Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length

• URL: http://arxiv.org/abs/2008.03609v4
• Date: Mon, 30 Nov 2020 20:09:42 GMT
• Title: Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length
• Authors: Linhai Ma, Liang Liang
• Abstract summary: Deep neural networks (DNNs) have been developed for automatic interpretation of ECG signals. In this work, we designed a CNN for classification of 12-lead ECG signals with variable length. The evaluation results show that our customized CNN reached satisfying F1 score and average accuracy, comparable to the top-6 entries in the CPSC 2018 ECG classification challenge.
• Score: 2.2977141788872366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many research labs for automatic interpretation of ECG signals to identify potential abnormalities in patient hearts. Studies have shown that given a sufficiently large amount of data, the classification accuracy of DNNs could reach human-expert cardiologist level. However, despite of the excellent performance in classification accuracy, it has been shown that DNNs are highly vulnerable to adversarial noises which are subtle changes in input of a DNN and lead to a wrong class-label prediction with a high confidence. Thus, it is challenging and essential to improve robustness of DNNs against adversarial noises for ECG signal classification, a life-critical application. In this work, we designed a CNN for classification of 12-lead ECG signals with variable length, and we applied three defense methods to improve robustness of this CNN for this classification task. The ECG data in this study is very challenging because the sample size is limited, and the length of each ECG recording varies in a large range. The evaluation results show that our customized CNN reached satisfying F1 score and average accuracy, comparable to the top-6 entries in the CPSC2018 ECG classification challenge, and the defense methods enhanced robustness of our CNN against adversarial noises and white noises, with a minimal reduction in accuracy on clean data.

Related papers

• Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals [4.659427498118277]
  This paper proposes an automated AI based system for ECG-based arrhythmia classification. Deep learning based solution has been proposed for ECG-based arrhythmia classification.
  arXiv  Detail & Related papers  (2023-12-01T03:16:32Z)
• 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)
• Global ECG Classification by Self-Operational Neural Networks with Feature Injection [25.15075119957447]
  We propose a novel approach for inter-patient ECG classification using a compact 1D Self-Organized Operational Neural Networks (Self-ONNs) We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved.
  arXiv  Detail & Related papers  (2022-04-07T22:49:18Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification [1.8579693774597703]
  Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. Deep neural networks (DNNs) interpretation of ECG signals can be fully automated for the identification of potential abnormalities in a patient's heart in a fraction of a second. DNNs are highly vulnerable to adversarial noises that are subtle changes in the input of a DNN and may lead to a wrong class-label prediction. We propose a regularization method to improve robustness from the perspective of noise-to-signal ratio (NSR) for the application of ECG signal classification.
  arXiv  Detail & Related papers  (2021-10-19T06:22:02Z)
• ECG-ATK-GAN: Robustness against Adversarial Attacks on ECG using Conditional Generative Adversarial Networks [12.833916980261368]
  Deep neural networks (DNN) are vulnerable to adversarial attacks, which can misclassify ECG signals. We introduce a novel Conditional Generative Adversarial Network (GAN), robust against adversarial attacked ECG signals.
  arXiv  Detail & Related papers  (2021-10-17T08:44:17Z)
• 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)
• Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
  We present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
  arXiv  Detail & Related papers  (2020-11-17T16:15:49Z)
• Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective [2.2977141788872366]
  Deep neural networks (DNNs) have been developed for automatic interpretation of ECG signals. DNNs are highly vulnerable to adversarial attacks. In this work, we propose to improve DNN robustness from the perspective of noise-to-signal ratio (NSR)
  arXiv  Detail & Related papers  (2020-05-18T23:37:33Z)
• 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)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.