Embolic Doppler Ultrasound Signal Detection Using Discrete Wavelet Transform

Abstract

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N = 100), artifact (N = 100) or Doppler speckle (DS) (N = 100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.

Stochastic Method for Non-Homogeneous Cycles Identification in Quasi-Periodic Signals

“…One of the most widely used techniques for identifying non-repetitive and/or periodic patterns or distortions has been the wavelet transform [4][5].This technique adapts a wavelet pattern to the characteristics of the signal distortion to be identified.This has been used for identification of epileptic spikes in electroencephalography (EEG) signal [6][7][8][9], to identify emboli in the blood flow signal [10][11][12][13], to identify arrhythmias in the ECG signal [14][15][16], for identifying flaws in industrial materials (metals, concrete, etc. )in the ultrasound signal [17][18][19], and many other scenarios.…”

Automatic Detection of Microemboli During Percutaneous Coronary Interventions

“…2,1 Applying the wavelet transform to filter a signal has two main advantages: wavelet techniques are applicable to non-stationary signals and offer a high time to frequency resolution. The applied multiresolution wavelet analysis with sub-band coding splits the frequency range of the considered Doppler recordings f = 0 to p in two parts: f low = 0 to p/2 by a low pass filter and f high = p/2 to p by a high pass filter.…”

“…2) of a time signal f(t) is defined as the sum over all time t of the signal multiplied by scaled and shifted versions of the wavelet function resulting in wavelet coefficients W, which are a function of the scale a and the translation b. After multiplication of each coefficient by the appropriately scaled and shifted wavelet the constituent wavelets of the original signal are obtained 1,17. …”

Comparative Study On Different Features And Classifiers For Emboli Signal Identification

“…By determining the frequency of embolus event, TCD can identify the conditions of the artery in which the blockage may affect the amount of blood flowing toward the brain [4]. Nevertheless, the detection of emboli based on TCD is still relying on human observer as a gold standard.…”

“…Linear Prediction Coefficient (LPC) and statistical features i.e. the combination of Measured Embolus-to-Blood Ratio (MEBR), Peak Embolus-to-Blood Ratio (PEBR), entropy, standard deviation and maximum peak as the feature extraction [4] [7]. In order to classify the embolus in the pattern matching process, various classifiers from the Nearest Neighbor (NN) family were used including k Nearest Neighbor (kNN) 8 , Fuzzy k-nearest neighbor (FkNN) [9], k nearest centroid neighbor (kNCN) [11], and Fuzzy-Based kNearest Centroid Neighbor (FkNCN) [12].…”