Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Acoustic signal compression with wavelet packets
Wavelets: a tutorial in theory and applications
An automated feature extraction and emboli detection system based on the PCA and fuzzy sets
Computers in Biology and Medicine
Detection and estimation of embolic Doppler signals using discrete wavelet transform
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Adaptive AR and Neurofuzzy Approaches: Access to Cerebral Particle Signatures
IEEE Transactions on Information Technology in Biomedicine
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
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The transcranial Doppler (TCD) ultrasound technique is widely applied to detect emboli within the middle cerebral artery (MCA). However, due to the interference of certain artifacts including the probe motion and patient movement, etc. the detection results obtained with the TCD are always obscured. In traditional methods, the spectrogram analysis and/or the wavelet transform are performed to represent Doppler ultrasound signals and extract sensitive characteristics. Unfortunately these features do not have ideal specificity and sensitivity because of limitations on the time and frequency resolution and a lack of an adaptive classifier. In this paper, a new method based on the adaptive wavelet packet basis (AWPB) is used to make a sparse representation of Doppler ultrasound blood flow signals. Different from other dimensionality reduction methods, both the approximation coefficients and the decomposition scales are extracted to represent the signal and sent to the Takagi-Sugeno (T-S) neurofuzzy classifier. The T-S fuzzy inference system can easily combine the linguistic expert rules to make a more robust decision score to characterize emboli. Experiments show that within a 15% confidence interval of the decision score, 99.0% and 97.1% detection rates of emboli can be obtained for simulated and in vivo studies, respectively. The proposed AWPB method and neurofuzzy classification quite outperform traditional methods and are suitable to detect Doppler embolic signals with a high performance.