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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.

Regional Signal Recognition of Body Sounds

Vücut Seslerinden Bölgesel Sinyal Teşhisi

How to cite: Ballı O, Kutlu Y. Regional signal recognition of body sounds. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2021; 4(2): 157-160. DOI: 10.54856/jiswa.202112187

Full Text: PDF, in English.

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Title: Regional Signal Recognition of Body Sounds

Abstract: One of the most important signals in the field of biomedicine is audio signals. Sound signals obtained from the body give us information about the general condition of the body. However, the detection of different sounds when recording audio signals belonging to the body or listening to them by doctors makes it difficult to diagnose the disease from these signals. In addition to isolating these sounds from the external environment, it is also necessary to separate their sounds from different parts of the body during the analysis. Separation of heart, lung and abdominal sounds will facilitate digital analysis, in particular. In this study, a dataset was created from the lungs, heart and abdominal sounds. MFCC (Mel Frekans Cepstrum Coefficient) coefficient data were obtained. The obtained coefficients were trained in the CNN (Convolution Neural Network) model. The purpose of this study is to classify audio signals. With this classification, a control system can be created. In this way, erroneous recordings that may occur when recording physicians' body voices will be prevented. When looking at the results, the educational success is about 98% and the test success is about 85%.

Keywords: body sounds; mel frequency cepstrum coefficients; deep learning


Başlık: Vücut Seslerinden Bölgesel Sinyal Teşhisi

Özet: Biyomedikal alanındaki en önemli sinyallerden birisi ses sinyalleridir. Vücuttan elde edilen ses sinyalleri bize vücudun genel durumu hakkında bilgi verir. Ancak, vücuda ait ses sinyallerini kaydederken veya doktorlar tarafından dinlenirken farklı seslerin algılanması, hastalığın bu sinyallerden teşhis edilmesini zorlaştırır. Bu sesleri dış ortamdan izole etmenin yanı sıra, analiz sırasında vücudun farklı bölgelerinden gelen seslerini de ayırmak gerekir. Kalp, akciğer ve karın seslerinin ayrılması, özellikle dijital analizi kolaylaştıracaktır. Bu çalışmada akciğerler, kalp ve karın seslerinden bir veriseti oluşturulmuştur. MFCC katsayı verileri alınmıştır. Elde edilen katsayılar CNN modelinde eğitilmiştir. Bu çalışmanın amacı ses sinyallerini sınıflandırmaktır. Bu sınıflandırma ile bir kontrol sistemi oluşturulabilecektir. Bu sayede hekimlerin vücut seslerini kaydederken oluşabilecek hatalı kayıtların önüne geçilecektir. Sonuçlara bakıldığında eğitim başarısı %98, test başarısı ise %85 civarındadır.

Anahtar kelimeler: vücut sesleri; mel frekansı spektrum katsayısıları; derin öğrenme


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