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Springer Sounds
This dataset is derived from the repository published by David Springer. It comprises multiple phonocardiogram (PCG) signals, each sampled at a frequency of 1000 Hz.
Originally, the dataset was shared in MAT format to accompany the implementation of the article "Logistic Regression-HSMM-based Heart Sound Segmentation". The current version represents a subset of the original data, focusing solely on the signal values and their corresponding pre-computed labels.
Each signal in the dataset is annotated with labels identifying the four primary heart sound components:
- S1 (First Heart Sound)
- Systole
- S2 (Second Heart Sound)
- Diastole
In the CSV files, these components are numerically encoded as 1, 2, 3, and 4, respectively. The labels are provided for each timestep of the signal, allowing for precise temporal segmentation of the heart sounds. This refined dataset offers researchers and developers a streamlined resource for heart sound analysis, segmentation, and classification tasks in the field of cardiac acoustics and digital health.
References
- D. B. Springer, L. Tarassenko and G. D. Clifford, "Logistic Regression-HSMM-Based Heart Sound Segmentation," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822-832, April 2016, doi: 10.1109/TBME.2015.2475278. keywords: {Heart;Hidden Markov models;Electrocardiography;Phonocardiography;Detectors;Pathology;Logistics;Phonocardiography;Hidden Markov models;Logistic regression;Heart sound segmentation;Heart sound segmentation;hidden Markov models (HMMs);logistic regression (LR);phonocardiography (PCG)},
- Springer, D. (2019). Logistic Regression-HSMM-based Heart Sound Segmentation (version 1.0). PhysioNet. https://doi.org/10.13026/vnt9-kf93.
- Schmidt, S.E.; Holst-Hansen, C.; Graff, C.; Toft, E.; Struijk, J.J. Segmentation of heart sound recordings by a duration-dependent hidden markov model. Physiol Meas 2010, 31, 513-529.
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