Classification Analysis using CNN and LSTM on Wheezing Sounds
DOI:
https://doi.org/10.21108/ijoict.v8i1.621Keywords:
classification algorithm, wheezing, convolutional neural network, long short term memoryAbstract
Asthma is a public health problem in almost all countries in the world. One of the symptoms that exist in asthmatics is wheezing. In several studies, wheezing has been classified using classification algorithm. However, the implemented classification algorithm still has a low level of accuracy. This study aims to determine the accuracy of the results from wheezing classification of respiratory sounds by comparing the algorithm.
Downloads
Download data is not yet available.
References
1.) A. C.-R. a. F. A. G. G. D. Sosa, "Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM," 10th Int. Symp. Med. Inf. Process. Anal., vol. 9287, no. 1, p. 928709, 2015, doi: 10.1117/12.2073614., 2015.
2.) Ö. K. B. K. a. S. S. M. Aykanat, "Classification of lung sounds using convolutional neural networks," Eurasip J. Image Video Process, vol. 2017, no. no. 1, 2017, doi: 10.1186/s13640-017-0213-2., 2017.
3.) O. B. a. M. Bahoura, "Efficient FPGA-based architecture of an," J. Syst. Archit, vol. 88, no. pp. 54–64, 2018, doi:10.1016/j.sysarc.2018.05.010, 2018.
4.) D. O. a. V. Bilas, "Asthmatic Wheeze Detection from Compressively," IEEE J. Biomed. Heal. Informatics,, vol. 22, no. 5, pp. 1406–1414, 2018, doi: 10.1109/JBHI.2017.2781135, 2018.
5.) E. P. S. A. I. A. F. Kirill Kochetov, "Wheeze Detection Using Convolutional Neural Networks," EPIA Conf. Artif. Intelegent, vol. 1, no. . d, pp. 87–94, 2017, doi:, 2017.
6.) B. M. P. F. a. C. D. P. Bokov, "Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric," Comput. Biol. Med, vol. 70, no. pp. 40–50, 2016, doi:10.1016/j.compbiomed.2016.01.002., 2016.
7.) A. P. a. M. Pawar, "Analysis of deformities in lung using short time Fourier transform spectrogram analysis on lung sound," Proc. - 2011 Int.Conf. Comput. Intell. Commun. Syst. CICN, no. pp. 177–181, 2011, doi: 10.1109/CICN.2011.35., 2011.
8.) A. B. O. a. R. E. S. E. Lapian, "RECURRENT NEURAL NETWORK FOR SPEAKING RECOGNITION IN DITALCES MANADO," Medicus, vol. 5, no. 3, pp. 3–4, 2018.
9.) E. M. E. Gershwin and T. E. Albertson, "Bronchial Asthma," Humana Press, vol. 53, no. 9, 2013.
10.) A. B. a. B. P. S. F. Syafria, "Lung Voice Recognition with MFCC as Feature Extraction and Backpropagation as Classifier," Ilmu Komput. dan Agri-Informatika, vol. 3, no. 1, p. 27, doi: 10.29244/jika.3.1.27-36., 2017.
11.) R. Y. a. D. P. A. Krisdanti, "Imunopatogenesis Asthma," J.Respirasi, vol. 3, no. 1, p. 26, 2019, doi: 10.20473/jr.v3-i.1.2017.26-33, 2019.
12.) H. Hasanah, "Comparative Evaluation of Short Time Fourier Transform (STFT) and Wigner Distribution (WD) on Electrocardiogram Classification(ECG)," no. pp. 1–7.
13.) A. R. a. D. K. B. E. T. Handono, "Tune determination Javanese gamelan using the short time fourier algorithm transform," no. pp. 1–12.
14.) A. Y. W. a. R. S. I. W. S. E. Putra, "Image Classification Using Convolutional Neural Network ( Cnn ) On Caltech 101 Image Classfication Using Convolution Neural Network ( Cnn ) on Caltech 101," Inst. Teknol. Sepuluh Novemb, 2016.
15.) S. I. a. A. Nilogiri, "Implementation of Deep Learning in Identification Types of Plants Based on Leaf Image Using Convolutional Neural Network," JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones, vol. 3, no. 2, pp. 49–56, 2018, doi: 10.32528/JUSTINDO.V3I2.2254., 2018.
16.) K. J. Piczak, "ENVIRONMENTAL SOUND CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS," IEEE 25th Int. Work. Mach. Learn. Signal Process, no. pp. 1–6, 2015, doi: 10.1109/MLSP.2015.7324337., 2015.
17.) S. H. A. E. P. Heriyanto, "CEPSTRAL FREQUENCY MEL FREQUENCY EXTRACTION COEFFICIENT (MFCC) AND AVERAGE COEFFICIENT FOR QUR'AN READING CHECK," TELEMATIKA, vol. 15, no. 02, OKTOBER, 2018, Pp. 99 – 108, 2018.
18.) A. S. a. M. Bahoura, "Long Short Term Memory Based Recurrent Neural Network for Wheezing Detection in Pulmonary Sounds," IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2021.
19.) P. A. R. R. M. Ardiyansyah, "Comparative Analysis of Data Mining Classification Algorithms For Blogger Dataset With Rapid Miner," JURNAL KHATULISTIWA INFORMATIKA, vol. 6, no. 1 p-ISSN: 2339-1928 & e-ISSN: 2579-633X, 2018.
20.) V. K. O. K. Afshin Gholamy, "Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation," Departement Technical Reports(CS), 2018.
21.) D. M. M. B. K. K. E. C. T. Jennifer Jepkoech, "The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 12, no. 8, 2021, 2021.
22.) M. C. Benyamin Ghojogh, "The Theory Behind Overfitting, Cross Validation,Regularization, Bagging, and Boosting: Tutorial," no. arXiv:1905.12787v1, 2019.
23.) A. B. O. a. R. E. S. A. Anggoro, "Recurrent Neural Network For Speech Recognition in Recurrent Neural Network for Speech," vol. 5, no. 3, pp. 6431–6435, 2018.
2.) Ö. K. B. K. a. S. S. M. Aykanat, "Classification of lung sounds using convolutional neural networks," Eurasip J. Image Video Process, vol. 2017, no. no. 1, 2017, doi: 10.1186/s13640-017-0213-2., 2017.
3.) O. B. a. M. Bahoura, "Efficient FPGA-based architecture of an," J. Syst. Archit, vol. 88, no. pp. 54–64, 2018, doi:10.1016/j.sysarc.2018.05.010, 2018.
4.) D. O. a. V. Bilas, "Asthmatic Wheeze Detection from Compressively," IEEE J. Biomed. Heal. Informatics,, vol. 22, no. 5, pp. 1406–1414, 2018, doi: 10.1109/JBHI.2017.2781135, 2018.
5.) E. P. S. A. I. A. F. Kirill Kochetov, "Wheeze Detection Using Convolutional Neural Networks," EPIA Conf. Artif. Intelegent, vol. 1, no. . d, pp. 87–94, 2017, doi:, 2017.
6.) B. M. P. F. a. C. D. P. Bokov, "Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric," Comput. Biol. Med, vol. 70, no. pp. 40–50, 2016, doi:10.1016/j.compbiomed.2016.01.002., 2016.
7.) A. P. a. M. Pawar, "Analysis of deformities in lung using short time Fourier transform spectrogram analysis on lung sound," Proc. - 2011 Int.Conf. Comput. Intell. Commun. Syst. CICN, no. pp. 177–181, 2011, doi: 10.1109/CICN.2011.35., 2011.
8.) A. B. O. a. R. E. S. E. Lapian, "RECURRENT NEURAL NETWORK FOR SPEAKING RECOGNITION IN DITALCES MANADO," Medicus, vol. 5, no. 3, pp. 3–4, 2018.
9.) E. M. E. Gershwin and T. E. Albertson, "Bronchial Asthma," Humana Press, vol. 53, no. 9, 2013.
10.) A. B. a. B. P. S. F. Syafria, "Lung Voice Recognition with MFCC as Feature Extraction and Backpropagation as Classifier," Ilmu Komput. dan Agri-Informatika, vol. 3, no. 1, p. 27, doi: 10.29244/jika.3.1.27-36., 2017.
11.) R. Y. a. D. P. A. Krisdanti, "Imunopatogenesis Asthma," J.Respirasi, vol. 3, no. 1, p. 26, 2019, doi: 10.20473/jr.v3-i.1.2017.26-33, 2019.
12.) H. Hasanah, "Comparative Evaluation of Short Time Fourier Transform (STFT) and Wigner Distribution (WD) on Electrocardiogram Classification(ECG)," no. pp. 1–7.
13.) A. R. a. D. K. B. E. T. Handono, "Tune determination Javanese gamelan using the short time fourier algorithm transform," no. pp. 1–12.
14.) A. Y. W. a. R. S. I. W. S. E. Putra, "Image Classification Using Convolutional Neural Network ( Cnn ) On Caltech 101 Image Classfication Using Convolution Neural Network ( Cnn ) on Caltech 101," Inst. Teknol. Sepuluh Novemb, 2016.
15.) S. I. a. A. Nilogiri, "Implementation of Deep Learning in Identification Types of Plants Based on Leaf Image Using Convolutional Neural Network," JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones, vol. 3, no. 2, pp. 49–56, 2018, doi: 10.32528/JUSTINDO.V3I2.2254., 2018.
16.) K. J. Piczak, "ENVIRONMENTAL SOUND CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS," IEEE 25th Int. Work. Mach. Learn. Signal Process, no. pp. 1–6, 2015, doi: 10.1109/MLSP.2015.7324337., 2015.
17.) S. H. A. E. P. Heriyanto, "CEPSTRAL FREQUENCY MEL FREQUENCY EXTRACTION COEFFICIENT (MFCC) AND AVERAGE COEFFICIENT FOR QUR'AN READING CHECK," TELEMATIKA, vol. 15, no. 02, OKTOBER, 2018, Pp. 99 – 108, 2018.
18.) A. S. a. M. Bahoura, "Long Short Term Memory Based Recurrent Neural Network for Wheezing Detection in Pulmonary Sounds," IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2021.
19.) P. A. R. R. M. Ardiyansyah, "Comparative Analysis of Data Mining Classification Algorithms For Blogger Dataset With Rapid Miner," JURNAL KHATULISTIWA INFORMATIKA, vol. 6, no. 1 p-ISSN: 2339-1928 & e-ISSN: 2579-633X, 2018.
20.) V. K. O. K. Afshin Gholamy, "Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation," Departement Technical Reports(CS), 2018.
21.) D. M. M. B. K. K. E. C. T. Jennifer Jepkoech, "The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 12, no. 8, 2021, 2021.
22.) M. C. Benyamin Ghojogh, "The Theory Behind Overfitting, Cross Validation,Regularization, Bagging, and Boosting: Tutorial," no. arXiv:1905.12787v1, 2019.
23.) A. B. O. a. R. E. S. A. Anggoro, "Recurrent Neural Network For Speech Recognition in Recurrent Neural Network for Speech," vol. 5, no. 3, pp. 6431–6435, 2018.
Downloads
Published
2022-08-20
How to Cite
Gustav Bagus Samanta. (2022). Classification Analysis using CNN and LSTM on Wheezing Sounds. International Journal on Information and Communication Technology (IJoICT), 8(1), 60–68. https://doi.org/10.21108/ijoict.v8i1.621
Issue
Section
Intelligence System
License
Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.
.png)
.jpg)
