Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks

Ke Wei Chen, Laura Bear, Che Wei Lin

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs’ ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.

原文English
文章編號2331
期刊Sensors
22
發行號6
DOIs
出版狀態Published - 2022 3月 1

All Science Journal Classification (ASJC) codes

  • 分析化學
  • 資訊系統
  • 原子與分子物理與光學
  • 生物化學
  • 儀器
  • 電氣與電子工程

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