PVC Recognition Algorithm Based on Ensemble Learning

ZHOU Fei-yan; JIN Lin-peng; DONG Jun

doi:10.3969/j.issn.0372-2112.2017.02.032

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PVC Recognition Algorithm Based on Ensemble Learning

更新时间：2025-07-16

- PVC Recognition Algorithm Based on Ensemble Learning
- Acta Electronica Sinica Vol. 45, Issue 2, Pages: 501-507(2017)
- 作者机构：
  
  1. 中国科学院苏州纳米技术与纳米仿生研究所,江苏,苏州,215123
  2. 中国科学院大学,北京,100049
  3. 中国科学院苏州纳米技术与纳米仿生研究所,江苏,苏州,215123
  4. 中国科学院大学,北京,100049
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2017.02.032
  CLC： TP391
- Published Online：25 February 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
ZHOU Fei-yan, JIN Lin-peng, DONG Jun. PVC Recognition Algorithm Based on Ensemble Learning[J]. Acta Electronica Sinica, 2017, 45(2): 501-507.
DOI：

ZHOU Fei-yan, JIN Lin-peng, DONG Jun. PVC Recognition Algorithm Based on Ensemble Learning[J]. Acta Electronica Sinica, 2017, 45(2): 501-507. DOI： 10.3969/j.issn.0372-2112.2017.02.032.

摘要

本文提出了一种集成学习方法以提升室性早搏的识别性能.MIT-BIH两个通道的数据分别经过卷积神经网络进行室性早搏心拍分类，然后按照融合规则对分类结果进行融合决策，其准确率、灵敏度和特异性分别为99.91%、98.76%、99.97%，优于已有算法的室性早搏心拍分类结果.此外，面向临床应用，本文还利用卷积神经网络和诊断规则相结合的方法实现了病人间室性早搏识别实验，在有14万多条记录的数据集上，取得的准确率、灵敏度及特异性分别为97.87%、87.94%、98.02%，验证了该算法的有效性.

Abstract

In order to improve the recognition performance of premature ventricular contraction (PVC)

this paper reports an algorithm based on ensemble learning.First

the tow-lead ECG signals from the MIT-BIH Arrhythmia database are classified into PVC and non PVC beats using lead convolutional neural network (LCNN) classifier.Then the results are fused with some rules.The accuracy

sensitivity and specificity of the proposed algorithm are 99.91%

98.76% and 99.97%

respectively

which are better than that of other existing algorithms for PVC beats classification.In addition

this paper realizes an inter-patient PVC recognition experiment by combining LCNN and diagnostic rules for clinical application.The effectiveness of the proposed algorithm has been confirmed by the accuracy (97.87%)

sensitivity (87.94%) and specificity (98.02%) with the data set over 140000 ECG records.

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