Tek Kanallı Akciğer Seslerinde Süzgeç Tipi Özellik Seçim Yöntemlerini Kullanarak Solunum Patolojisinin Teşhisi

Yıl 2022, Sayı: 34, 374 - 380, 31.03.2022

Mustafa Alptekin Engin , Latif Akçay , Selim Aras

https://doi.org/10.31590/ejosat.1082560

Öz

Yapılan çalışmada, tek kanallı yaygın akciğer sesleri kullanılarak patolojik ve sağlıklı denekler üzerinde detaylı bir öznitelik analizi gerçekleştirilmiştir. 94 kişiden elde edilen normal, ronküs, ince ral ve kaba ral seslerine ait 594 adet solunum döngüsünün otomatik tespiti ile elde edilen veri tabanı kullanılmıştır. Daha sonra, sıfır geçiş oranı, enerji, enerjinin entropisi, spektral merkezilik, bir çerçevenin yayılımı, spektral entropi, spektral akı, spektral devrilme, Mel-frekans cepstral katsayıları, harmonik oran, pencerenin temel frekansı ve renk vektörü öznitelik çıkarma yöntemleri veri tabanına uygulanmıştır. Sonsuz gizli öznitelik seçimi, sonsuz öznitelik seçimi, özvektör merkeziliği, minimum artıklık, maksimum ilgililik, relief, karşılıklı bilgi, laplace skoru, çoklu küme, fisher, denetimsiz ayrımcı, yerel öğrenmeye dayalı kümeleme, korelasyona dayalı öznitelik seçim yöntemleri eğitim aşamasında kullanılmıştır. Sınıflandırma için destek vektör makinesi, k en yakın komşu, naive bayes ve karar ağaçları algoritmaları kullanılmıştır. Sonuç olarak, öznitelik sayısı sınırlı olmadığı durumda, k en yakın komşuluk sınıflandırıcısı ve çoklu küme öznitelik seçim yöntemi kullanılarak %97,5 sınıflandırma doğruluğu elde edilmiştir. Öznitelik sayısı 3 ile sınırlandırıldığında ise k en yakın komşu sınıflandırıcısı ve özvektör merkeziliği veya sonsuz öznitelik seçimi yöntemleri kullanılarak %91,6 sınıflandırma doğruluğu elde edilmektedir.

Anahtar Kelimeler

Akciğer sesleri, Solunum Döngüsü, Sınıflandırma, Öznitelik Çıkarımı, Öznitelik Seçimi.

Diagnosing Respiratory Pathology Using Filter-Type Feature Selection Methods in Single-Channel Lung Sounds

Öz

In this study, detailed feature analysis was performed on the pathological and healthy subjects via single-channel common lung sounds. The database obtained from the automatic detection of 594 respiratory cycles of normal, rhonchi, fine crackle, and coarse crackle sounds obtained from 94 people was used. Then, zero-crossing rate, energy, the entropy of energy, spectral centroid, the spread of a frame, spectral entropy, spectral flux, spectral roll-off, Mel-frequency cepstral coefficients, harmonic ratio, the fundamental frequency of a window, and chroma vector feature extraction methods have been applied to the database. Infinite latent feature selection, infinite feature selection, eigenvector centrality, minimum redundancy maximum relevance, relief, mutual information, laplacian score, multi-cluster, fisher, unsupervised discriminative, local learning-based clustering, correlation-based feature selection methods are used in the training phase. Support vector machine, k nearest neighbors, naive bayes, and decision trees algorithms were used for classification. As a result, 97.5% classification accuracy was achieved by using the k nearest neighbor classifier and the multi-cluster feature selection method when the number of features is not limited. When the number of features is limited to 3, 91.6% classification accuracy is achieved by using the k nearest neighbor classifier and the eigenvector centrality or infinite feature selection methods.

Anahtar Kelimeler

Lung Sounds, Respiratory Cycle, Classification, Feature Extraction, Feature Selection.

Kaynakça

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