An Emboli Detection System Based on Dual Tree Complex Wavelet Transform

Automated decision systems for emboli detection is a crucial need since it is being done by visual determination of experts which causes excess time consumption and subjectivity. This work presents an emboli detection system using various dimensionality reduction algorithms on Doppler ultrasound signals recorded from both forward and reverse flow of blood transformed via Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), and Dual Tree Complex Wavelet Transform (DTCWT). The combined forward and reverse DTCWT based features produced the highest performance when fed to SVMs classifier. As to compare dimensionality reduction algorithms, although PCA and LDA gave comparable accuracies, LDA has accomplished these accuracies only with two components due to its less than the number of classes' orthogonal projective directions limitation. SVMs yielded higher classification accuracies than k-NN with all considered dimensionality reduction methods since SVMs classifier is more robust to noise and irrelevant features. With the ability to localize well both in time and frequency, wavelet transform based extracted features gave higher overall classification accuracies than FFT with the more stable classifier SVMs. Additionally, DTCWT accuracies are higher with SVMs than those of DWT since it also has the ability of being shift-invariant.