The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
An open access database for the evaluation of heart sound algorithms
Open accessAbstract
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.
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Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Self-citationCk, F. (1970) · PubMed · Cited by 641
Logistic Regression-HSMM-based Heart Sound Segmentation
Springer, D., Tarassenko, L., & Clifford, G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2475278 · Cited by 518
Self-citationHeart sound segmentation algorithm based on heart sound envelogram
Liang, H., Lukkarinen, S., & Hartimo, I. (2002) · Computers in Cardiology 1997 · doi:10.1109/cic.1997.647841 · Cited by 414
Department of electrical engineering and computer science
Sarrafzadeh, M. (1990) · ACM SIGDA Newsletter · doi:10.1145/378886.380416 · Cited by 327
Segmentation of heart sound recordings by a duration-dependent hidden Markov model
Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/4/004 · Cited by 297
Self-citationNeural network classification of homomorphic segmented heart sounds
Gupta, C. N., Palaniappan, R., Swaminathan, S., & Krishnan, S. M. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2005.06.006 · Cited by 251
Support Vectors Machine-based identification of heart valve diseases using heart sounds
Maglogiannis, I., Loukis, E., Zafiropoulos, E., & Stasis, A. (2009) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.01.003 · Cited by 241
Clifford, G., Liu, C., Springer, D., Moody, B., Li, Q., Abad, R., Millet, J., Silva, I., Johnson, A., & Mark, R. (2016) · 2016 Computing in Cardiology Conference (CinC) · doi:10.22489/cinc.2016.179-154 · Cited by 233
Self-citationPapadaniil, C. D., & Hadjileontiadis, L. J. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2294399 · Cited by 191
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Cited by
Li, P., Hu, Y., & Liu, Z.-P. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102474 · Cited by 116
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey
Ganapathy, N., Swaminathan, R., & Deserno, T. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1667083 · Cited by 83
Metadata
- Authors
- Liu, C., Springer, D., Li, Q., Moody, B., Juan, R. A., Chorro, F. J., Castells, F., Roig, J. M., Silva, I., Johnson, A. E. W., Syed, Z., Schmidt, S. E., Papadaniil, C. D., Hadjileontiadis, L., Naseri, H., Moukadem, A., Dieterlen, A., Brandt, C., Tang, H., Samieinasab, M., Samieinasab, M. R., Sameni, R., Mark, R. G., & Clifford, G. D.
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2016-11-21
- Volume
- 37
- Issue
- 12
- Pages
- 2181-2213
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2557139718
- Cited by
- 820
- References
- 35
- FWCI
- 45.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Liu, C., Springer, D., Li, Q., Moody, B., Juan, R. A., Chorro, F. J., Castells, F., Roig, J. M., Silva, I., Johnson, A. E. W., Syed, Z., Schmidt, S. E., Papadaniil, C. D., Hadjileontiadis, L., Naseri, H., Moukadem, A., Dieterlen, A., Brandt, C., Tang, H., . . . Clifford, G. D. (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. https://doi.org/10.1088/0967-3334/37/12/2181
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