基于频率有序小波包的生物信号特征检测

S. Z. Mahmoodabadi, J. Alirezaie, P. Babyn
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引用次数: 10

摘要

本文介绍了小波包在心电图和磁共振波谱特征检测中的应用。开发了一个全自动系统来检测“R”峰,这是心跳指示器,因此用于定位其他ECG特征。它们包括“P”,“Q”,“S”和“T”波以及“ST”段移位。同时检测了磁振子信号的波峰和波峰下面积。选取Daubechies小波作为基处理滤波器。频率有序小波包(FOWPT)用于生成信号的时频图,用于进一步处理。在MIT-BIH数据库上对算法进行了验证。所提出的温度检测器的灵敏度为99.18%plusmn2.75,正预测性为98.00%plusmn4.45。P波检测仪的灵敏度为51.69%,阳性预测值为53.64%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bio-signal Characteristics Detection Utilizing Frequency Ordered Wavelet Packets
An application of wavelet packet is presented for electrocardiogram (ECG) and magnetic resonance spectroscopy (MRS) characteristics detection in this study. A fully automated system is developed to detect the "R" peaks which are beat designators and are used consequently to locate other ECG characteristics. They include "P", "Q", "S" and "T" waves along with "ST" segment shift. The peaks and the area under the peaks of MRS signals are also detected. The Daubechies wavelets are selected as base processing filters. Frequency ordered wavelet packets (FOWPT) is utilized to generate a time-frequency plot of the signal used for further processing. The algorithm is validated on MIT-BIH database. The proposed beat detector achieved sensitivity of 99.18%plusmn2.75 and a positive predictivity of 98.00%plusmn4.45. The "P" wave detector achieved sensitivity of 51.69% and a positive predictivity of 53.64%.
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