A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

• URL: http://arxiv.org/abs/2306.01210v1
• Date: Fri, 2 Jun 2023 00:08:38 GMT
• Title: A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
• Authors: Zhuo He, Hongjin Si, Xinwei Zhang, Qing-Hui Chen, Jiangang Zou, Weihua Zhou
• Abstract summary: The transfer learning model achieved an accuracy of 72% in distinguishing responders from non-responders. The model showed good sensitivity (0.78) and specificity (0.79) in identifying CRT responders.
• Score: 2.8136734847819773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting which patients will respond to CRT remains a challenge. This study explores the application of deep transfer learning techniques to train a predictive model for CRT response. Methods: In this study, the short-time Fourier transform (STFT) technique was employed to transform ECG signals into two-dimensional images. A transfer learning approach was then applied on the MIT-BIT ECG database to pre-train a convolutional neural network (CNN) model. The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response. Results: Seventy-one CRT patients were enrolled in this study. The transfer learning model achieved an accuracy of 72% in distinguishing responders from non-responders in the local dataset. Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in identifying CRT responders. The performance of our model outperformed clinic guidelines and traditional machine learning approaches. Conclusion: The utilization of ECG images as input and leveraging the power of transfer learning allows for improved accuracy in identifying CRT responders. This approach offers potential for enhancing patient selection and improving outcomes of CRT.

Related papers

• 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)
• Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey [6.1689808850463335]
  Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. Recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference. This paper aims to provide a comprehensive review of the methods of solving ECG inverse problem, the validation strategies, the clinical applications, and future perspectives.
  arXiv  Detail & Related papers  (2024-06-17T11:57:14Z)
• ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks [0.0]
  In clinical practice, the assignment of captured ECG recordings to incorrect patients can occur inadvertently. We propose a small and efficient neural-network based model for determining whether two ECGs originate from the same patient. Our model achieves state-of-the-art performance in gallery-probe patient identification on PTB-XL while utilizing 760x fewer parameters.
  arXiv  Detail & Related papers  (2023-06-09T18:53:25Z)
• A new method using deep learning to predict the response to cardiac resynchronization therapy [5.220522498181878]
  The purpose of this study is to combine clinical variables, features from electrocardiogram (ECG), and parameters from assessment of cardiac function with polarmaps from gated SPECT MPI. A DL model was constructed by combining a pre-trained VGG16 module and a multilayer perceptron. The DL model demonstrated average AUC (0.83), accuracy (0.73), sensitivity (0.76), and specificity (0.69) surpassing the ML models and guideline criteria.
  arXiv  Detail & Related papers  (2023-05-04T00:51:42Z)
• Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients [60.78505216352878]
  We propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.
  arXiv  Detail & Related papers  (2022-11-08T11:50:31Z)
• A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT [0.0]
  The aim of this study was to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958.
  arXiv  Detail & Related papers  (2022-06-15T18:35:22Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing [68.68882022019272]
  COVI-AgentSim is an agent-based compartmental simulator based on virology, disease progression, social contact networks, and mobility patterns. We use COVI-AgentSim to perform cost-adjusted analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features.
  arXiv  Detail & Related papers  (2020-10-30T00:47:01Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• DENS-ECG: A Deep Learning Approach for ECG Signal Delineation [15.648061765081264]
  This paper proposes a deep learning model for real-time segmentation of heartbeats. The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model.
  arXiv  Detail & Related papers  (2020-05-18T13:13:41Z)
• 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.