ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring

• URL: http://arxiv.org/abs/2409.12493v1
• Date: Thu, 19 Sep 2024 06:14:30 GMT
• Title: ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring
• Authors: Rayan Ansari, John Cao, Sabyasachi Bandyopadhyay, Sanjiv M. Narayan, Albert J. Rogers, Mert Pilanci,
• Abstract summary: 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.
• Score: 43.23305904110984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for efficient training and deployment in resource constrained environments, while also having deterministic and explainable behavior. Using data from 25 patients, we demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead, highlighting its potential for real-time, low-resource monitoring applications.

Related papers

• LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring [8.13292883415769]
  Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status. Existing single-channel EEG adjacency graph construction process lacks interpretability. We propose an edge-end lightweight dual graph convolutional network (LDGCN)
  arXiv  Detail & Related papers  (2024-07-08T08:55:25Z)
• Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network [0.7673339435080443]
  Coronary Artery Disease (CAD) diagnostic to be a major global cause of death, necessitating innovative solutions. We propose the potential of one-dimensional convolutional neural networks (1D-CNN) to enhance detection accuracy and reduce network complexity. This study goes beyond traditional diagnostic methodologies, leveraging the remarkable ability of 1D-CNN to interpret complex patterns within Electrocardiogram (ECG) signals without depending on feature extraction techniques.
  arXiv  Detail & Related papers  (2024-05-15T13:51:02Z)
• Optimizing Neural Network Scale for ECG Classification [1.8953148404648703]
  We study scaling convolutional neural networks (CNNs) specifically targeting Residual neural networks (ResNet) for analyzing electrocardiograms (ECGs) We explored and demonstrated an efficient approach to scale ResNet by examining the effects of crucial parameters, including layer depth, the number of channels, and the convolution kernel size. Our findings provide insight into obtaining more efficient and accurate models with fewer computing resources or less time.
  arXiv  Detail & Related papers  (2023-08-24T01:26:31Z)
• Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks [11.964843902569925]
  Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. Wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity.
  arXiv  Detail & Related papers  (2022-10-27T14:24:48Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Binary Graph Neural Networks [69.51765073772226]
  Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-12-31T18:48:58Z)
• End-to-End Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms [0.057350354637930076]
  SeismoNet aims to provide an end-to-end solution to observe heart activity from Seismocardiogram (SCG) signals. These SCG signals are motion-based and can be acquired in an easy, user-friendly fashion. The use of deep learning enables the detection of R-peaks directly from SCG signals in spite of their noise-ridden morphology.
  arXiv  Detail & Related papers  (2020-10-12T13:02:07Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• 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)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

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.