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基于经验模态分解的第一心音和第二心音检测与识别

Detection and identification of first and second heart sounds using empirical mode decomposition.

作者信息

Bajelani Kourosh, Navidbakhsh Mahdi, Behnam Hamid, Doyle John D, Hassani Kamran

机构信息

Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.

出版信息

Proc Inst Mech Eng H. 2013 Sep;227(9):976-87. doi: 10.1177/0954411913493734. Epub 2013 Jun 13.

Abstract

We present a novel, low complexity method for the detection of the first and second of heart sounds (S1 and S2, respectively) and the periods of systole and diastole without using an electrocardiogram reference. The algorithm uses a technique called empirical mode decomposition to produce intensity envelopes of the main heart sounds in the time domain. The performance of the algorithm was evaluated using 14,000 cardiac periods from 100 normal and abnormal digital phonocardiographic recordings. The sensitivity of the detection method was 88.3% for both S1 and S2, and the precision (positive predictive value) was 95.8% for both S1 and S2.

摘要

我们提出了一种新颖的、低复杂度的方法,用于在不使用心电图参考的情况下检测第一心音和第二心音(分别为S1和S2)以及收缩期和舒张期。该算法使用一种称为经验模态分解的技术在时域中生成主要心音的强度包络。使用来自100份正常和异常数字心音图记录的14000个心动周期对该算法的性能进行了评估。该检测方法对S1和S2的灵敏度均为88.3%,对S1和S2的精度(阳性预测值)均为95.8%。

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