Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Reyna, Matthew A.; Sadr, Nadi; Alday, Erick Andres Perez; Gu, Annie; Shah, Amit; Robichaux, Chad; Rad, Ali Bahrami; Elola, Andoni; Seyedi, Salman; Ansari, Sardar; Ghanbari, Hamid; Li, Qiao; Sharma, Ashish; Clifford, Gari D.

doi:10.23919/cinc53138.2021.9662687

Cited by 93 publications

(114 citation statements)

References 8 publications

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“…In the sequel, some of the important results, which can be derived for the ECG models studied in Section III, as the most popular ECG models in the literature, are reviewed. 1) Multi-beat parameter estimation: For regular morpholoincreasing the number of observed segments by synchronous averaging across multiple beats improves the performance bound (by a factor K), as shown in (33) and (34).…”

Section: Discussionmentioning

confidence: 98%

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Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

Fattahi¹,

Sameni²

2021

Preprint

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<div>Objective: Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameter estimation performed by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, depending on the selected data-model, the adopted estimation scheme (least-squares error, maximum likelihood, or Bayesian), and the prior assumptions on the model parameters and noise distributions, any estimation algorithm used in this context has an upper performance bound, which is not exceedable (for the same model and assumptions).</div><div><br></div><div>Method: In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature; namely bases expansions (including polynomials) and sum of Gaussian functions.</div><div><br></div><div>Results: The developed framework is evaluated over real and synthetic data, for three popular applications: T/R ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context, and compared with the derived theoretical CRLBs.</div><div>Conclusion and Significance: The proposed framework and the derived CRLBs provide fact-based guidelines for the selection of data-models, sampling frequency (beyond the Nyquist requirements), modeling segment length, the number of beats required for average ECG beat extraction, and other factors that influence the accuracy of ECG-based clinical parameter estimation.</div>

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Section: Discussionmentioning

confidence: 98%

“…A state-of-theart question is "how many and which ECG channels are most informative for extracting clinical ECG parameters? [34]". As shown in Fig.…”

Section: Crlb For Multichannel Ecgmentioning

confidence: 99%

Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

Fattahi¹,

Sameni²

2021

Preprint

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“…We experiment with five different combinations of available leads: 12-lead, 6-lead (I, II, III, aVF, aVL, aVR), 3-lead (I, II, V2), 2-lead (I, II), and 1-lead (I). These lead combinations are identical to the setup used in PhysioNet/Computing in Cardiology Challenge 2021 (Reyna et al, 2021), except for the 4-lead combination (I, II, III, V2). It was excluded because of the correlation between the limb leads, where lead III can be obtained by the simple equation of lead I and II (i.e.…”

Section: Fine-tuningmentioning

confidence: 99%

“…In addition, we propose random lead masking as an ECG-specific augmentation method to make our proposed model robust to an arbitrary set of leads. Experimental results on two downstream tasks, cardiac arrhythmia classification and patient identification, show that our proposed approach outperforms other state-of-the-art methods.Data and Code Availability This paper uses the Physionet 2021 dataset and PTB-XL dataset, which are publicly available on the PhysioNet repository (Reyna et al, 2021; Wagner et al, 2020). More details about datasets can be found at Section 5.1.Our implementation code can be accessed at this repository.…”

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confidence: 99%

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Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram

Oh¹,

Chung²,

Kwon³

et al. 2022

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In recent years, self-supervised learning methods have shown significant improvement for pre-training with unlabeled data and have proven helpful for electrocardiogram signals. However, most previous pre-training methods for electrocardiogram focused on capturing only global contextual representations. This inhibits the models from learning fruitful representation of electrocardiogram, which results in poor performance on downstream tasks. Additionally, they cannot fine-tune the model with an arbitrary set of electrocardiogram leads unless the models were pre-trained on the same set of leads. In this work, we propose an ECG pre-training method that learns both local and global contextual representations for better generalizability and performance on downstream tasks. In addition, we propose random lead masking as an ECG-specific augmentation method to make our proposed model robust to an arbitrary set of leads. Experimental results on two downstream tasks, cardiac arrhythmia classification and patient identification, show that our proposed approach outperforms other state-of-the-art methods.Data and Code Availability This paper uses the Physionet 2021 dataset and PTB-XL dataset, which are publicly available on the PhysioNet repository (Reyna et al., 2021; Wagner et al., 2020). More details about datasets can be found at Section 5.1.Our implementation code can be accessed at this repository. 1

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DeepPlayer: An open‐source SignalPlant plugin for deep learning inference

et al. 2022

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Background and Objective: Machine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre-trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal. Methods:The DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding. Results:We developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant.The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre-processing and post-processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service. Conclusion:The DeepPlayer plugin allows running deep learning models easily and interactively. Therefore, experts and non-AI experts alike can explore and apply deep learning models for (biomedical) signal processing. Its ease of use and interactivity might also contribute to a better understanding and acceptance of AI methods in biomedicine.

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Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Cited by 93 publications

References 8 publications

Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram

DeepPlayer: An open‐source SignalPlant plugin for deep learning inference

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