唐洪

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:生物医学工程学院

学科:生物医学工程. 信号与信息处理

办公地点:大连理工大学电信学部

联系方式:tanghong@dlut.edu.cn

电子邮箱:tanghong@dlut.edu.cn

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An open access database for the evaluation of heart sound algorithms

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论文类型:期刊论文

发表时间:2016-12-01

发表刊物:PHYSIOLOGICAL MEASUREMENT

收录刊物:SCIE、Scopus

卷号:37

期号:12

页面范围:2181-2213

ISSN号:0967-3334

关键字:heart sound; phonocardiogram (PCG); database; heart sound classification; heart sound segmentation; PhysioNet/CinC Challenge

摘要:In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.