基于模态熵的室性早搏和房性早搏检测

[Detection of premature ventricular contraction and atrial premature contraction based on mode entropy].

作者信息

Wu Jun, Wang Jun

机构信息

Image Processing and Image Communications Key Lab., College of Telecommunications & Information Engineering, Nanjing Univ. of Posts & Telecomm., Nanjing 210003, China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Jun;27(3):516-8.

PMID:20649009

Abstract

Electrocardiogram (ECG) beat classification is essential for timely diagnosis of serious heart disorders. Premature ventricular contraction (PVC) and atrial premature contraction (APC) are serious threats against human lives. In order to cope with difficulties in diagnosis, a mode entropy based technique is herein proposed to classify normal sinus rhythm (NSR), APC and PVC, As is well known that the accuracy of most methods for detecting NSR, APC, PVC, etc is now between 90% to 98%, but most of them need large data sets. After calculation of the mode entropy value of NSR, PVC and APC, the results were analyzed and compared. The detection method based on the mode entropy technique is proven to be rational and accurate, and it does not need large data sets.

摘要

心电图（ECG）搏动分类对于严重心脏疾病的及时诊断至关重要。室性早搏（PVC）和房性早搏（APC）对人类生命构成严重威胁。为了应对诊断中的困难，本文提出一种基于模态熵的技术来对正常窦性心律（NSR）、APC和PVC进行分类。众所周知，目前大多数检测NSR、APC、PVC等的方法的准确率在90%至98%之间，但其中大多数需要大量数据集。在计算出NSR、PVC和APC的模态熵值后，对结果进行了分析和比较。基于模态熵技术的检测方法被证明是合理且准确的，并且不需要大量数据集。