The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering

• URL: http://arxiv.org/abs/2407.15555v1
• Date: Mon, 22 Jul 2024 11:34:47 GMT
• Title: The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering
• Authors: Lucas Plagwitz, Lucas Bickmann, Michael Fujarski, Alexander Brenner, Warnes Gobalakrishnan, Lars Eckardt, Antonius Büscher, Julian Varghese,
• Abstract summary: We aim to reintroduce shallow learning techniques, including support vector machines and principal components analysis, into ECG signal processing. To this end, we developed and evaluated a transformation that effectively restructures ECG signals into a fully structured format. Our approach demonstrates a significant advantage for shallow machine learning methods over CNNs, especially when dealing with limited training data.
• Score: 34.88496713576635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiogram (ECG) recordings have long been vital in diagnosing different cardiac conditions. Recently, research in the field of automatic ECG processing using machine learning methods has gained importance, mainly by utilizing deep learning methods on raw ECG signals. A major advantage of models like convolutional neural networks (CNNs) is their ability to effectively process biomedical imaging or signal data. However, this strength is tempered by challenges related to their lack of explainability, the need for a large amount of training data, and the complexities involved in adapting them for unsupervised clustering tasks. In addressing these tasks, we aim to reintroduce shallow learning techniques, including support vector machines and principal components analysis, into ECG signal processing by leveraging their semi-structured, cyclic form. To this end, we developed and evaluated a transformation that effectively restructures ECG signals into a fully structured format, facilitating their subsequent analysis using shallow learning algorithms. In this study, we present this adaptive transformative approach that aligns R-peaks across all signals in a dataset and resamples the segments between R-peaks, both with and without heart rate dependencies. We illustrate the substantial benefit of this transformation for traditional analysis techniques in the areas of classification, clustering, and explainability, outperforming commercial software for median beat transformation and CNN approaches. Our approach demonstrates a significant advantage for shallow machine learning methods over CNNs, especially when dealing with limited training data. Additionally, we release a fully tested and publicly accessible code framework, providing a robust alignment pipeline to support future research, available at https://github.com/ imi-ms/rlign.

Related papers

• CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation [60.08541107831459]
  This paper proposes a CNN-Transformer rectified collaborative learning framework to learn stronger CNN-based and Transformer-based models for medical image segmentation. Specifically, we propose a rectified logit-wise collaborative learning (RLCL) strategy which introduces the ground truth to adaptively select and rectify the wrong regions in student soft labels. We also propose a class-aware feature-wise collaborative learning (CFCL) strategy to achieve effective knowledge transfer between CNN-based and Transformer-based models in the feature space.
  arXiv  Detail & Related papers  (2024-08-25T01:27:35Z)
• ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning [20.465733855762835]
  Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification. Classic rule-based algorithms are now completely outperformed by deep learning based methods. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout.
  arXiv  Detail & Related papers  (2023-04-10T15:19:00Z)
• GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction [20.8603653664403]
  We propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions.
  arXiv  Detail & Related papers  (2022-08-02T03:14:13Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• Effective classification of ecg signals using enhanced convolutional neural network in iot [0.0]
  This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL) Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions. The results show that the proposed strategy outperforms others in terms of classification accuracy.
  arXiv  Detail & Related papers  (2022-02-08T13:37:23Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Quality-aware semi-supervised learning for CMR segmentation [2.9928692313705505]
  One of the challenges in developing deep learning algorithms for medical image segmentation is the scarcity of training data. We propose a novel scheme that uses QC of the downstream task to identify high quality outputs of CMR segmentation networks. In essence, this provides quality-aware augmentation of training data in a variant of SSL for segmentation networks.
  arXiv  Detail & Related papers  (2020-09-01T17:18:22Z)
• DENS-ECG: A Deep Learning Approach for ECG Signal Delineation [15.648061765081264]
  This paper proposes a deep learning model for real-time segmentation of heartbeats. The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model.
  arXiv  Detail & Related papers  (2020-05-18T13:13:41Z)
• 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)

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.