Classification of Pathological and Healthy Voice Using Perceptual Wavelet Packet Decomposition and Support Vector Machine
Yükleniyor...
Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, a new approach has been presented based on perceptual wavelet packet transform and support vector machines for analysis and classification of pathological and healthy voice signals. Feature extraction and development of classification algorithm play important role in the area of automatic classification of pathological and healthy voice signals. The critical sub-bands are obtained by separating pathological and healthy voice signals with perceptual wavelet packet trans- form. The energy and entropy measures are extracted at each sub-bands used for training and testing of the classifier. In the study, the VIOCED database are used and it consists of 208 voice signals which are 58 healthy and 150 pathological. Experimental results demonstrate that the proposed features and classification algorithm provide 93.1% sensitivity, 96.5% specificity and 97.1% accuracy rates and it shows that the proposed method can be used to help medical professionals for diagnosing pathological status of a voice signal. © 2020 IEEE.
Açıklama
2020 Medical Technologies Congress, TIPTEKNO 2020 -- 19 November 2020 through 20 November 2020 -- -- 166140
Anahtar Kelimeler
energy, entropy, perceptual wavelet packet, Biomedical engineering, Classification (of information), Diagnosis, Support vector machines, Wavelet analysis, Wavelet decomposition, Automatic classification, Classification algorithm, Energy and entropies, Medical professionals, New approaches, Training and testing, Wavelet Packet Decomposition, Wavelet packet transforms, Biomedical signal processing
Kaynak
TIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020
WoS Q Değeri
N/A
