MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution

• URL: http://arxiv.org/abs/2412.04861v1
• Date: Fri, 06 Dec 2024 08:53:31 GMT
• Title: MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution
• Authors: Jie Lin, I Chiu, Kuan-Chen Wang, Kai-Chun Liu, Hsin-Min Wang, Ping-Cheng Yeh, Yu Tsao,
• Abstract summary: 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.
• Score: 27.433941157026737
• License:
• Abstract: Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a lower sampling rate. In this study, 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, allowing for the effective reconstruction of high-resolution signals. We also assess the model's performance in real-world noisy conditions by utilizing ECG data from the PTB-XL database and noise data from the MIT-BIH Noise Stress Test Database. Experimental results show that MSECG outperforms two contemporary ECG SR models under both clean and noisy conditions while using fewer parameters, offering a more powerful and robust solution for long-term ECG monitoring applications.

Related papers

• Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study [43.28613210217385]
  We employ and compare three state-of-the-art generative models to generate PCG data. Our results demonstrate that the generated PCG data closely resembles the original datasets. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs.
  arXiv  Detail & Related papers  (2024-12-17T18:07:40Z)
• MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal [23.040957989796155]
  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.
  arXiv  Detail & Related papers  (2024-09-27T15:22:44Z)
• 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)
• 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)
• 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)
• 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)
• ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss [0.0]
  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.
  arXiv  Detail & Related papers  (2020-12-07T15:43:50Z)
• 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)
• AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising [46.0231398013639]
  We propose a novel cycleGAN architecture using a single switchable generator. The proposed method outperforms the previous cycleGAN approaches while using only about half the parameters.
  arXiv  Detail & Related papers  (2020-08-13T08:30:23Z)

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.