Version 1 - July 2017
When referencing this material, please cite:
@article{UNICAPROP,
author = {M. Samieinasab and R. Sameni},
title = {Fetal phonocardiogram extraction using single channel blind source separation},
conference = {2015 23rd Iranian Conference on Electrical Engineering},
pages = {78-83},
year = {2015}
}
Chengyu Liu, David Springer, Qiao Li, Benjamin Moody, Ricardo Abad Juan, Francisco J Chorro, Francisco Castells, José Millet Roig, Ikaro Silva, Alistair E W Johnson, Zeeshan Syed, Samuel E Schmidt, Chrysa D Papadaniil, Leontios Hadjileontiadis, Hosein Naseri, Ali Moukadem, Alain Dieterlen, Christian Brandt, Hong
Tang, Maryam Samieinasab, Mohammad Reza Samieinasab, Reza Sameni, Roger G Mark, and Gari D Clifford.
An open access database for the evaluation of heart sound algorithms, Physiological Meassurement. 2016 Dec;37(12):2181-2213.
[bib]
@article{0967-3334-37-12-2181,
author = {Chengyu Liu and David Springer and Qiao Li and Benjamin Moody and Ricardo Abad Juan and Francisco J Chorro and Francisco Castells and José Millet Roig and Ikaro Silva and Alistair E W Johnson and Zeeshan Syed and Samuel E Schmidt and Chrysa D Papadaniil and Leontios Hadjileontiadis and Hosein Naseri and Ali Moukadem and Alain Dieterlen and Christian Brandt and Hong Tang and Maryam Samieinasab and Mohammad Reza Samieinasab and Reza Sameni and Roger G Mark and Gari D Clifford},
title = {An open access database for the evaluation of heart sound algorithms},
journal = {Physiological Measurement},
volume = {37},
number = {12},
pages = {2181-2213},
url = {http://stacks.iop.org/0967-3334/37/i=12/a=2181},
year = {2016},
abstract = {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.},
}
Please also include the standard citation for
PhysioNet:
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG,
Mietus JE, Moody GB, Peng C-K, Stanley HE.
PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
Research Resource for Complex Physiologic Signals.
Circulation 101(23):e215-e220
[Circulation Electronic Pages;
http://circ.ahajournals.org/content/101/23/e215];
2000 (June 13).
[bib]
@article{PhysioNet,
author = {Goldberger, Ary L. and Amaral, Luis A. N.
and Glass, Leon and Hausdorff, Jeffrey M.
and Ivanov, Plamen Ch. and Mark, Roger G.
and Mietus, Joseph E. and Moody, George B.
and Peng, Chung-Kang and Stanley, H. Eugene},
title = {{PhysioBank}, {PhysioToolkit}, and {PhysioNet}:
Components of a New Research Resource for Complex
Physiologic Signals},
journal = {Circulation},
publisher = {American Heart Association, Inc.},
volume = {101},
number = {23},
year = {2000},
month = {June},
pages = {e215--e220},
doi = {10.1161/01.CIR.101.23.e215},
issn = {0009-7322},
url = {http://circ.ahajournals.org/content/101/23/e215}
}
Introduction
The Shiraz University (SU) fetal heart sounds database (SUFHSDB) contains fetal and maternal PCG recordings from 109 pregnant women.
Data Collection
This database was constructed using recordings made from 109 pregnant women (mothers aged from 16 to 47 years, mean±SD: 29.3±5.8 years with BMI from 19.5 to 38.9 mean±SD: 29.2±4.0), with a digital JABES Electronic stethoscope (GS Technology Co. Ltd., South Korea) placed on the lower maternal abdomen as described in (Samieinasab and Sameni, 2015).
In the case of twins (seven cases) the data were collected twice according to the locations advised by the expert gynecologist. Audacity® cross-platform audio software was used for recording and editing the signals on a PC. In total, 99 subjects had one signal recorded, three subjects had two and seven cases of twins were recorded individually, resulting in 119 total recordings. The average duration of each record is about 90 seconds. The sampling rate was generally 16,000 Hz with 16-bit quantization and a few recordings at 44,100 Hz. The data was recorded in wide-band mode of the digital stethoscope, with a frequency response of 20 Hz to 1 kHz.
In most cases (91 subjects), the heart sounds of the mothers were also recorded before each fetal PCG recording session. As a result, a total number of 92 maternal heart sounds data (90 subjects had one heart sound signal but one had two signals recorded) are also available in the dataset.
Files
The record names are in the form fSS or mSS where f stands for fetal recording, m stands for maternal recording, and SS is the subject number. There are also WFDB headers provided to read the data using WFDB format.
Contributors
The SUFHSDB was provided by Dr. Reza Sameni from Shiraz University. The datasets were recorded as part of the Master's thesis of Ms. Maryam Samieinasab at Shiraz University.
Acknowledgements
The authors would like to thank Dr. M. Hosseiniasl and Ms. Nasihatkon from Shiraz Hafez Hospital, for their valuable assistance during fetal PCG recordings.
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f43.hea 2017-07-13 11:18 95 header file
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f43.wav 2017-07-13 11:18 5.8M
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f110.wav 2017-07-13 11:18 5.5M
f75.wav 2017-07-13 11:18 5.5M
f112.hea 2017-07-13 11:18 98 header file
f85.wav 2017-07-13 11:18 3.0M
f14.wav 2017-07-13 11:18 5.5M
m27.hea 2017-07-13 11:18 94 header file
m90.wav 2017-07-13 11:18 1.9M
f1.wav 2017-07-13 11:18 2.3M
m49.wav 2017-07-13 11:18 1.9M
f15.hea 2017-07-13 11:18 95 header file
f77.hea 2017-07-13 11:18 95 header file
m52.wav 2017-07-13 11:18 1.9M
f54.wav 2017-07-13 11:18 5.5M
f97.wav 2017-07-13 11:18 5.5M
f56.wav 2017-07-13 11:18 5.5M
f47.hea 2017-07-13 11:18 95 header file
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f60.hea 2017-07-13 11:18 95 header file
m25.hea 2017-07-13 11:18 94 header file
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m68.hea 2017-07-13 11:18 94 header file
f34.hea 2017-07-13 11:18 95 header file
m26.wav 2017-07-13 11:18 1.8M
m66.wav 2017-07-13 11:18 1.9M
m89.wav 2017-07-13 11:18 1.8M
![[ ]](/icons/index.gif)
RECORDS 2017-07-13 11:18 893 list of record names
m46.wav 2017-07-13 11:18 1.9M
m15.hea 2017-07-13 11:18 94 header file
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f26.wav 2017-07-13 11:18 5.5M
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f82.hea 2017-07-13 11:18 94 header file
m76.hea 2017-07-13 11:18 94 header file
f20.wav 2017-07-13 11:18 5.5M
f79.wav 2017-07-13 11:18 3.6M
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f77.wav 2017-07-13 11:18 5.5M
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f86.wav 2017-07-13 11:18 2.3M
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m43.wav 2017-07-13 11:18 1.9M
f81.hea 2017-07-13 11:18 94 header file
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m25.wav 2017-07-13 11:18 1.8M
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m105.hea 2017-07-13 11:18 97 header file
m50.hea 2017-07-13 11:18 94 header file
f84.wav 2017-07-13 11:18 2.3M
f81.wav 2017-07-13 11:18 2.6M
f17-1.hea 2017-07-13 11:18 101 header file
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f22.hea 2017-07-13 11:18 95 header file
m105.wav 2017-07-13 11:18 1.8M
m11.wav 2017-07-13 11:18 1.9M
f68.wav 2017-07-13 11:18 5.5M
m88.hea 2017-07-13 11:18 94 header file
m52.hea 2017-07-13 11:18 94 header file
f2.hea 2017-07-13 11:18 91 header file
f95.wav 2017-07-13 11:18 5.5M
f94.hea 2017-07-13 11:18 95 header file
m28.wav 2017-07-13 11:18 2.5M
f66.wav 2017-07-13 11:18 5.5M
f9-1.hea 2017-07-13 11:18 96 header file
f6.wav 2017-07-13 11:18 4.5M
f108.wav 2017-07-13 11:18 6.1M
m55.wav 2017-07-13 11:18 1.9M
DOI 2018-08-29 15:43 19
MD5SUMS 2018-08-30 16:37 18K
SHA1SUMS 2018-08-30 16:37 21K
SHA256SUMS 2018-08-30 16:37 31K
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Updated
Friday, 28 October 2016 at 16:58 EDT
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