基于心動(dòng)周期估計(jì)的心音分割及異常心音篩查算法

趙湛; 張旭茹; 方震; 陳賢祥; 杜利東; 李田昌

doi:10.11999/JEIT170108

基于心動(dòng)周期估計(jì)的心音分割及異常心音篩查算法

doi: 10.11999/JEIT170108

2.
(中國(guó)科學(xué)院電子學(xué)研究所北京 100190) ②(中國(guó)科學(xué)院大學(xué) 北京 100049) ③(中國(guó)人民解放軍海軍總醫(yī)院北京 100048)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61302033)，北京市自然科學(xué)基金(Z160003)，國(guó)家重點(diǎn)研發(fā)計(jì)劃(2016YFC1304302, 2016YFC0206502, 2016YFC1303900)

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出版歷程
- 收稿日期: 2017-02-10
- 修回日期: 2017-04-20
- 刊出日期: 2017-11-19

Phonocardiogram Segmentation and Abnormal Phonocardiogram Screening Algorithm Based on Cardiac Cycle Estimation

2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Natural Science Foundation of China (61302033), The Beijing Municipal Natural Science Foundation (Z160003), The National Key Research and Development Project (2016YFC1304302, 2016YFC0206502, 2016YFC1303900)

摘要

摘要: 心臟疾病是全球發(fā)病率和死亡率最高的疾病，心音聽診可以獲取心臟的機(jī)械特性及結(jié)構(gòu)特征，與超聲心動(dòng)圖、核磁共振等無創(chuàng)診斷技術(shù)相比具有快速、低成本和操作簡(jiǎn)單的優(yōu)勢(shì)。心音信號(hào)成分復(fù)雜，容易受到各種噪聲和干擾的影響，聽診診斷結(jié)果容易受到醫(yī)生主觀性的影響，極大限制了心音聽診的應(yīng)用。該文提出一種基于心動(dòng)周期估計(jì)的心音分割及異常心音篩查算法，預(yù)先估計(jì)了心音的心動(dòng)周期，存在隨機(jī)干擾的情況下也可以正確識(shí)別信號(hào)中80%以上的心動(dòng)周期，提高了算法的穩(wěn)定性。同時(shí)提出了區(qū)分度良好的時(shí)域和頻域特征指標(biāo)，利用支持向量機(jī)建模，對(duì)異常心音的識(shí)別率可達(dá)92%。算法可輔助醫(yī)生診斷，或用于家用便攜式心音監(jiān)護(hù)設(shè)備。
- 心音分割 /
- 異常心音篩查 /
- 支持向量機(jī)
Abstract: Heart disease is of highest morbidity and mortality. The cardiac structure and mechanical characteristics can be reflected by auscultation. Compared with echocardiography and nuclear magnetic resonance, auscultation gets the advantages of fast, low cost and easy to use. The composition of phonocardiogram is complex, and the auscultation is easy to be affected by the subjectivity of the doctor, various noise and disturbances, which limits the application of auscultation. The algorithm of phonocardiogram segmentation and abnormal phonocardiogram screening is presented. For the reason that the heart cycle is estimated in advance, 80% cardiac cycle can be recognition correctly when random disturbances exist. The diagnostic indexes of time and frequency domain with high discrimination are also presented, and the abnormal heart sounds are recognized by Support Vector Machine (SVM) with the accuracy about 92%. The algorithm can be used for assisting doctors or portable phonocardiogram monitoring device.
- Phonocardiogram segment /
- Abnormal PCG screening /
- Support Vector Machine (SVM)

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