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用于开发自动栓子检测算法的多普勒超声数据集。

Doppler ultrasound dataset for the development of automatic emboli detection algorithms.

作者信息

Pierleoni Paola, Mercuri Marco, Belli Alberto, Pieri Massimo, Marroni Alessandro, Palma Lorenzo

机构信息

Department of Information Engineering, Università Politecnica Delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy.

DAN Europe Foundation Research Division, Sir Ugo Mifsud Street, Ta' Xbiex, XBX 1431, Malta.

出版信息

Data Brief. 2019 Nov 4;27:104739. doi: 10.1016/j.dib.2019.104739. eCollection 2019 Dec.

DOI:10.1016/j.dib.2019.104739
PMID:31763396
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6864345/
Abstract

The article describes a dataset of doppler ultrasound audio tracks taken on a sample of 30 divers according to the acquisition protocol defined by the Divers Alert Network. The audio tracks are accompanied by a medical evaluation for the decompression sickness risk according to the Spencer's scale levels. During the acquisition campaign, each diver in the post-dive phase was subjected to a double doppler ultrasound examination of approximately 45 seconds each one in the precordial area using a Huntleigh FD1 Fetal doppler probe. The two measurements were separated by a time of 8-10 seconds necessary for carrying out specific physical exercises designed to free the bubbles trapped in the tissues. The audio tracks were stored without compression via the TASCAM DP-004 recorder and processed in order to eliminate the noise generated by the positioning of the probe and the time interval between the two measurements. The audio tracks recorded during the acquisition campaign have been evaluated by experts belonging to three independent blind teams in order to provide an assessment of the decompression sickness risk according to Extended Spencer's scale. The specific typology of doppler ultrasound audio tracks and the associated medical evaluation according to the Spencer's scale levels make this dataset useful for the development, testing, and performance evaluation of new audio processing algorithms capable of automatically detecting bubbles in the blood vessels.

摘要

本文描述了一个多普勒超声音频轨道数据集,该数据集是根据潜水员警报网络定义的采集协议,对30名潜水员的样本进行采集得到的。这些音频轨道还伴有根据斯宾塞量表级别对减压病风险进行的医学评估。在采集过程中,潜水后的每位潜水员在心脏前区使用Huntleigh FD1胎儿多普勒探头进行了两次多普勒超声检查,每次检查约45秒。两次测量之间间隔8 - 10秒,这是进行特定体育锻炼以释放组织中被困气泡所必需的时间。音频轨道通过TASCAM DP - 004录音机无压缩存储,并进行处理以消除探头定位和两次测量之间的时间间隔产生的噪声。采集过程中记录的音频轨道由三个独立的盲测团队的专家进行评估,以便根据扩展斯宾塞量表对减压病风险进行评估。多普勒超声音频轨道的特定类型以及根据斯宾塞量表级别进行的相关医学评估,使得该数据集对于开发、测试和性能评估能够自动检测血管中气泡的新音频处理算法很有用。

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引用本文的文献

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An open-source framework for synthetic post-dive Doppler ultrasound audio generation.用于合成潜水后多普勒超声音频生成的开源框架。
PLoS One. 2023 Apr 27;18(4):e0284922. doi: 10.1371/journal.pone.0284922. eCollection 2023.

本文引用的文献

1
Patent foramen ovale and scuba diving: a practical guide for physicians on when to refer for screening.卵圆孔未闭与水肺潜水:医生关于何时转诊进行筛查的实用指南
Extrem Physiol Med. 2013 Apr 1;2(1):10. doi: 10.1186/2046-7648-2-10.
2
Acute management of vascular air embolism.血管空气栓塞的急性处理
J Emerg Trauma Shock. 2009 Sep;2(3):180-5. doi: 10.4103/0974-2700.55330.
3
A method for the automated detection of venous gas bubbles in humans using empirical mode decomposition.一种使用经验模态分解自动检测人体静脉气泡的方法。
Ann Biomed Eng. 2005 Oct;33(10):1411-21. doi: 10.1007/s10439-005-6045-8.
4
Automated determination of bubble grades from Doppler ultrasound recordings.通过多普勒超声记录自动确定气泡等级。
Aviat Space Environ Med. 2005 Aug;76(8):771-7.
5
Fast detection of venous air embolism in Doppler heart sound using the wavelet transform.
IEEE Trans Biomed Eng. 1997 Apr;44(4):237-46. doi: 10.1109/10.563293.
6
Spectral analysis of Doppler ultrasonic decompression data.多普勒超声减压数据的频谱分析。
Ultrasonics. 1977 May;15(3):105-10. doi: 10.1016/0041-624x(77)90026-9.