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

• Telceken M, Kutlu Y. Detecting abnormalities in heart sounds. Journal of Intelligent Systems with Applications 2021; 4(2): 137-143.
• Cancioglu E, Sahin S, Isler Y. Heart sounds analysis and classification based on long-short term memory. Journal of Intelligent Systems with Applications 2020; 3(1): 25-28.
• Altan G, Kutlu Y, Pekmezci AO, Nural S. The diagnosis of asthma using hilbert-huang transform and deep learning on lung sounds. Journal of Intelligent Systems with Applications 2019; 2(2): 100-105.
• Selek MB, Duyar MC, Isler Y. Electronic stethoscope design. Journal of Intelligent Systems with Applications 2020; 3(1): 10-16
• Altan G, Kutlu Y, Pekmezci AO, Nural S. Deep learning with 3D-second order difference plot on respiratory sounds. Biomedical Signal Processing and Control 2018; 45: 58–69.
• Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR. Application of semi-supervised deep learning to lung sound analysis. In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 16-20, 2016, Orlando, FL, USA, pp. 804–807.
• Aykanat M, Kilic O, Kurt B, Saryal S. Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing 2017; 65(2017).
• Altan G, Kutlu Y, Gokcen A. Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds. Turkish Journal of Electrical Engineering and Computer Sciences 2020; 28(5): 2979–2996.
• Kamarulafizam I, Salleh SH, Najeb JM, Ariff AK, Chowdhury A. Heart sound analysis using MFCC and time frequency distribution. IFMBE Proceedings 2007; 14(1): 946–949.
• Chauhan S, Wang P, Sing Lim C, Anantharaman V. A computer-aided MFCC-based HMM system for automatic auscultation. Computers in Biology and Medicine 2008; 38(2): 221–233.
• Balli O, Kutlu Y. Effect of window size for detection of abnormalities in respiratory sounds. Natural and Engineering Sciences 2019; 4(3): 124-129.
• Balli O, Kutlu Y. Effect of deep learning feature inference techniques on respiratory sounds. Journal of Intelligent Systems with Applications 2020; 3(2): 134-137.
• Nowak JK, Nowak R, Radzikowski K, Grulkowski I, Walkowiak J. Automated bowel sound analysis: An overview. Sensors 2021; 21(16): 1–16.
• Liu J, Yin Y, Jiang H, Kan H, Zhang Z, Chen P, Zhu B, Wang Z. Bowel sound detection based on MFCC feature and LSTM neural network. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), October 17-19, 2018, Cleveland, OH, USA, pp. 1-4.
• Palaniappan R, Sundaraj K, Sundaraj S. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinformatics 2014; 15(1): 1–8.
• Nogueira DM, Ferreira CA, Gomes EF, Jorge AM. Classifying heart sounds using images of motifs, MFCC and temporal features. Journal of Medical Systems 2019; 43(6): 186–203.
• Kutlu Y, Camgozlu Y. Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks. Natural and Engineering Sciences 2021; 6(1): 60–74.
• Narin A, Isler Y. Detection of new coronavirus disease from chest x-ray images using pre-trained convolutional neural networks, Journal of the Faculty of Engineering and Architecture of Gazi University 2021; 36(4): 2095-2107.