Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer

Mahmoud Fakhry , Abeer FathAllah Brery

Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (3) : 1832 -1856.

PDF (2936KB)
Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (3) :1832 -1856. DOI: 10.3934/era.2026082
Research article
research-article
Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer
Author information +
History +
PDF (2936KB)

Abstract

Systolic murmurs are extra heart sounds that occur during the contraction phase of the cardiac cycle, often indicating heart abnormalities caused by turbulent blood flow. Their intensity, pitch, and quality vary, requiring precise identification for the accurate diagnosis of cardiac disorders. This study presents an automatic classification system for systolic murmurs using a feature extraction module, followed by a classification model. The feature extraction module employs complex orthogonal matching pursuit to project single or multiple murmur segments onto a redundant dictionary composed of multiresolution complex Gabor basis functions (GBFs). The resulting projection weights are split and reshaped into variable-resolution time–frequency feature matrices. Processing multiple segments of a single recording using a shared dictionary mitigates murmur variability. This is achieved by learning the weights for each segment while enforcing that they correspond to the same set of basis functions in the dictionary, promoting consistent time–frequency feature matrices. The classification model is built based on a vision transformer to process multiple input matrices of different resolutions by passing each through a convolutional neural network for patch tokenization. All embedding tokens are then concatenated to form a matrix and forwarded to an encoder layer that includes multihead attention, residual connections, and a convolutional network with a kernel size of one. This integration of multiresolution feature extraction with transformer-based feature classification enhances the accuracy and reliability of heart murmur identification. An experimental analysis of four types of systolic murmurs from the CirCor DigiScope dataset demonstrates the effectiveness of the system, achieving a classification accuracy of 95.96%.

Keywords

cardiovascular disease / systolic murmurs / heart sounds / multiresolution complex Gabor dictionary / vision transformer

Cite this article

Download citation ▾
Mahmoud Fakhry, Abeer FathAllah Brery. Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer. Electronic Research Archive, 2026, 34 (3) : 1832-1856 DOI:10.3934/era.2026082

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

World Health Organization, Cardiovascular Diseases CVDs , 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

[2]

H. Jung, L. S. Lilly, The cardiac cycle: Mechanisms of heart sounds and murmurs, in Pathophysiology of Heart Disease: A Collaborative Project of Medical Students and Faculty , 5th edition, Chapter 2, Philadelphia, (2011), 28-53.

[3]

B. Boashash, time-frequency Signal Analysis and Processing: A Comprehensive Reference , 2nd edition, Academic Press, Oxford, 2015.

[4]

S. Mallat, Z. Zhang, Matching pursuit with time-frequency dictionaries, IEEE Trans. Signal Process., 41 (1993), 3397-3415. https://doi.org/10.1109/78.258082

[5]

X. Zhang, L. G. Durand, L. Senhadji, H. C. Lee, J. L. Coatrieux, Analysis-synthesis of the phonocardiogram based on the matching pursuit method, IEEE Trans. Biomed. Eng., 45 (1998), 962-971. https://doi.org/10.1109/10.704865

[6]

X. Zhang, L. Durand, L. Senhadji, H. Lee, J. L. Coatrieux, time-frequency scaling transformation of the phonocardiogram based of the matching pursuit method, IEEE Trans. Biomed. Eng., 45 (1998), 972-979. https://doi.org/10.1109/10.704866

[7]

I. Goodfellow, Y. Bengio, A. Courville, Deep Learning , MIT Press, Cambridge, MA, 2017.

[8]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, (2017), 5998-6008.

[9]

Y. Wang, Y. Deng, Y. Zheng, P. Chattopadhyay, L. Wang, Vision transformers for image classification: A comparative survey, Technologies, 13 (2025), 32. https://doi.org/10.3390/technologies13010032

[10]

J. Oliveira, F. Renna, P. D. Costa, M. Nogueira, C. Oliveira, C. Ferreira, et al., The circor digiscope dataset: From murmur detection to murmur classification, IEEE J. Biomed. Health. Inf., 26 (2022), 2524-2535. https://doi.org/10.1109/JBHI.2021.3137048

[11]

R. O. Bonow, D. L. Mann, D. P. Zipes, P. Libby, Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine , 9th edition, Elsevier Health Sciences, Philadelphia, 2011.

[12]

J. Singh, R. Anand, Computer aided analysis of phonocardiogram, J. Med. Eng. Technol., 31 (2007), 319-323. https://doi.org/10.1080/03091900500282772

[13]

Y. Zheng, X. Guo, X. Ding, A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification, Expert Syst. Appl., 42 (2015), 2710-2721. https://doi.org/10.1016/j.eswa.2014.10.051

[14]

P. D. Stein, H. N. Sabbah, J. B. Lakier, S. R. Kemp, D. J. Magilligan, Frequency content of heart sounds and systolic murmurs in patients with porcine bioprosthetic valves: Diagnostic value for the early detection of valvular degeneration, Henry Ford Hosp. Med. J., 30 (1982), 119-123.

[15]

M. Akay, time-frequency and Wavelets in Biomedical Signal Processing , Wiley-IEEE Press, New York, 1998.

[16]

M. Fakhry, A. Gallardo-Antolín, Variational mode decomposition and a light cnn-lstm model for classification of heart sound signals, in IEEE EUROCON 2023 - 20th International Conference on Smart Technologies, Torino, Italy, (2023), 295-300. https://doi.org/10.1109/EUROCON56442.2023.10199054

[17]

N. Atanasov, T. Ning, Isolation of systolic heart murmurs using wavelet transform and energy index, in 2008 Congress on Image and Signal Processing, Sanya, China, (2008), 216-220. https://doi.org/10.1109/CISP.2008.758

[18]

A. Haghighi-Mood, N. Torry, time-frequency analysis of systolic murmurs, in Computers in Cardiology 1997, IEEE, (1997), 113-116. https://doi.org/10.1109/CIC.1997.647843

[19]

J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, J. Opt. Soc. Am. A, 2 (1985), 1160-1169. https://doi.org/10.1364/JOSAA.2.001160

[20]

M. Fakhry, A. F. Brery, A. Gallardo-Antolín, Analysis of heart sound signals using sparse modeling with gabor dictionary, in 2022 IEEE International Symposium on Multimedia (ISM), Italy, (2022), 92-96. https://doi.org/10.1109/ISM55400.2022.00021

[21]

M. Fakhry, A. Gallardo-Antolín, Elastic net regularization and gabor dictionary for classification of heart sound signals using deep learning, Eng. Appl. Artif. Intell., 127 (2024), 107406. https://doi.org/10.1016/j.engappai.2023.107406

[22]

M. Fakhry, A. Gallardo-Antolín, Analysis of systolic murmurs in heart sounds using multiresolution complex gabor dictionary, in 2024 International Conference on Computer and Applications (ICCA), IEEE, Cairo, Egypt, (2024), 783-787. https://doi.org/10.1109/ICCA62237.2024.10927981

[23]

S. Jabbari, H. Ghassemian, Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders, Comput. Biol. Med., 41 (2011), 802-811. https://doi.org/10.1016/j.compbiomed.2011.06.016

[24]

M. Shabbir, X. Liu, M. Nasseri, S. Helgeson, Heart murmur classification in phonocardiogram representations using convolutional neural networks, in The International FLAIRS Conference Proceedings, 36 (2023). https://doi.org/10.32473/flairs.36.133189

[25]

C. Yin, Y. Zheng, X. Ding, Y. Shi, J. Qin, X. Guo, Detection of coronary artery disease based on clinical phonocardiogram and multiscale attention convolutional compression network, IEEE J. Biomed. Health. Inf., 28 (2024), 1353-1362. https://doi.org/10.1109/JBHI.2024.3354832

[26]

J. Kim, G. Park, B. Suh, Classification of phonocardiogram recordings using vision transformer architecture, in 2022 Computing in Cardiology (CinC), IEEE, Tampere, Finland, 498 (2022), 1-4. https://doi.org/10.22489/CinC.2022.084

[27]

Z. Liu, H. Jiang, F. W. Zhang, W. B. Ouyang, X. Li, X. Pan, Heart sound classification based on bispectrum features and vision transformer mode, Alexandria Eng. J., 85 (2023), 49-59. https://doi.org/10.1016/j.aej.2023.11.035

[28]

J. Han, A. Shaout, Enact-heart - ensemble-based assessment using cnn and transformer on heart sounds, preprint, arXiv:2502.16914. https://doi.org/10.48550/arXiv.2502.16914

[29]

W. Zhao, H. Ma, N. Jin, Y. Zheng, X. Guo, Detection of coronary heart disease based on heart sound and hybrid vision transformer, Appl. Acoust., 230 (2025), 110420. https://doi.org/10.1016/j.apacoust.2024.110420

[30]

R. Wang, Y. Duan, Y. Li, D. Zheng, X. Liu, C. T. Lam, et al., Pctmf-net: heart sound classification with parallel CNNs-transformer and second-order spectral analysis, Vis. Comput., 39 (2023), 3811-3822. https://doi.org/10.1007/s00371-023-03031-5

[31]

S. Qiu, H. G. Feichtinger, Discrete gabor structures and optimal representations, IEEE Trans. Signal Process., 43 (1995), 2258-2268. https://doi.org/10.1109/78.469862

[32]

I. Rish, G. Grabarnik, Sparse Modeling: Theory, Algorithms, and Applications , CRC Press, 2014.

[33]

Z. Průša, N. Holighaus, P. Balázs, Fast matching pursuit with multi-Gabor dictionaries, ACM Trans. Math. Software, 47 (2021), 1-20. https://doi.org/10.1145/3447958

[34]

Z. Zhang, S. Wei, D. Wei, L. Li, F. Liu, C. Liu, Comparison of four recovery algorithms used in compressed sensing for ecg signal processing, in 2016 Computing in Cardiology Conference (CinC), IEEE, (2016), 401-404.

[35]

W. Dai, O. Milenkovic, Subspace pursuit for compressive sensing signal reconstruction, IEEE Trans. Inf. Theory, 55 (2008), 2230-2249. https://doi.org/10.1109/TIT.2009.2016006

[36]

K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770-778.

PDF (2936KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉