An emboli detection system based on Dual Tree Complex Wavelet Transform and ensemble learning

• Published in: Applied soft computing 2015-12, Vol.37, p.87-94
• Main Authors: Serbes, Gorkem, Sakar, Betul Erdogdu, Gulcur, Halil Ozcan, Aydin, Nizamettin
• Format: Article
• Language: English
• Subjects: Dimensionality reduction; Dual Tree Complex Wavelet Transform; Embolic signals; Ensemble learning; Stacked generalization; Support vector machines
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.08.015

•Embolic signals are used for the identification of active embolic sources in stroke-prone individuals.•Dual Tree Complex Wavelet Transform (DTCWT) is used as a new feature extractor from forward and reverse Doppler ultrasound signals.•The features acquired from forward and reverse flow directions of the blood are fed into k-NN and SVMs.•The individual predictions of classifiers are combined using ensemble stacking method considering that the forward and reverse blood flow coefficients carry different characteristics.•The results show that the DTCWT is superior to the DWT and FFT. The traditional visual and acoustic embolic signal detection methods based on the expert analysis of individual spectral recordings and Doppler shift sounds are the gold standards. However, these types of detection methods are high-cost, subjective, and can only be applied by experts. In order to overcome these drawbacks, computer based automated embolic detection systems which employ spectral properties of emboli, speckle, and artifact using Fourier and Wavelet Transforms have been proposed. In this study, we propose a fast, accurate, and robust automated emboli detection system based on the Dual Tree Complex Wavelet Transform (DTCWT). Employing the DTCWT, which does not suffer from the lack of shift invariance property of ordinary Discrete Wavelet Transform (DWT), increases the robustness of the coefficients extracted from the Doppler ultrasound signals. In this study, a Doppler ultrasound dataset including 100 samples from each embolic, Doppler speckle, and artifact signal is used. Each sample obtained from forward and reverse blood flow directions is represented by 1024 points. In our method, we first extract the forward and reverse blood flow coefficients separately using DTCWT from the samples. Then dimensionality reduction is applied to each set of coefficients and both of the reduced set of coefficients are fed to classifiers individually. Subsequently, in the view that the forward and reverse blood flow coefficients carry different characteristics, the individual predictors of these classifiers are combined using ensemble stacking method. We compare the obtained results with Fast Fourier Transform and DWT based emboli detection systems, and show that the features extracted using DTCWT give the highest accuracy and emboli detection rate. It is also observed that combining forward and reverse coefficients using stacking ensemble method improves the emboli and artifact dete