Clasificación de sonidos respiratorios adventicios a través de gradientes orientados
DOI:
https://doi.org/10.46842/ipn.cien.v28n1a04Palabras clave:
Histograma de gradientes orientados, sonidos respiratorios adventicios, transformada de Fourier de tiempo corto, transformada continua en ondículasResumen
Las enfermedades respiratorias representan una de las principales causas de muerte en el mundo. La prevención, el diagnóstico oportuno y el tratamiento efectivo son pilares fundamentales para reducir la propagación de enfermedades, así como su impacto negativo en la sociedad. El presente trabajo está enfocado en desarrollar un modelo computacional, a través del uso del algoritmo Histograma de Gradientes Orientados (HOG) en conjunto con técnicas de aprendizaje de máquina, capaz de clasificar sonidos respiratorios adventicios; para apoyar en el diagnóstico oportuno de enfermedades respiratorias. Para ello, se propone utilizar al HOG como extractor de características, el cual no ha sido explorado en la literatura actual sobre la clasificación de sonidos respiratorios. De igual manera, se plantea usar distintos algoritmos de aprendizaje de máquina, como: Máquina de Soporte Vectorial (SVM), K-Vecinos más Cercanos (KNN) y Bosques Aleatorios (RF). Asimismo, evaluar el modelo según el marco de trabajo proveído por la base de datos ICBHI17. La principal contribución del presente trabajo es el análisis de diferentes configuraciones del histograma de gradientes orientados para optimizar los modelos de aprendizaje de máquina. En donde, después de una serie de experimentos, nuestro mejor modelo surgió con la configuración Alternativa 4 (ALT 4); que obtuvo como resultados: 55.07% ACC, 34.37% BAL, 51.52% SCC, 75.87% SPE y 27.18% SEN.
Referencias
“The top 10 causes of death,” Who.int. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death [Accessed: 24-Nov-2023].
“Comunicado de Prensa Núm. 600/22 26 de octubre de 2022 página 1/92,” Org.mx. [Online]. Available: https://www.inegi.org.mx/contenidos/saladeprensa/boletines/2022/EDR/EDR2021_10.pdf [Accessed: 24-Nov-2023].
D. Perna, A. Tagarelli, “Deep Auscultation: Predicting Respiratory Anomalies and Diseases via Recurrent Neural Networks”, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 50-55, 2019, doi: https://doi.org/10.48550/arXiv.1907.05708
M. Sarkar, I. Madabhavi, N. Niranjan, M. Dogra, “Auscultation of the respiratory system,” Ann. Thorac. Med., vol. 10, no. 3, p. 158, 2015, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4518345/pdf/ATM-10-158.pdf
A. Bohadana, G. Izbicki, S. S. Kraman, “Fundamentals of lung auscultation,” N. Engl. J. Med., vol. 370, no. 8, pp. 744-751, 2014, https://www.nejm.org/doi/full/10.1056/nejmra1302901
R. Palaniappan, K. Sundaraj, N. Ahamed, A. Arjunan, S. Sundaraj, “Computer-based respiratory sound analysis: A systematic review,” IETE Tech. Rev., vol. 30, no. 3, p. 248, 2013, https://www.tandfonline.com/doi/abs/10.4103/0256-4602.113524
S. Reichert, R. Gass, C. Brandt, E. Andrès, “Analysis of respiratory sounds: State of the art,” Clin. Med. Circ. Respirat. Pulm. Med., vol. 2, p. CCRPM.S530, 2008, https://doi.org/10.4137/CCRPM
A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, N. Malmurugan, “Neural classification of lung sounds using wavelet coefficients,” Comput. Biol. Med., vol. 34, no. 6, pp. 523–537, 2004, https://doi.org/10.1016/S0010-4825(03)00092-1
B. M. Rocha et al., “An open access database for the evaluation of respiratory sound classification algorithms,” Physiol. Meas., vol. 40, no. 3, p. 035001, 2019, https://doi.org/10.1088/1361-6579/ab03ea
D. Hayes Jr, S. S. Kraman, “The physiologic basis of spirometry,” Respir. Care, vol. 54, no. 12, pp. 1717–1726, 2009, https://rc.rcjournal.com/content/54/12/1717
A.R.A. Sovijärvi, J. Vanderschoot, J.E. Earis, “Standardization of computerized respiratory sound analysis,” European Respiratory Review, 2000, https://www.ers-education.org/lr/show-details/?idP=37502
A. Gurung, C. G. Scrafford, J. M. Tielsch, O. S. Levine, W. Checkley, “Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis,” Respir. Med., vol. 105, no. 9, pp. 1396–1403, 2011, https://doi.org/10.1016/j.rmed.2011.05.007
A. Marques, A. Oliveira, C. Jácome, “Computerized adventitious respiratory sounds as outcome measures for respiratory therapy: A systematic review,” Respir. Care, vol. 59, no. 5, pp. 765–776, 2014, https://doi.org/10.1016/j.rmed.2011.05.007
R. X. A. Pramono, S. Bowyer, E. Rodriguez-Villegas, “Automatic adventitious respiratory sound analysis: A systematic review,” PLoS One, vol. 12, no. 5, p. e0177926, 2017, https://doi.org/10.1371/journal.pone.0177926
J. E. Earis, B. M. G. Cheetham, “Current methods used for computerized respiratory sound analysis”, European Respiratory Review, vol. 10, no. 77, pp. 586-590, 2000, https://www.researchgate.net/publication/327528906_Current_methods_used_for_computerized_respiratory_sound_analysis
A. Rakotomamonjy, G. Gasso, “Histogram of gradients of time-frequency representations for audio scene classification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 1, pp. 142–153, 2014, https://doi.org/10.1109/TASLP.2014.2375575
S. Abidin, R. Togneri, F. Sohel, “Spectrotemporal analysis using local binary pattern variants for acoustic scene classification,” IEEE ACM Trans. Audio Speech Lang. Process., vol. 26, no. 11, pp. 2112–2121, 2018, https://doi.org/10.1109/TASLP.2018.2854861
V. Bisot, S. Essid, G. Richard, “HOG and subband power distribution image features for acoustic scene classification,” in 2015 23rd European Signal Processing Conference (EUSIPCO), 2015, https://doi.org/10.1109/EUSIPCO.2015.7362477
G. Chambres, P. Hanna, M. Desainte-Catherine, “Automatic detection of patient with respiratory diseases using lung sound analysis,” in 2018 International Conference on Content-Based Multimedia Indexing (CBMI), 2018, https://doi.org/10.1109/CBMI.2018.8516489
N. Jakovljevic, T. ˇcar-Turukalo, “Hidden Markov Model Based Respiratory Sound Classification,” in Precision Medicine
Powered by pHealth and Connected Health, vol. 66. Singapore; Singapore: Springer, 2018,
https://link.springer.com/book/10.1007/978-981-10-7419-6
G. Serbes, S. Ulukaya, Y. P. Kahya, An Automated Lung Sound Preprocessing and Classification System Based OnSpectral Analysis Methods,” in Precision Medicine Powered by pHealth and Connected Health (N. Maglaveras, I. Chouvarda, and), vol. 66. Singapore; Singapore: Springer, 2018.
I. Ampiras, “Classification of Sounds Indicative of Respiratory Diseases,” in Engineering Applications of Neural Networks, vol. 1000. Cham: Springer International Publishing, 2019.
H. Pham, R. Phan, A. Palaniappan, I. Mertins, “CNNMoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection,” IEEE Journal of Biomedical and Health Informatics, vol. 25, pp. 2938–2947, 2021, https://doi.org/10.48550/arXiv.2004.04072
D. Ngo, L. Pham, A. Nguyen, B. Phan, K. Tran, T. Nguyen, “Deep learning framework applied for predicting anomaly of respiratory sounds,” in 2021 International Symposium on Electrical and Electronics Engineering (ISEE), 2021, https://doi.org/10.1109/ISEE51682.2021.9418742
G. Petmezas et al., “Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function,” Sensors (Basel), vol. 22, no. 3, p. 1232, 2022, https://doi.org/10.3390/s22031232
Y. Chang, Z. Ren, T. T. Nguyen, W. Nejdl, B. W. Schuller, “Example based Explanations with Adversarial Attacks for Respiratory Sound Analysis,” Proc. Interspeech 2022, pp. 4003-4007, 2022, https://doi.org/10.21437/Interspeech.2022-11355
I. Moummad, N. Farrugia, “Pretraining respiratory sound representations using metadata and contrastive learning,” arXiv [cs.SD], 2022, https://doi.org/10.48550/arXiv.2210.16192
J. Neto, N. Arrais, T. Vinuto, J. Lucena, “Convolution-Vision Transformer for Automatic Lung Sound Classification,” in 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Natal, Brazil: IEEE, 2022, pp. 97–102, https://doi.org/10.1109/SIBGRAPI55357.2022.9991756
Y. Ma et al., “LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using biResNet Deep Learning Algorithm,” in 2019 IEEE Biomedical Circuits and Systems Conference, Nara, Japan: IEEE, 2019, pp. 1–4,
https://doi.org/10.1109/BIOCAS.2019.8919021
Z. Yang, S. Liu, M. Song, E. Parada-Cabaleiro, B. W. Schuller, “Adventitious respiratory classification using attentive residual neural networks,” in Interspeech 2020, 2020, https://doi.org/10.21437/Interspeech.2020-2790
Y. Ma, X. Xu, Y. Li, “LungRN+NL: An improved adventitious lung sound classification using non-local block ResNet neural network with mixup data augmentation,” in Interspeech 2020, 2020, https://doi.org/10.21437/Interspeech.2020-2487
S. Gairola, F. Tom, N. Kwatra, M. Jain, “RespireNet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2021, pp. 527–530, 2021, https://doi.org/10.48550/arXiv.2011.00196
T. Nguyen, F. Pernkopf, “Lung sound classification using co-tuning and stochastic normalization,” IEEE Trans. Biomed. Eng., vol. 69, no. 9, pp. 2872–2882, 2022, https://doi.org/10.48550/arXiv.2108.01991
F. Tong, L. Liu, X. Xie, Q. Hong, L. Li, “Respiratory sound classification: From fluid-solid coupling analysis to feature-band attention,” IEEE Access, vol. 10, pp. 22018–22031, 2022, https://doi.org/10.1109/ACCESS.2022.3151789
L. Pham, D. Ngo, T. Hoang, A. Schindler, I. McLoughlin, “An ensemble of deep learning frameworks applied for predicting respiratory anomalies,” arXiv [cs.SD], 2022, https://doi.org/10.48550/arXiv.2201.03054
L. Pham, H. Phan, A. Schindler, R. King, A. Mertins, I. McLoughlin, “Inception-based network and multi-spectrogram ensemble applied to predict respiratory anomalies and lung diseases,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2021, pp. 253–256, 2021, https://doi.org/10.1109/EMBC46164.2021.9629857
T. Q. Vinh, N. M. Chau, T. N. N. Nam, N. T. Long, “Implementation of an irregular lung sound diagnostic system,” in 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE), 2022, https://doi.org/10.1109/ICCE55644.2022.9852035
N. Dalal, B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, https://doi.org/10.1109/CVPR.2005.177
N. Dalal, B. Triggs, C. Schmid, “Human detection using oriented histograms of flow and appearance”, in Lecture Notes in Computer Science, pp. 428-441, 2006, https://inria.hal.science/inria-00548587/document
Q. Zhang et al., “SPRSound: Open Source SJTU Pediatric Respiratory Sound Database,” IEEE Transactions on Biomedical Circuits and Systems, vol. 99, no. 99, 2022, https://doi.org/10.36227/techrxiv.20422479.v1
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2024 Instituto Politécnico Nacional
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
