ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks

• URL: http://arxiv.org/abs/2306.06196v2
• Date: Tue, 19 Sep 2023 14:51:04 GMT
• Title: ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks
• Authors: Michal Sej\'ak, Jakub Sido, David \v{Z}ahour
• Abstract summary: In clinical practice, the assignment of captured ECG recordings to incorrect patients can occur inadvertently. We propose a small and efficient neural-network based model for determining whether two ECGs originate from the same patient. Our model achieves state-of-the-art performance in gallery-probe patient identification on PTB-XL while utilizing 760x fewer parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Electrocardiograms (ECGs) are commonly used by cardiologists to detect heart-related pathological conditions. Reliable collections of ECGs are crucial for precise diagnosis. However, in clinical practice, the assignment of captured ECG recordings to incorrect patients can occur inadvertently. In collaboration with a clinical and research facility which recognized this challenge and reached out to us, we present a study that addresses this issue. In this work, we propose a small and efficient neural-network based model for determining whether two ECGs originate from the same patient. Our model demonstrates great generalization capabilities and achieves state-of-the-art performance in gallery-probe patient identification on PTB-XL while utilizing 760x fewer parameters. Furthermore, we present a technique leveraging our model for detection of recording-assignment mistakes, showcasing its applicability in a realistic scenario. Finally, we evaluate our model on a newly collected ECG dataset specifically curated for this study, and make it public for the research community.

Related papers

• ECG Arrhythmia Detection Using Disease-specific Attention-based Deep Learning Model [0.0]
  We propose a disease-specific attention-based deep learning model (DANet) for arrhythmia detection from short ECG recordings. The novel idea is to introduce a soft-coding or hard-coding waveform enhanced module into existing deep neural networks. For the soft-coding DANet, we also develop a learning framework combining self-supervised pre-training with two-stage supervised training.
  arXiv  Detail & Related papers  (2024-07-25T13:27:10Z)
• Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care [0.9503773054285559]
  We investigate the capability of a single model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a sole ECG collected in the emergency department. We find that 253, 81 cardiac, and 172 non-cardiac, ICD codes can be reliably predicted in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner.
  arXiv  Detail & Related papers  (2023-12-18T09:29:42Z)
• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines [46.09869227806991]
  evaluating canine electrocardiograms (ECG) require skilled veterinarians. Current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal.
  arXiv  Detail & Related papers  (2023-05-17T09:06:39Z)
• Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals [2.5008947886814186]
  We propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diagnosis of ECG signals. The first-level model is composed of a Memory-Augmented Deep auto-Encoder with GAN, which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmias identification.
  arXiv  Detail & Related papers  (2022-10-19T12:29:05Z)
• Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network [18.859487271034336]
  We introduce some rules into convolutional neural networks, which help present clinical knowledge to deep learning based ECG analysis. Our new approach considerably outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-16T04:42:57Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.