基于心動周期估計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

基于心動周期估計的心音分割及異常心音篩查算法

doi: 10.11999/JEIT170108 cstr: 32379.14.JEIT170108

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

基金項目:

國家自然科學(xué)基金(61302033)，北京市自然科學(xué)基金(Z160003)，國家重點研發(fā)計劃(2016YFC1304302, 2016YFC0206502, 2016YFC1303900)

計量
- 文章訪問數(shù): 1594
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- 被引次數(shù): 0
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械特性及結(jié)構(gòu)特征，與超聲心動圖、核磁共振等無創(chuàng)診斷技術(shù)相比具有快速、低成本和操作簡單的優(yōu)勢。心音信號成分復(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周期估計的心音分割及異常心音篩查算法，預(yù)先估計了心音的心動周期，存在隨機干擾的情況下也可以正確識別信號中80%以上的心動周期，提高了算法的穩(wěn)定性。同時提出了區(qū)分度良好的時域和頻域特征指標(biāo)，利用支持向量機建模，對異常心音的識別率可達(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
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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