Heart sound signal classification using fast independent component analysis

KOÇYİĞİT Y.

Turkish Journal of Electrical Engineering and Computer Sciences, cilt.24, sa.4, ss.2949-2960, 2016 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 4
  • Basım Tarihi: 2016
  • Doi Numarası: 10.3906/elk-1409-123
  • Dergi Adı: Turkish Journal of Electrical Engineering and Computer Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.2949-2960
  • Anahtar Kelimeler: Heart sound classification, Independent component analysis, Linear discriminant analysis, Naive Bayes, Principal component analysis, Support vector machines, Wavelet transform
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

The analysis of heart sound signals is a basic method for heart examination. It may indicate the presence of heart disorders and provide clinical information in the diagnostic process. In this study, a novel feature dimension reduction method based on independent component analysis (ICA) has been proposed for the classification of fourteen different heart sound types; the method was compared with principal component analysis. The feature vectors are classified by support vector machines, linear discriminant analysis, and naive Bayes (NB) classifiers using 10-fold cross validation. The ICA combined with NB achieves the highest average performance with a sensitivity of 98.53%, specificity of 99.89%, g-means of 99.21%, and accuracy of 99.79%.