Robustness of convolutional neural networks to physiological ECG noise

• URL: http://arxiv.org/abs/2108.01995v1
• Date: Mon, 2 Aug 2021 08:16:32 GMT
• Title: Robustness of convolutional neural networks to physiological ECG noise
• Authors: J. Venton, P. M. Harris, A. Sundar, N. A. S. Smith, P. J. Aston
• Abstract summary: The electrocardiogram (ECG) is one of the most widespread diagnostic tools in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. There are open questions around the robustness of these methods to various factors, including physiological ECG noise. We generate clean and noisy versions of an ECG dataset before applying Symmetric Projection Attractor Reconstruction (SPAR) and scalogram image transformations. A pretrained convolutional neural network is trained using transfer learning to classify these image transforms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The electrocardiogram (ECG) is one of the most widespread diagnostic tools in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study we generate clean and noisy versions of an ECG dataset before applying Symmetric Projection Attractor Reconstruction (SPAR) and scalogram image transformations. A pretrained convolutional neural network is trained using transfer learning to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively, and the scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the performance decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification.

Related papers

• EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection [7.574088346030774]
  This paper focuses on detecting abnormal signals in electrocardi-ograms (ECGs) using only labels from normal signals as training data. In-spired by self-supervised vision transformers, which learn by dividing images into patches, and masked auto-encoders, we introduce the ECG Heartbeat Anomaly Detection model, EB-GAME.
  arXiv  Detail & Related papers  (2024-04-08T13:01:59Z)
• 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)
• ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal [19.885905393439014]
  We propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals. Based on the structural features, a temporal model is designed to learn the temporal information for various clinical tasks. The proposed method outperforms the baseline model and shows competitive performances compared with task-specific methods in three clinical applications.
  arXiv  Detail & Related papers  (2023-10-01T23:17:55Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• 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)
• SEVGGNet-LSTM: a fused deep learning model for ECG classification [38.747030782394646]
  The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features.
  arXiv  Detail & Related papers  (2022-10-31T07:36:48Z)
• Two-stream Network for ECG Signal Classification [3.222802562733787]
  This paper explores an effective algorithm for automatic classifications of multi-classes of heartbeat types based on ECG. A two-stream architecture is used in this paper and presents an enhanced version of ECG recognition based on this. Results on the MIT-BIH Arrhythmia Database demonstrate that the proposed algorithm performs an accuracy of 99.38%.
  arXiv  Detail & Related papers  (2022-10-05T08:14:51Z)
• Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis [48.64462717254158]
  We developed a self-supervised contrastive learning approach, EchoCLR, to catered to echocardiogram videos. When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets.
  arXiv  Detail & Related papers  (2022-07-23T19:17:26Z)
• Blind ECG Restoration by Operational Cycle-GANs [15.264145425539128]
  Continuous long-term monitoring of electrocardiography signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. We propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs)
  arXiv  Detail & Related papers  (2022-01-29T19:47:17Z)
• 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)
• 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.