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+ # Springer Sounds
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+ This dataset is derived from the [repository](https://github.com/davidspringer/Springer-Segmentation-Code) published by David Springer.
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+ It comprises multiple phonocardiogram (PCG) signals, each sampled at a frequency of 1000 Hz.
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+ Originally, the dataset was shared in MAT format to accompany the implementation of the article "Logistic Regression-HSMM-based Heart Sound Segmentation".
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+ The current version represents a subset of the original data, focusing solely on the signal values and their corresponding pre-computed labels.
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+ Each signal in the dataset is annotated with labels identifying the four primary heart sound components:
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+ 1. S1 (First Heart Sound)
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+ 2. Systole
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+ 3. S2 (Second Heart Sound)
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+ 4. Diastole
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+ In the CSV files, these components are numerically encoded as 1, 2, 3, and 4, respectively.
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+ The labels are provided for each timestep of the signal, allowing for precise temporal segmentation of the heart sounds.
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+ This refined dataset offers researchers and developers a streamlined resource for heart sound analysis, segmentation, and classification tasks in
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+ the field of cardiac acoustics and digital health.
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+
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+ ## References
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+ - 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)},
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+ - Springer, D. (2019). Logistic Regression-HSMM-based Heart Sound Segmentation (version 1.0). PhysioNet. https://doi.org/10.13026/vnt9-kf93.
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+ - 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.