Research Article
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A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE

Year 2022, Volume: 17 Issue: 1, 79 - 88, 20.03.2022
https://doi.org/10.55525/tjst.1063039

Abstract

The method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure.

References

  • Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021, May). “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. In proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
  • Altan, G., & Kutlu, Y. (2018). Generative autoencoder kernels on deep learning for brain activity analysis. Natural and Engineering Sciences, 3(3), 311-322.
  • Ferreira, J., Ferro, M., Fernandes, B., Valenca, M., Bastos-Filho, C., & Barros, P. (2017, November). Extreme learning machine autoencoder for data augmentation. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)(pp. 1-6). IEEE.
  • Nishizaki, H. (2017, December). Data augmentation and feature extraction using variational autoencoder for acoustic modeling. In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1222-1227). IEEE.
  • Cao, G., & Kamata, S. I. (2019, September). Data augmentation for historical documents via cascade variational auto-encoder. In 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 340-345). IEEE.
  • Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. (2017). Differential data augmentation techniques for medical imaging classification tasks. In AMIA annual symposium proceedings (Vol. 2017, p. 979). American Medical Informatics Association.
  • Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018, April). Synthetic data augmentation using GAN for improved liver lesion classification. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 289-293). IEEE.
  • Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V. (2019). Data augmentation using learned transformations for one-shot medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8543-8553).
  • Eaton-Rosen, Z., Bragman, F., Ourselin, S., & Cardoso, M. J. (2018). Improving data augmentation for medical image segmentation.
  • ŞENGÜR, D. EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi, 1-1.
  • Shuvo, S. B., Ali, S. N., Swapnil, S. I., Hasan, T., & Bhuiyan, M. I. H. (2020). A lightweight cnn model for detecting respiratory diseases from lung auscultation sounds using emd-cwt-based hybrid scalogram. IEEE Journal of Biomedical and Health Informatics.
  • ÇOLAK, M., BENLİ, Ş. G., & Müge, D. O. L. U. Akciğer Hastalıklarının Dalgacık Katsayıları Kullanılarak Karar Ağaçlarına Dayalı Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (24), 463-468.
  • Demir, F., Ismael, A. M., & Sengur, A. (2020). Classification of lung sounds with cnn model using parallel pooling structure. IEEE Access, 8, 105376-105383.
  • N. Sengupta, M. Sahidullah and G. Saha, "Lung sound classification using cepstral-based statistical features", Comput. Biol. Med., vol. 75, pp. 118-129, Aug. 2016.
  • G.-C. Chang and Y.-F. Lai, "Performance evaluation and enhancement of lung sound recognition system in two real noisy environments", Comput. Methods Programs Biomed., vol. 97, pp. 141-150, Feb. 2010.
  • S. Reichert, R. Gass, C. Brandt and E. Andrès, "Analysis of respiratory sounds: State of the art", Clin. medicine. Circulatory Respiratory Pulmonary Med., vol. 2, pp. 1-14, Jan. 2008.
  • S. İçer and Ş. Gengeç, “Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds,” Digit. Signal Process., vol. 28, pp. 18–27, 2014, doi: 10.1016/j.dsp.2014.02.001.
  • A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, and N. Malmurugan, “Neural classification of lung sounds using wavelet coefficients,” Comput. Biol. Med., vol. 34, no. 6, pp. 523–537, 2004, doi: https://doi.org/10.1016/S0010-4825(03)00092-1.
  • S. ULUKAYA, G. SERBES, İ. ŞEN, and Y. P. KAHYA, “Akciğer Solunum Seslerinin Spektral Öznitelikler ile Sınıflandırılması,” Süleyman Demirel Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 22, no. 2, p. 711, 2017, doi: 10.19113/sdufbed.84471.
  • A. Sovijärvi et al., “Characteristic of breath sounds and adventitious respiratory sounds,” Charact. Breath Sounds Adventitious Respir. Sounds, vol. 10, pp. 591–596, Jan. 2000.
  • Rocha, B. M., Pessoa, D., Marques, A., Carvalho, P., & Paiva, R. P. (2021, January). Influence of Event Duration on Automatic Wheeze Classification. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 7462-7469). IEEE.
  • ER, M. B. (2020). Akciğer Seslerinin Derin Öğrenme ile Sınıflandırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8(4), 830-844.
  • Khan, S. I., Palodiya, V., & Poluboyina, L. (2021). Automated classification of human lung sound signals using phase space representation of intrinsic mode function.
  • Nguyen, T., & Pernkopf, F. (2021). Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks. arXiv preprint arXiv:2104.14921.
  • Khan, S. I., & Pachori, R. B. (2021). Automated classification of lung sound signals based on empirical mode decomposition. Expert Systems with Applications, 184, 115456.
  • Murphy, Raymond & Vyshedskiy, Andrey & Power-Charnitsky, Verna-Ann & Bana, Dhirendra & Marinelli, Patricia & Wong-Tse, Anna & Paciej, Rozanne. (2005). Automated Lung Sound Analysis in Patients With Pneumonia. Respiratory care. 49. 1490-7. 10.1378/chest.124.4_MeetingAbstracts.190S-b.
  • Aras, S., & Gangal, A. (2017, July). Comparison of different features derived from mel frequency cepstrum coefficients for classification of single channel lung sounds. In 2017 40th International Conference on Telecommunications and Signal Processing (TSP) (pp. 346-349). IEEE.
  • "Challenge", 2017, [online] Available: https://bhichallenge.med.auth.gr/.
  • Demir, F., Sengur, A., & Bajaj, V. (2020). Convolutional neural networks based efficient approach for classification of lung diseases. Health information science and systems, 8(1), 1-8.
  • Serbes, G., Ulukaya, S., & Kahya, Y. P. (2017, November). An automated lung sound preprocessing and classification system based onspectral analysis methods. In International Conference on Biomedical and Health Informatics (pp. 45-49). Springer, Singapore.
  • Soro, B., & Lee, C. (2019). A wavelet scattering feature extraction approach for deep neural network based indoor fingerprinting localization. Sensors, 19(8), 1790.
  • Sepúlveda, A., Castillo, F., Palma, C., & Rodriguez-Fernandez, M. (2021). Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning. Applied Sciences, 11(11), 4945.
  • Mallat, S. Group invariant scattering. Commun. Pure Appl. Math. 2012, 65, 1331–1398.
  • Lauraitis, A., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2020). Detection of speech impairments using cepstrum, auditory spectrogram and wavelet time scattering domain features. IEEE Access, 8, 96162-96172.Zscore
  • Fei, N., Gao, Y., Lu, Z., & Xiang, T. (2021). Z-Score Normalization, Hubness, and Few-Shot Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 142-151).
  • Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
  • Uzair, M., & Mian, A. (2016). Blind domain adaptation with augmented extreme learning machine features. IEEE transactions on cybernetics, 47(3), 651-660.
  • Javed, K., Gouriveau, R., & Zerhouni, N. (2014). SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing, 123, 299-307.
  • Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks, 17(1), 113-126.
  • Ucuz, I., Ciçek, A. U., Ari, A., Ozcan, O. O., & Sari, S. A. (2020). Determining the probability of juvenile delinquency by using support vector machines and designing a clinical decision support system. Medical hypotheses, 143, 110118.
  • Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
  • Berna, A. R. I., Ali, A. R. I., & ŞENGÜR, A. (2020). Suicide Prediction from Hemogram with Machine Learning. Avrupa Bilim ve Teknoloji Dergisi, 364-369.
  • Berna, A. R. I., İlknur, U. C. U. Z., Ali, A. R. I., Özdemir, F., & SENGUR, A. (2020). Grafik Tablet Kullanılarak Makine Öğrenmesi Yardımı ile El Yazısından Cinsiyet Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 243-252.
  • Chambres, G., Hanna, P., & Desainte-Catherine, M. (2018, September). Automatic detection of patient with respiratory diseases using lung sound analysis. In 2018 International Conference on Content-Based Multimedia Indexing (CBMI) (pp. 1-6). IEEE.
  • Gómez, A. F. R., & Orjuela-Cañón, A. D. (2021, May). Respiratory Sounds classification employing a Multi-label Approach. In 2021 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) (pp. 1-5). IEEE.
Year 2022, Volume: 17 Issue: 1, 79 - 88, 20.03.2022
https://doi.org/10.55525/tjst.1063039

Abstract

References

  • Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021, May). “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. In proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
  • Altan, G., & Kutlu, Y. (2018). Generative autoencoder kernels on deep learning for brain activity analysis. Natural and Engineering Sciences, 3(3), 311-322.
  • Ferreira, J., Ferro, M., Fernandes, B., Valenca, M., Bastos-Filho, C., & Barros, P. (2017, November). Extreme learning machine autoencoder for data augmentation. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)(pp. 1-6). IEEE.
  • Nishizaki, H. (2017, December). Data augmentation and feature extraction using variational autoencoder for acoustic modeling. In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1222-1227). IEEE.
  • Cao, G., & Kamata, S. I. (2019, September). Data augmentation for historical documents via cascade variational auto-encoder. In 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 340-345). IEEE.
  • Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. (2017). Differential data augmentation techniques for medical imaging classification tasks. In AMIA annual symposium proceedings (Vol. 2017, p. 979). American Medical Informatics Association.
  • Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018, April). Synthetic data augmentation using GAN for improved liver lesion classification. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 289-293). IEEE.
  • Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V. (2019). Data augmentation using learned transformations for one-shot medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8543-8553).
  • Eaton-Rosen, Z., Bragman, F., Ourselin, S., & Cardoso, M. J. (2018). Improving data augmentation for medical image segmentation.
  • ŞENGÜR, D. EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi, 1-1.
  • Shuvo, S. B., Ali, S. N., Swapnil, S. I., Hasan, T., & Bhuiyan, M. I. H. (2020). A lightweight cnn model for detecting respiratory diseases from lung auscultation sounds using emd-cwt-based hybrid scalogram. IEEE Journal of Biomedical and Health Informatics.
  • ÇOLAK, M., BENLİ, Ş. G., & Müge, D. O. L. U. Akciğer Hastalıklarının Dalgacık Katsayıları Kullanılarak Karar Ağaçlarına Dayalı Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (24), 463-468.
  • Demir, F., Ismael, A. M., & Sengur, A. (2020). Classification of lung sounds with cnn model using parallel pooling structure. IEEE Access, 8, 105376-105383.
  • N. Sengupta, M. Sahidullah and G. Saha, "Lung sound classification using cepstral-based statistical features", Comput. Biol. Med., vol. 75, pp. 118-129, Aug. 2016.
  • G.-C. Chang and Y.-F. Lai, "Performance evaluation and enhancement of lung sound recognition system in two real noisy environments", Comput. Methods Programs Biomed., vol. 97, pp. 141-150, Feb. 2010.
  • S. Reichert, R. Gass, C. Brandt and E. Andrès, "Analysis of respiratory sounds: State of the art", Clin. medicine. Circulatory Respiratory Pulmonary Med., vol. 2, pp. 1-14, Jan. 2008.
  • S. İçer and Ş. Gengeç, “Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds,” Digit. Signal Process., vol. 28, pp. 18–27, 2014, doi: 10.1016/j.dsp.2014.02.001.
  • A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, and N. Malmurugan, “Neural classification of lung sounds using wavelet coefficients,” Comput. Biol. Med., vol. 34, no. 6, pp. 523–537, 2004, doi: https://doi.org/10.1016/S0010-4825(03)00092-1.
  • S. ULUKAYA, G. SERBES, İ. ŞEN, and Y. P. KAHYA, “Akciğer Solunum Seslerinin Spektral Öznitelikler ile Sınıflandırılması,” Süleyman Demirel Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 22, no. 2, p. 711, 2017, doi: 10.19113/sdufbed.84471.
  • A. Sovijärvi et al., “Characteristic of breath sounds and adventitious respiratory sounds,” Charact. Breath Sounds Adventitious Respir. Sounds, vol. 10, pp. 591–596, Jan. 2000.
  • Rocha, B. M., Pessoa, D., Marques, A., Carvalho, P., & Paiva, R. P. (2021, January). Influence of Event Duration on Automatic Wheeze Classification. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 7462-7469). IEEE.
  • ER, M. B. (2020). Akciğer Seslerinin Derin Öğrenme ile Sınıflandırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8(4), 830-844.
  • Khan, S. I., Palodiya, V., & Poluboyina, L. (2021). Automated classification of human lung sound signals using phase space representation of intrinsic mode function.
  • Nguyen, T., & Pernkopf, F. (2021). Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks. arXiv preprint arXiv:2104.14921.
  • Khan, S. I., & Pachori, R. B. (2021). Automated classification of lung sound signals based on empirical mode decomposition. Expert Systems with Applications, 184, 115456.
  • Murphy, Raymond & Vyshedskiy, Andrey & Power-Charnitsky, Verna-Ann & Bana, Dhirendra & Marinelli, Patricia & Wong-Tse, Anna & Paciej, Rozanne. (2005). Automated Lung Sound Analysis in Patients With Pneumonia. Respiratory care. 49. 1490-7. 10.1378/chest.124.4_MeetingAbstracts.190S-b.
  • Aras, S., & Gangal, A. (2017, July). Comparison of different features derived from mel frequency cepstrum coefficients for classification of single channel lung sounds. In 2017 40th International Conference on Telecommunications and Signal Processing (TSP) (pp. 346-349). IEEE.
  • "Challenge", 2017, [online] Available: https://bhichallenge.med.auth.gr/.
  • Demir, F., Sengur, A., & Bajaj, V. (2020). Convolutional neural networks based efficient approach for classification of lung diseases. Health information science and systems, 8(1), 1-8.
  • Serbes, G., Ulukaya, S., & Kahya, Y. P. (2017, November). An automated lung sound preprocessing and classification system based onspectral analysis methods. In International Conference on Biomedical and Health Informatics (pp. 45-49). Springer, Singapore.
  • Soro, B., & Lee, C. (2019). A wavelet scattering feature extraction approach for deep neural network based indoor fingerprinting localization. Sensors, 19(8), 1790.
  • Sepúlveda, A., Castillo, F., Palma, C., & Rodriguez-Fernandez, M. (2021). Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning. Applied Sciences, 11(11), 4945.
  • Mallat, S. Group invariant scattering. Commun. Pure Appl. Math. 2012, 65, 1331–1398.
  • Lauraitis, A., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2020). Detection of speech impairments using cepstrum, auditory spectrogram and wavelet time scattering domain features. IEEE Access, 8, 96162-96172.Zscore
  • Fei, N., Gao, Y., Lu, Z., & Xiang, T. (2021). Z-Score Normalization, Hubness, and Few-Shot Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 142-151).
  • Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
  • Uzair, M., & Mian, A. (2016). Blind domain adaptation with augmented extreme learning machine features. IEEE transactions on cybernetics, 47(3), 651-660.
  • Javed, K., Gouriveau, R., & Zerhouni, N. (2014). SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing, 123, 299-307.
  • Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks, 17(1), 113-126.
  • Ucuz, I., Ciçek, A. U., Ari, A., Ozcan, O. O., & Sari, S. A. (2020). Determining the probability of juvenile delinquency by using support vector machines and designing a clinical decision support system. Medical hypotheses, 143, 110118.
  • Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
  • Berna, A. R. I., Ali, A. R. I., & ŞENGÜR, A. (2020). Suicide Prediction from Hemogram with Machine Learning. Avrupa Bilim ve Teknoloji Dergisi, 364-369.
  • Berna, A. R. I., İlknur, U. C. U. Z., Ali, A. R. I., Özdemir, F., & SENGUR, A. (2020). Grafik Tablet Kullanılarak Makine Öğrenmesi Yardımı ile El Yazısından Cinsiyet Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 243-252.
  • Chambres, G., Hanna, P., & Desainte-Catherine, M. (2018, September). Automatic detection of patient with respiratory diseases using lung sound analysis. In 2018 International Conference on Content-Based Multimedia Indexing (CBMI) (pp. 1-6). IEEE.
  • Gómez, A. F. R., & Orjuela-Cañón, A. D. (2021, May). Respiratory Sounds classification employing a Multi-label Approach. In 2021 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) (pp. 1-5). IEEE.
There are 45 citations in total.

Details

Primary Language English
Journal Section TJST
Authors

Berna Arı 0000-0003-1000-2619

Ömer Faruk Alçin 0000-0002-2917-3736

Abdülkadir Şengür 0000-0003-1614-2639

Publication Date March 20, 2022
Submission Date January 25, 2022
Published in Issue Year 2022 Volume: 17 Issue: 1

Cite

APA Arı, B., Alçin, Ö. F., & Şengür, A. (2022). A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE. Turkish Journal of Science and Technology, 17(1), 79-88. https://doi.org/10.55525/tjst.1063039
AMA Arı B, Alçin ÖF, Şengür A. A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE. TJST. March 2022;17(1):79-88. doi:10.55525/tjst.1063039
Chicago Arı, Berna, Ömer Faruk Alçin, and Abdülkadir Şengür. “A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE”. Turkish Journal of Science and Technology 17, no. 1 (March 2022): 79-88. https://doi.org/10.55525/tjst.1063039.
EndNote Arı B, Alçin ÖF, Şengür A (March 1, 2022) A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE. Turkish Journal of Science and Technology 17 1 79–88.
IEEE B. Arı, Ö. F. Alçin, and A. Şengür, “A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE”, TJST, vol. 17, no. 1, pp. 79–88, 2022, doi: 10.55525/tjst.1063039.
ISNAD Arı, Berna et al. “A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE”. Turkish Journal of Science and Technology 17/1 (March 2022), 79-88. https://doi.org/10.55525/tjst.1063039.
JAMA Arı B, Alçin ÖF, Şengür A. A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE. TJST. 2022;17:79–88.
MLA Arı, Berna et al. “A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE”. Turkish Journal of Science and Technology, vol. 17, no. 1, 2022, pp. 79-88, doi:10.55525/tjst.1063039.
Vancouver Arı B, Alçin ÖF, Şengür A. A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE. TJST. 2022;17(1):79-88.