Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral Doppler ultrasound waveforms. - Seker, H.
Compensatory fuzzy neural networks (CFNN) without normalization, which can be trained with a backpropagation learning algorithm, are proposed as a pattern recognition technique for the 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 the 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 other techniques, and can be a powerful way to analyze Doppler ultrasound signals from various arteries.
Keywords: Doppler ultrasound waveforms; Abnormal neonatal cerebral hemodynamics; Anterior cerebral arteries; Backpropagation learning algorithm; Blood-flow velocity; Compensatory fuzzy neural networks; Feature extraction; Full-term babies; Intelligent waveform detection; Intracranial pathology; Mature babies; Maximum-velocity waveforms; Normalization; Pattern recognition technique; Principal component analysis; Signal analysis