ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

• URL: http://arxiv.org/abs/2012.03803v1
• Date: Mon, 7 Dec 2020 15:43:50 GMT
• Title: ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss
• Authors: Tsai-Min Chen (1 and 2), Yuan-Hong Tsai (3 and 4), Huan-Hsin Tseng (2), Jhih-Yu Chen (5), Chih-Han Huang (6), Guo-Yuan Li (3 and 4), Chun-Yen Shen (1 and 7) and Yu Tsao (1 and 2) ((1) Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan, (2) Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan, (3) Taiwan AI Academy, Science and Technology Ecosystem Development Foundation, Taipei, Taiwan, (4) Artificial Intelligence Foundation, Taipei, Taiwan, (5) Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, (6) Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, (7) Department of Mathematics, National Taiwan University, Taipei, Taiwan)
• Abstract summary: We propose a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals. Experimental results show that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios.

Related papers

• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals. Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality. The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution [27.433941157026737]
  We propose MSECG, a compact neural network model designed for ECG SR. MSECG combines the strength of the recurrent Mamba model with convolutional layers to capture both local and global dependencies in ECG waveforms. Experimental results show that MSECG outperforms two contemporary ECG SR models under both clean and noisy conditions.
  arXiv  Detail & Related papers  (2024-12-06T08:53:31Z)
• ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring [43.23305904110984]
  ConvexECG is an explainable and resource-efficient method for reconstructing six-lead electrocardiograms from single-lead data. We demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead.
  arXiv  Detail & Related papers  (2024-09-19T06:14:30Z)
• ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion [1.727597257312416]
  We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead. We propose ECGrecover, a neural network model trained on a novel composite objective function to address the reconstruction problem.
  arXiv  Detail & Related papers  (2024-05-31T15:17:12Z)
• Bayesian ECG reconstruction using denoising diffusion generative models [11.603515105957461]
  We propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data. Our results show that this innovative generative model can successfully generate realistic ECG signals.
  arXiv  Detail & Related papers  (2023-12-18T15:56:21Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• 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)
• Leveraging Statistical Shape Priors in GAN-based ECG Synthesis [3.3482093430607267]
  We propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines.
  arXiv  Detail & Related papers  (2022-10-22T18:06:11Z)
• 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)
• 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.