article · 2025
Trainable Fractional Spectrogram for Phonocardiogram Classification
E. A. Nehary, Sreeraman C. Rajan
Cited by 0Abstract and details only
Abstract
Automatic classification of phonocardiogram (PCG) acquired through an electronic stethoscope is currently done using deep learning methods. Deep learning methods need large PCG datasets. Such datasets often exhibit heterogeneity due to differences in sensor specifications, recording locations, recording environments, and patient pathologies. Heterogeneous data pose significant challenges for classification, and training a single deep learning model that can generalize across such heterogeneous data is difficult. This work proposes a single deep learning model with a trainable fractional spectrogram (TFS) as input instead of traditional spectrograms. Using the newly proposed trainable fractional order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> as input, and our previously proposed domain-balanced training, the model adapts the representation to provide improved classification despite heterogeneity in PCG. Preliminary results show that TFS outperforms the traditional spectrogram, with improvements of approximately 4% in average balanced accuracy and 11% in average Matthews correlation coefficient.
Cite this paper
Nehary, E. A., & Rajan, S. C. (2025). Trainable Fractional Spectrogram for Phonocardiogram Classification. https://doi.org/10.1109/sensors59705.2025.11330251
Nehary, E. A., and Sreeraman C. Rajan. “Trainable Fractional Spectrogram for Phonocardiogram Classification.” 2025, pp. 1–4, https://doi.org/10.1109/sensors59705.2025.11330251.
Nehary, E. A., and Sreeraman C. Rajan. 2025. “Trainable Fractional Spectrogram for Phonocardiogram Classification.” https://doi.org/10.1109/sensors59705.2025.11330251.
Nehary, E.A. and Rajan, S.C. (2025) ‘Trainable Fractional Spectrogram for Phonocardiogram Classification’. Available at: https://doi.org/10.1109/sensors59705.2025.11330251.
@article{nehary2025trainable,
title = {{Trainable Fractional Spectrogram for Phonocardiogram Classification}},
author = {Nehary, E. A. and Rajan, Sreeraman C.},
pages = {1--4},
year = {2025},
doi = {10.1109/sensors59705.2025.11330251}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.