Fully-Automated Diagnosis of Aortic Stenosis Using Phonocardiogram-Based Features

被引：0

作者：

Saraf, Kanav ^{[1
]}

Baek, Christopher I. ^{[2
]}

Wasko, Michael H. ^{[2
]}

Zhang, Xu ^{[1
]}

Zheng, Yi ^{[2
]}

Borgstrom, Per H. ^{[2
]}

Mahajan, Aman ^{[3
]}

Kaiser, William J. ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Los Angeles, CA 90095 USA

[2] Sensydia Corp, Los Angeles, CA 90024 USA

[3] Univ Pittsburgh, Sch Med, Pittsburgh, PA 15261 USA

来源：

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2019年

关键词：

HEART; IDENTIFICATION; MURMUR;

D O I：

10.1109/embc.2019.8857506

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

The irreversible damage and eventual heart failure caused by untreated aortic stenosis (AS) can be prevented by early detection and timely intervention. Prior work in the field of phonocardiogram (PCG) signal analysis has provided proof of concept for using heart-sound data in AS diagnosis. However, such systems either require operation by trained technicians, fail to address a diverse subject set, or involve unwieldy configuration procedures that challenge real world application. This paper presents an end-to-end, fully-automated system that uses noise-subtraction, heartbeat-segmentation and quality-assurance algorithms to extract physiologically-motivated features from PCG signals to diagnose AS. When tested on n=96 patients showing a diverse set of cardiac and non-cardiac conditions, the system was able to diagnose AS with 92% sensitivity and 95% specificity.

引用

页码：6673 / 6676

页数：4

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