开发一种新的心电分类小波

B. Paul, K. T. Shanavaz, P. Mythili
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引用次数: 3

摘要

本文尝试使用由多个高斯函数构成的小波,从给定的心电图(ECG)中准确地确定室性早搏(PVC)周期的数量。长时间连续监测患者的心电图很难评估。因此,提出的分类方法将有助于医生确定患者PVC的严重程度。除了特别开发的小波变换外,还使用了主成分分析(PCA)和简单分类器。所提出的小波是用多个高斯函数设计的,当求和时看起来与正常心电图相似。使用的高斯数取决于正常心电图中出现的峰数。所开发的小波满足传统连续小波的所有性质。采用遗传算法对新小波进行优化。来自麻省理工学院贝斯以色列医院(MIT-BIH)数据库的心电图记录已被用于验证。在用于评估的8694个ECG周期中,该分类算法的响应准确率为97.77%。为了比较新小波的性能,还使用morlet、meyer、bior3.9、db5、db3、sym3和haar等标准小波进行了分类。新小波优于其他小波。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards the development of a new wavelet for ECG classification
In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest.
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