Feature Extraction Using Time-Frequency Analysis for Monophonic-Polyphonic Wheeze Discrimination
Küçük Resim Yok
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The aim of this study is monophonic-polyphonic wheeze episode discrimination rather than the conventional wheeze (versus non-wheeze) episode detection. We used two different methods for feature extraction to discriminate monophonic and polyphonic wheeze episodes. One of the methods is based on frequency analysis and the other is based on time analysis. Frequency analysis based method uses ratios of quartile frequencies to exploit the difference in the power spectrum. Time analysis based method uses mean crossing irregularity to exploit the difference in periodicity in the time domain. Both methods are applied on the data before and after an image processing based preprocessing step. Calculated features are used in classification both individually and in combinations. Support vector machine, k-nearest neighbor and Naive Bayesian classifiers are adopted in leave-one-out scheme. A total of 121 monophonic and 110 polyphonic wheeze episodes are used in the experiments, where the best classification performances are 71.45% for time domain based features, 68.43% for frequency domain based features, and 75.78% for a combination of selected best features.
Açıklama
37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) -- AUG 25-29, 2015 -- Milan, ITALY
Anahtar Kelimeler
[No Keywords]
Kaynak
2015 37th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (Embc)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
