1Research Scholar, Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India
2Associate Professor, Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India
3Research Scholar, Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India
4Vigan’s Lara Institute of Technology and Science, Andhra Pradesh
Online published on 21 September, 2018.
The survival rate of preterm infants has been increased by the development in treatment methods, still premature birth increases globally. These infants are at risk of medical condition that affects the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. Better understanding of preterm birth can be utilized for the prevention of such birth. This paper explores the significance of entropy features and classification using machine learning approach. Classification of spectral energy entropy feature using Elman neural network shows better result with specificity 99.85%, sensitivity 99.92% and accuracy of 99.8875%.
Preterm birth, entropy, energy entropy, spectral entropy, spectral energy entropy, Elman neural network, Butterworth filter
