the Gaussian mixture model (GMM) and Markov chain (MC) models have been proposed for generating new samples [11][12][13]. Learning-based models such as variational autoencoder (VAE) [14] and generative adversarial networks (GANs) [15], are relatively new models developed recently for the generation of ECGs. The paper is structured as follows:…”

Section: Introductionmentioning

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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Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records

Cited by 15 publications

References 24 publications

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

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

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