LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

Kalyakulina, Alena; Yusipov, Igor; Moskalenko, V. A.; Nikolskiy, Alexander V.; Kosonogov, K.A.; Osipov, Grigory V.; Zolotykh, Nikolai Yu.; Ivanchenko, Mikhail

doi:10.1109/access.2020.3029211

Cited by 44 publications

(23 citation statements)

References 31 publications

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“…In our experiment, we use 2, 033 10-s ECG signals of frequency 500 Hz (Kalyakulina et al, 2019 , 2020a , b ). We process them according to the principles as described above (see section 2.1) and train our network on the obtained 252, 636 cardiac cycles.…”

Section: Resultsmentioning

confidence: 99%

Interpretable Feature Generation in ECG Using a Variational Autoencoder

et al. 2021

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We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 3.83 × 10−3, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.

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Section: Resultsmentioning

confidence: 99%

Interpretable Feature Generation in ECG Using a Variational Autoencoder

et al. 2021

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“…In [ 8 ] the authors pushed the idea of using Convolutional Neural Networks even further and used deep fully convolutional network architecture called U-Net [ 9 ] that not only achieved a high F1 score on detecting QRS complex but was capable of detecting P and T offset. Although producing high results, the method cannot be considered reliable for R-peak detection as it was trained on the dataset with a very low occurrence of arrhythmia peaks (only eight records out of 200) [ 10 ]. In this study, we extend this approach to the PVC-rich datasets.…”

Section: Related Workmentioning

confidence: 99%

Robust deep learning pipeline for PVC beats localization

Petryshak

Kachko

Maksymenko

et al. 2021

THC

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BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

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“…The experiments used the ECG of patients from the LUDB dataset [13] -both those with pathologies and healthy ones. Example ECGs are shown in figure 1.…”

Section: Ecg Datamentioning

confidence: 99%

Problems of representation of electrocardiograms in convolutional neural networks

Sereda

Alekseev

Koneva

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Using electrocardiograms as an example, we demonstrate the characteristic problems that arise when modeling one-dimensional signals containing inaccurate repeating pattern by means of standard convolutional networks. We show that these problems are systemic in nature. They are due to how convolutional networks work with composite objects, parts of which are not fixed rigidly, but have significant mobility. We also demonstrate some counterintuitive effects related to generalization in deep networks.

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LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

Cited by 44 publications

References 31 publications

Interpretable Feature Generation in ECG Using a Variational Autoencoder

Interpretable Feature Generation in ECG Using a Variational Autoencoder

Robust deep learning pipeline for PVC beats localization

Problems of representation of electrocardiograms in convolutional neural networks

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