Öz
Son yıllarda biyomedikal sinyal işleme alanındaki gelişmelere rağmen, akciğer rahatsızlıklarının tespiti üzerine hızlı ve yüksek doğrulukta çalışan teşhis sistemlerine duyulan ihtiyaç artmaktadır. Yapılan çalışmada fiziki muayene ile 94 farklı kişiden, solunum döngülerinin otomatik olarak tespit edilmesiyle elde edilen 150 adet normal ve 444 adet normal olmayan akciğer sesleri veri tabanı olarak kullanılmıştır. Sınıflandırma işleminde öznitelik olarak frekans ve zaman bölgesinde 12 farklı yöntem uygulanmıştır. Tüm veriler %80 eğitim %20 test aşamasında kullanılacak şekilde ikiye bölünmüştür. Elde edilen öznitelikler gömülü ve sarıcı öznitelik seçim yöntemleri kullanılarak değerlendirilmiştir. Bu yöntemler; özyinelemeli öznitelik eliminasyonu, uyarlanabilir yapı öğrenimi ile öznitelik seçimi, bağımlılık kılavuzlu denetimsiz öznitelik seçimi, sıralı yerellik ile denetimsiz öznitelik seçimi, içbükey küçültme yoluyla öznitelik seçimi, en küçük mutlak büzülme ve seçim operatörü öznitelik seçim yöntemleri olarak isimlendirilmektedir. İncelenen bu öznitelikler doğrusal destek vektör makineleri, k en yakın komşuluk, karar ağaçları ve naive bayes yöntemleri ile sınıflandırılmıştır. Sonuç olarak öznitelik sayısının sınırlandırılmadığı durum için, özyinelemeli öznitelik eliminasyonu yönteminin k en yakın komşuluk sınıflandırma ile beraber kullanıldığı durum için %97,3 doğruluk değerindeki başarıma ulaşılmaktadır. Öznitelik sayısının üç ile sınırlandırıldığı durumda ise uyarlanabilir yapı öğrenimi ile öznitelik seçimi yönteminin karar ağaçları yöntemi ile beraber kullanılması ile %91,4 değerinde başarıma ulaşılmıştır.
Anahtar Kelimeler
Solunum sesleri öznitelik çıkarımı öznitelik seçimi sınıflandırma
Kaynakça
- Aras, S. (2018). Tek Kanallı Yaygın Akciğer Seslerinden Solunum Döngülerinin Otomatik Algılanması ve Sınıflandırılması, Doktora Tezi, Karadeniz Teknik Üniversitesi. Fen Bilimleri Enstitüsü.
- Aras, S., & Öztürk, M. (2018). Automatic detection of the respiratory cycle from recorded, single-channel sounds from lungs”. Turk J of Electr Eng Comput Scı, 26, 11–22.
- Azmy, M. M. (2015). Classification of lung sounds based on linear prediction cepstral coefficients and support vector machine”. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 1–5.
- Bartsch, M. A., & Wakefield, G. H. (2005). Audio thumbnailing of popular music using chroma-based representations”. IEEE Transactions on Multimedia, 7, 96–104.
- Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Amsterdam, The Netherlands: Elsevier Science Publisher.
- Bohadana, A., Izbicki, G., & Kraman, S. S. (2014). Fundamentals of lung auscultation”. N Engl J Med, 370(21).
- Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines”. In Machine Learning Proceedings of the Fifteenth International Conference (pp. 82–90). Cortes, C., & Vapnik, V. (1995). Support-vector networks”. Mach Learn, 20, 273–297.
- Du, L., & Shen, Y. D. (2015). Unsupervised feature selection with adaptive structure learning”. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 209–218).
- Fix, E., & Hodges, J. L. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties”. USAF School of Aviation Medicine, 41(128).
- Forman, G. (2003). An Extensive Empirical Study of Feature Selection Metrics for Text Classification”. Journal of Machine Learning Research, 3, 1289–1305.
- Guo, J., Quo, Y., Kong, X., & He, R. (2017). Unsupervised feature selection with ordinal locality”. In IEEE International Conference on Multimedia and Expo (ICME) (pp. 1213–1218).
- Guo, J., & Zhu, W. (2018). Dependence guided unsupervised feature selection”. In Proc. AAAI Conf. Artificial Intell. (AAAI) (pp. 2232–2239). New Orleans, Louisiana.
- Gurung, A., Scrafford, C. G., Tielsch, J. M., Levine, O., & Checkley, W. (2011). Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis”. Respir Med, 105(9), 1396–1403.
- Guyon, I. (2006). Feature extraction: foundations and applications. Springer Science &Business Media.
- Guyon, I., Weston, J., & Barnhill, S. (2002). Gene Selection for Cancer Classification using Support Vector Machines”. Machine Learning, 46, 389–422.
- Himeshima, M., Yamashita, M., Matsunaga, S., & Miyahara, S. (2012). Detection of Abnormal Lung Sounds Taking into Account Duration Distribution for Adventitious Sounds”. In Signal Processing Conference (EUSIPCO) (pp. 1821–1825). Bucharest, Romania: Ağustos.
- İçer, S., & Gengeç, Ş. (2014). Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds”. Digit Signal Process, 28, 18–27.
- Kim, H. G., Moreau, N., & Sikora, T. (2007). MPEG-7 audio and beyond: Audio content indexing and retrieval. Nashville, TN: John Wiley & Sons.
- Kim, Y., Hyon, Y., & Jung, S. S. (2021). Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning”. Sci Rep, 11.
- Koeipensri, T., Boonchoo, P., & Sueaseenak, D. (2016). The development of biosignal processing system (BPS-SWU V1. 0) for learning and research in biomedical engineering”. In 9th Biomedical Engineering International Conference (BMEiCON) (pp. 1–4). Laos.
- Ladha, L., & Deepa, T. (2011). Feature Selection Methods And Algorithms”. International Journal on Computer Science and Engineering, 3(5), 1787–1797.
- Lehrer, S. (2008). Understanding lung sounds with audio CD (3rd ed.). London, England: W B Saunders.
- Li, J., & Hong, Y. (2015). Wheeze Detection Algorithm Based on Spectrogram Analysis”. Computational Intelligence and Design (ISCID), 318–322.
- Mitra, P., & Murthy, C. A. (2002). Unsupervised feature selection using feature similarity”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3), 301–312.
- Mondal, A., Bhattacharya, P., & Saha, G. (2014). Detection of lungs status using morphological complexities of respiratory sounds”. ScientificWorld Journal.
- Palaniappan, R., Sundaraj, K., & Lam, C. K. (2016). Reliable system for respiratory pathology classification from breath sound signals”. In IEEE International Conference on System Reliability and Science (ICSRS).
- Sankur, B., Kahya, Y. P., Çağatay, G. E., & Engin, T. (1994). Comparison of AR-based algorithms for respiratory sounds classification”. Comput Biol Med, 24(1), 67–76.
- Şen, I., Saraclar, M., & Kahya, Y. P. (2015). A Comparison of DVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds”. IEEE Transactions on Biomedical Engineering, 62(7), 1768–1776.
- Sezer, E. A., Bozkır, A. S., Yağız, S., & Gökçeoğlu, C. (2010). Karar Ağacı Derinliğinin CART Algoritmasında Kestirim Kapasitesine Etkisi: Bir Tünel Açma Makinesinin İlerleme Hızı Üzerinde Uygulama”. Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso”. Journal of the Royal Statistical Society Series B (Methodological), 267–288.
- Volkmann, J., Stevens, S. S., & Newman, E. B. (1937). A scale for the measurement of the psychological magnitude pitch”. J Acoust Soc Am, 8(3), 208–208.
- Xie, S., Jin, F., Krishnan, S., & Sattar, F. (2012). Signal feature extraction by multi-scale PCA and its application to respiratory sound classification”. Med Biol Eng Comput, 50(7), 759–768.
- Yilmaz, C. A., & Kahya, Y. P. (2006). Multi-channel classification of respiratory sounds”. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2864–2867). New York, USA.
Öz
Despite the advances in biomedical signal processing in recent years, the need for fast and highly accurate diagnostic systems for the detection of lung disorders continues. In the study, 150 normal and 444 abnormal lung sounds obtained by automatic detection of respiratory cycles from 94 different people by physical examination were used as a database. Then, 12 different feature extraction methods were applied in the time and frequency domain. Features were evaluated using embedded and wrapper feature selection methods. These methods are recursive feature elimination, adaptive structure learning, dependence-guided unsupervised feature selection, unsupervised feature selection with ordinal locality, feature selection via concave minimization, least absolute shrinkage, and selection operator feature selection methods. Features are classified by linear support vector machines, k nearest neighbor, decision trees, and naive Bayes classification methods. As a result, when the number of features is not limited, 97.3% accuracy is obtained when the recursive feature elimination method is used together with the k nearest neighbor classifier. In the case where the number of features is limited to three, the classification accuracy of 91.4% was achieved using the adaptive structure learning feature selection method and the decision trees.
Anahtar Kelimeler
Respiratory sounds feature extraction feature selection classification
Kaynakça
- Aras, S. (2018). Tek Kanallı Yaygın Akciğer Seslerinden Solunum Döngülerinin Otomatik Algılanması ve Sınıflandırılması, Doktora Tezi, Karadeniz Teknik Üniversitesi. Fen Bilimleri Enstitüsü.
- Aras, S., & Öztürk, M. (2018). Automatic detection of the respiratory cycle from recorded, single-channel sounds from lungs”. Turk J of Electr Eng Comput Scı, 26, 11–22.
- Azmy, M. M. (2015). Classification of lung sounds based on linear prediction cepstral coefficients and support vector machine”. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 1–5.
- Bartsch, M. A., & Wakefield, G. H. (2005). Audio thumbnailing of popular music using chroma-based representations”. IEEE Transactions on Multimedia, 7, 96–104.
- Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Amsterdam, The Netherlands: Elsevier Science Publisher.
- Bohadana, A., Izbicki, G., & Kraman, S. S. (2014). Fundamentals of lung auscultation”. N Engl J Med, 370(21).
- Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines”. In Machine Learning Proceedings of the Fifteenth International Conference (pp. 82–90). Cortes, C., & Vapnik, V. (1995). Support-vector networks”. Mach Learn, 20, 273–297.
- Du, L., & Shen, Y. D. (2015). Unsupervised feature selection with adaptive structure learning”. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 209–218).
- Fix, E., & Hodges, J. L. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties”. USAF School of Aviation Medicine, 41(128).
- Forman, G. (2003). An Extensive Empirical Study of Feature Selection Metrics for Text Classification”. Journal of Machine Learning Research, 3, 1289–1305.
- Guo, J., Quo, Y., Kong, X., & He, R. (2017). Unsupervised feature selection with ordinal locality”. In IEEE International Conference on Multimedia and Expo (ICME) (pp. 1213–1218).
- Guo, J., & Zhu, W. (2018). Dependence guided unsupervised feature selection”. In Proc. AAAI Conf. Artificial Intell. (AAAI) (pp. 2232–2239). New Orleans, Louisiana.
- Gurung, A., Scrafford, C. G., Tielsch, J. M., Levine, O., & Checkley, W. (2011). Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis”. Respir Med, 105(9), 1396–1403.
- Guyon, I. (2006). Feature extraction: foundations and applications. Springer Science &Business Media.
- Guyon, I., Weston, J., & Barnhill, S. (2002). Gene Selection for Cancer Classification using Support Vector Machines”. Machine Learning, 46, 389–422.
- Himeshima, M., Yamashita, M., Matsunaga, S., & Miyahara, S. (2012). Detection of Abnormal Lung Sounds Taking into Account Duration Distribution for Adventitious Sounds”. In Signal Processing Conference (EUSIPCO) (pp. 1821–1825). Bucharest, Romania: Ağustos.
- İçer, S., & Gengeç, Ş. (2014). Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds”. Digit Signal Process, 28, 18–27.
- Kim, H. G., Moreau, N., & Sikora, T. (2007). MPEG-7 audio and beyond: Audio content indexing and retrieval. Nashville, TN: John Wiley & Sons.
- Kim, Y., Hyon, Y., & Jung, S. S. (2021). Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning”. Sci Rep, 11.
- Koeipensri, T., Boonchoo, P., & Sueaseenak, D. (2016). The development of biosignal processing system (BPS-SWU V1. 0) for learning and research in biomedical engineering”. In 9th Biomedical Engineering International Conference (BMEiCON) (pp. 1–4). Laos.
- Ladha, L., & Deepa, T. (2011). Feature Selection Methods And Algorithms”. International Journal on Computer Science and Engineering, 3(5), 1787–1797.
- Lehrer, S. (2008). Understanding lung sounds with audio CD (3rd ed.). London, England: W B Saunders.
- Li, J., & Hong, Y. (2015). Wheeze Detection Algorithm Based on Spectrogram Analysis”. Computational Intelligence and Design (ISCID), 318–322.
- Mitra, P., & Murthy, C. A. (2002). Unsupervised feature selection using feature similarity”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3), 301–312.
- Mondal, A., Bhattacharya, P., & Saha, G. (2014). Detection of lungs status using morphological complexities of respiratory sounds”. ScientificWorld Journal.
- Palaniappan, R., Sundaraj, K., & Lam, C. K. (2016). Reliable system for respiratory pathology classification from breath sound signals”. In IEEE International Conference on System Reliability and Science (ICSRS).
- Sankur, B., Kahya, Y. P., Çağatay, G. E., & Engin, T. (1994). Comparison of AR-based algorithms for respiratory sounds classification”. Comput Biol Med, 24(1), 67–76.
- Şen, I., Saraclar, M., & Kahya, Y. P. (2015). A Comparison of DVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds”. IEEE Transactions on Biomedical Engineering, 62(7), 1768–1776.
- Sezer, E. A., Bozkır, A. S., Yağız, S., & Gökçeoğlu, C. (2010). Karar Ağacı Derinliğinin CART Algoritmasında Kestirim Kapasitesine Etkisi: Bir Tünel Açma Makinesinin İlerleme Hızı Üzerinde Uygulama”. Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso”. Journal of the Royal Statistical Society Series B (Methodological), 267–288.
- Volkmann, J., Stevens, S. S., & Newman, E. B. (1937). A scale for the measurement of the psychological magnitude pitch”. J Acoust Soc Am, 8(3), 208–208.
- Xie, S., Jin, F., Krishnan, S., & Sattar, F. (2012). Signal feature extraction by multi-scale PCA and its application to respiratory sound classification”. Med Biol Eng Comput, 50(7), 759–768.
- Yilmaz, C. A., & Kahya, Y. P. (2006). Multi-channel classification of respiratory sounds”. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2864–2867). New York, USA.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Elektrik Elektronik Mühendisliği |
| Yazarlar | |
| Yayımlanma Tarihi | 3 Eylül 2022 |
| Gönderilme Tarihi | 30 Haziran 2022 |
| Yayımlandığı Sayı | Yıl 2022 |