MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

• URL: http://arxiv.org/abs/2409.18828v1
• Date: Fri, 27 Sep 2024 15:22:44 GMT
• Title: MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
• Authors: Kuo-Hsuan Hung, Kuan-Chen Wang, Kai-Chun Liu, Wei-Lun Chen, Xugang Lu, Yu Tsao, Chii-Wann Lin,
• Abstract summary: We propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E) MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. It requires less inference time than state-of-the-art diffusion-based ECG denoisers.
• Score: 23.040957989796155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing methods yield suboptimal performance under very noisy conditions or require several steps during inference, leading to latency during online processing. In this paper, we propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E), which leverages the Mamba architecture known for its fast inference and outstanding nonlinear mapping capabilities. Experimental results indicate that MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. Additionally, MECG-E requires less inference time than state-of-the-art diffusion-based ECG denoisers, demonstrating the model's functionality and efficiency.

Related papers

• ECGMamba: Towards Efficient ECG Classification with BiSSM [3.0120310355085467]
  We propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification.
  arXiv  Detail & Related papers  (2024-06-14T14:55:53Z)
• MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation. We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)
• 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)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• 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)
• 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)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection [0.0]
  This paper introduces a light deep learning approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythm. A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested.
  arXiv  Detail & Related papers  (2022-08-29T05:01:04Z)
• Representing and Denoising Wearable ECG Recordings [12.378631176671773]
  We develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor. We design a beat-to-beat representation that is conducive for analyzing variation, and devise a factor analysis-based method to denoise the ECG.
  arXiv  Detail & Related papers  (2020-11-30T21:33:11Z)
• 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.