Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral Doppler ultrasound waveforms.
Compensatory fuzzy neural networks (CFNN) without normalization, which can be trained with a backpropagation learning algorithm, is proposed as a pattern recognition technique for intelligent detection of Doppler ultrasound waveforms of abnormal neonatal cerebral hemodynamics. Doppler ultrasound signals were recorded from the anterior cerebral arteries of 40 normal full-term babies and 14 mature babies with intracranial pathology. The features of normal and abnormal groups as inputs to pattern recognition algorithms were extracted from the maximum velocity waveforms by using principal component analysis. The proposed technique is compared with the CFNN with normalization and other pattern recognition techniques applied to Doppler ultrasound signals from various arteries. The results show that the proposed method is superior to the others, and can be a powerful technique to be used in analyzing Doppler ultrasound signals from various arteries.
Seker
H
H
Biomedical Computing Research Group, School of Mathematical and Information Sciences, Coventry University, UK. h.seker@coventry.ac.uk
Evans
D H
DH
Aydin
N
N
Yazgan
E
E
D000465:Algorithms; D001921:Brain / Q000002:abnormalities* / Q000098:blood supply; D002536:Cerebral Arteries / Q000000981:diagnostic imaging; D002560:Cerebrovascular Circulation; D004453:Echoencephalography / Q000379:methods* / Q000706:statistics & numerical data; D017143:Fuzzy Logic; D006801:Humans; D007231:Infant, Newborn; D016571:Neural Networks, Computer