Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels

• URL: http://arxiv.org/abs/2208.08853v1
• Date: Mon, 8 Aug 2022 17:16:16 GMT
• Title: Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
• Authors: Radhika Dua, Jiyoung Lee, Joon-myoung Kwon, Edward Choi
• Abstract summary: We present a two-stage deep learning-based framework to automatically detect noisy ECG samples. We observe that the framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.
• Score: 12.176026483486252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and analysis on two different datasets, we observe that the deep learning-based framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.

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)
• Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG [1.174402845822043]
  We benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms. We observe that the method can robustly identify atrial fibrillation, even in cases signals are labelled by human experts as being noisy on multiple leads.
  arXiv  Detail & Related papers  (2023-03-24T11:04:16Z)
• 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)
• Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography [51.45515911920534]
  A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector. A specific two branch convolutional neural network with larger kernels is designed as the feature extractor. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
  arXiv  Detail & Related papers  (2022-07-04T13:15:08Z)
• 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)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• 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)
• Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM [3.1372269816123994]
  It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis.
  arXiv  Detail & Related papers  (2020-11-12T04:20:56Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• 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.