Can the Computer Tell Me What's Wrong With My Heart? Early Day Lessons From Digital Hospital and ECG Interpretation

Padayachee, C.; Sear, C.; Challa, P.; Jenkins, Carrie; Whitman, Mark

doi:10.1016/j.hlc.2018.06.581

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The arrhythmia labels were generated using a combination of three approaches. The majority of semi-structured diagnosis statements, i.e fragment of text that contains a single or multiple diagnoses [2], embedded in the dataset were assigned to a Unified Medical Language System Concept Unique Identifiers (CUI) [3] and the corresponding Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) [2]. All the segments of diagnosis statements that did not have a corresponding CUI were then split into two groups sorted by frequency.…”

Section: Muse Datasetmentioning

confidence: 99%

Robustness of Residual Network in Predicting PR Interval Trained using Noisy Labels

Cao¹,

Ghanbari²,

Farzaneh³

et al. 2022

Computing in Cardiology Conference (CinC)

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The PR interval represents the time required from the electrical impulse to advance from the atrium to AV node and His-Purkinje system until the ventricular myocardium begins to depolarize. PR interval prolongation has been associated with significant increases in atrial fibrillation, heart failure and mortality.Over the past years, multiple deep learning models have been proposed to interpret electrocardiogram (ECG) signals. Despite initial success, these models are often trained and validated using datasets that contain partially incorrect labels. These "noisy" labels exist because of the way the annotated data was collected and pose challenges for model training and validation.As a result, a residual neural network (ResNet), trained on noisy data, was proposed to estimate PR intervals. In addition, an electrophysiologist performed a blinded manual adjudication on a stratified sample to validate the accuracy of both the model and the noisy labels.The conclusion is that a ResNet trained on noisy data can correctly estimate PR intervals and outperforms the noisy labels it was trained on.

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Section: Muse Datasetmentioning

confidence: 99%

Robustness of Residual Network in Predicting PR Interval Trained using Noisy Labels

Cao¹,

Ghanbari²,

Farzaneh³

et al. 2022

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

“…The electrocardiogram (ECG) is widely used for the diagnosis and monitoring of various cardiovascular diseases and cardiac abnormalities [1]. However, manual interpretation of ECG recordings is laborious and requires inspection by trained clinical personnel [2]. Machine learning models may enable automatic classification of cardiac abnormalities and reduce interpretation time and healthcare costs.…”

Section: Introductionmentioning

confidence: 99%

Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning

Ansari¹,

Gillies²,

Cummings³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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This article concerns the PhysioNet/Computing in Cardiology Challenge 2020 which focused on building computational methods to identify cardiac abnormalities from 12-lead ECGs. Our team, MCIRCC, utilized a large secondary dataset of 12-lead ECGs obtained from the Section of Electrophysiology at the University of Michigan, called the MUSE dataset, to pre-train multiple residual neural networks that were later retrained on the challenge dataset. To do so, the diagnosis statements that existed in our dataset were utilized to assign the same labels to our ECGs as the challenge data. After parameter optimization, we selected a subset of top performing models and created an ensemble model that achieved a challenge validation score of 0.616, and full test score of 0.141, placing us 27 th out of 41 teams in the official ranking.

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Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals

Prusty,

Pandey,

Lekha

et al. 2024

Sci Rep

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Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT–CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT–CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases.

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Can the Computer Tell Me What's Wrong With My Heart? Early Day Lessons From Digital Hospital and ECG Interpretation

Cited by 3 publications

References 2 publications

Robustness of Residual Network in Predicting PR Interval Trained using Noisy Labels

Robustness of Residual Network in Predicting PR Interval Trained using Noisy Labels

Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning

Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals

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