小波变换在房颤预测中的应用

Hassan Serhal, Nassib Abdallah, J. Marion, P. Chauvet, Mohamad Oueidat, A. Humeau-Heurtier
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引用次数: 0

摘要

房颤(AF)的预测是医学上的一个主要问题。这是因为房颤通常是无症状的。在这项工作中,我们提出了基于小波分解的方法来寻找可以预测这种疾病的信号特征。该模型由预处理、特征提取、特征选择和分类预测四部分组成。本研究显示,在房颤发作前5分钟的预测准确率为94%,在房颤发作前110分钟的预测准确率为85.5%。我们的代码将提供给研究人员的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wavelet transformation approaches for prediction of atrial fibrillation
Prediction of atrial fibrillation (AF) is a major issue in medicine. This is due to the fact that AF is often asymptomatic. In this work, we present approaches based on wavelet decomposition to find features in the signal that can predict this disease. Our model consists of four parts: pre-processing, feature extraction, feature selection, and classification for prediction. The presented work shows a good predictive performance (94% accuracy) before 5 min of AF onset and a prediction accuracy of 85.5%, 110 min before AF onset. Our code will be available for researchers upon request.
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