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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

Hilbert-Huang Dönüşümü ve Derin Öğrenme Kullanarak Akciğer Seslerinde Astım Teşhisi

How to cite: Altan G, Kutlu Y, Pekmezci A, Nural S. The diagnosis of asthma using hilbert-huang transform and deep learning on lung sounds. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(2): 100-105. DOI: 10.54856/jiswa.201912073

Full Text: PDF, in Turkish.

Total number of downloads: 813

Title: The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

Abstract: Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the lung sounds from 12 different auscultation regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequencytime analysis.

Keywords: Deep learning; lung auscultation; Hilbert-Huang transform; deep belief networks; asthma; wheezing


Başlık: Hilbert-Huang Dönüşümü ve Derin Öğrenme Kullanarak Akciğer Seslerinde Astım Teşhisi

Özet: Akciğer oskültasyonu stetoskop kullanılarak nefes alma ve nefes verme süreçlerinde hava yollarında meydana gelen sesleri kullanarak çeşitli solunum rahatsızlıklarının teşhisinde kullanılan en etkili ve olmazsa olmaz bir yöntemdir. Bu çalışmada göğüs ve sırtta 12 farklı bölgeden kaydedilen akciğer seslerine Hilbert-Huang dönüşümü uygulanarak elde edilen yeni formdaki içsel mod fonksiyonlarından elde edilen istatistiksel öznitelikler hesaplanmıştır. Derin İnanç Ağları (DİA) kullanılarak astım ve sağlıklı akciğer seslerinin sınıflandırılmasını gerçekleştirmiştir. Çift gizli katmanlı DİA sınıflandırıcı modeli 5 parçalı çapraz doğrulama yöntemiyle test edilmiştir. Önerilen DİA modeli astımlı ve sağlıklı bireylerin akciğer seslerinin frekans-zaman analiziyle %84.61 genel başarım, %85.83 hassasiyet ve %77.11 belirlilikle ayrıştırmıştır.

Anahtar kelimeler: Derin öğrenme; akciğer oskültasyonu; Hilbert-Huang dönüşümü; derin inanç ağları; astım; hırıltı


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