A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection

• URL: http://arxiv.org/abs/2102.01319v1
• Date: Tue, 2 Feb 2021 05:19:19 GMT
• Title: A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
• Authors: Atiyeh Fotoohinasab, Toby Hocking, and Fatemeh Afghah
• Abstract summary: This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection.
• Score: 5.763710641111973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to other state-of-the-art approaches. The proposed method achieves the overall sensitivity of Sen = 99.64%, positive predictivity of PPR = 99.71%, and detection error rate of DER = 0.19.

Related papers

• Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack [2.1937382384136637]
  We propose treating noisy edges as adversarial attack and use a spectral adversarial robustness evaluation method to diminish the impact of noisy edges on the performance of graph algorithms. Our method identifies those points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms.
  arXiv  Detail & Related papers  (2024-01-28T10:03:37Z)
• Towards Self-Interpretable Graph-Level Anomaly Detection [73.1152604947837]
  Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. We propose a Self-Interpretable Graph aNomaly dETection model ( SIGNET) that detects anomalous graphs as well as generates informative explanations simultaneously.
  arXiv  Detail & Related papers  (2023-10-25T10:10:07Z)
• Open-World Lifelong Graph Learning [7.535219325248997]
  We study the problem of lifelong graph learning in an open-world scenario. We utilize Out-of-Distribution (OOD) detection methods to recognize new classes. We suggest performing new class detection by combining OOD detection methods with information aggregated from the graph neighborhood.
  arXiv  Detail & Related papers  (2023-10-19T08:18:10Z)
• Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning [22.307526272085024]
  In this work, we convert graph pruning to a problem of node relabeling and then relax it to a differentiable problem. Based on this idea, we further design a novel neural network to approximate a type of subgraph distance: the subgraph edit distance (SED) In the design of the model, we propose an attention mechanism to leverage the information about the query graph and guide the pruning of the target graph.
  arXiv  Detail & Related papers  (2022-10-19T15:16:28Z)
• Benchmarking Node Outlier Detection on Graphs [90.29966986023403]
  Graph outlier detection is an emerging but crucial machine learning task with numerous applications. We present the first comprehensive unsupervised node outlier detection benchmark for graphs called UNOD.
  arXiv  Detail & Related papers  (2022-06-21T01:46:38Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• 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)
• A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection [6.47783315109491]
  The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal.
  arXiv  Detail & Related papers  (2021-02-06T08:59:18Z)
• Graph Information Bottleneck for Subgraph Recognition [103.37499715761784]
  We propose a framework of Graph Information Bottleneck (GIB) for the subgraph recognition problem in deep graph learning. Under this framework, one can recognize the maximally informative yet compressive subgraph, named IB-subgraph. We evaluate the properties of the IB-subgraph in three application scenarios: improvement of graph classification, graph interpretation and graph denoising.
  arXiv  Detail & Related papers  (2020-10-12T09:32:20Z)
• A Graph-constrained Changepoint Detection Approach for ECG Segmentation [5.209323879611983]
  We introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of R-peak positions without employing any preprocessing step. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55.
  arXiv  Detail & Related papers  (2020-04-24T23:41:41Z)
• Data-Driven Factor Graphs for Deep Symbol Detection [107.63351413549992]
  We propose to implement factor graph methods in a data-driven manner. In particular, we propose to use machine learning (ML) tools to learn the factor graph. We demonstrate that the proposed system, referred to as BCJRNet, learns to implement the BCJR algorithm from a small training set.
  arXiv  Detail & Related papers  (2020-01-31T09:23:52Z)

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.