心脏瓣膜疾病患者心音反流数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES
Mohammad Fraiwan , Ali Ibnian , Nishi Shahnaj Haider
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引用次数: 0

摘要

心脏反流是一种以血液回流为特征的心脏疾病,在听诊时产生可听到的杂音。如果不及时治疗,它会导致严重的并发症,影响心脏功能。本文介绍了一个全面的心音记录数据集,包括主动脉瓣反流(AR)、二尖瓣反流(MR)、三尖瓣反流(TR)和健康的心音,这些数据来自一家医院使用电子听诊器收集的患者。对于每个参与者,从三个标准的胸部位置获得记录,所有诊断都由经验丰富的心脏病专家确认。该数据集提供了高质量的标记录音,记录了不同类型和位置的反刍声的可变性。它旨在支持用于检测心脏异常的自动算法的开发和评估,包括机器学习和信号处理方法。此外,该数据集为医学生和实习临床医生提供了一个教育资源,以练习听诊技能,识别不同类型的反流性杂音,并提高诊断能力。通过公开这些录音,数据集可以作为心脏听诊研究和临床培训的基准资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dataset of heart sound regurgitation of patients with heart valve disorders
Heart regurgitation is a cardiac condition characterized by the backward flow of blood, producing audible murmur sounds detectable during auscultation. If left untreated, it can lead to serious complications affecting cardiac function. This article presents a comprehensive dataset of heart sound recordings, including aortic regurgitation (AR), mitral regurgitation (MR), tricuspid regurgitation (TR), and healthy heart sounds, collected from patients at a single hospital using an electronic stethoscope. For each participant, recordings were obtained from three standard chest locations, and all diagnoses were confirmed by an experienced cardiologist. The dataset provides high-quality, labeled recordings that capture the variability of regurgitation sounds across different types and locations. It is intended to support the development and evaluation of automated algorithms for detecting cardiac abnormalities, including machine learning and signal processing approaches. Additionally, this dataset offers an educational resource for medical students and trainee clinicians to practice auscultation skills, recognize different types of regurgitation murmurs, and improve diagnostic proficiency. By making these recordings publicly available, the dataset can serve as a benchmark resource for both research and clinical training in cardiac auscultation.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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