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Denoising Quadrature Doppler Signals from Bi-directional Flow Using the Wavelet Frame
, 2003
"... A novel approach was proposed to denoise quadrature Doppler signals from bi-directional blood flow using the wavelet frame and a soft-thresholding algorithm. A direction separation step was firstly carried out to avoid the phase distortion of quadrature Doppler signals, which is induced from the non ..."
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A novel approach was proposed to denoise quadrature Doppler signals from bi-directional blood flow using the wavelet frame and a soft-thresholding algorithm. A direction separation step was firstly carried out to avoid the phase distortion of quadrature Doppler signals, which is induced from the non-linear soft-thresholding processing. Then real parts of separated complex signals from the uni-directional flow were denoised independently. Finally quadrature Doppler signals from the bi-directional flow were reconstructed from the denoised separated signals. The approach has been applied to the simulated Doppler signals from a femoral artery. It is concluded from the experimental results that this method is practical for denoising quadrature Doppler signals.
Correspondence Time-Scale Removal of “Wall Thump ” in Doppler Ultrasound Signals: A Simulation Study
"... Abstract—A novel approach for blood and wall signal separation in Doppler ultrasound is proposed in the wavelet time-scale domain. The Doppler signals of different clutterto-blood ratios from a femoral artery were simulated and used in the experiments. Compared with the conventional high-pass filter ..."
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Abstract—A novel approach for blood and wall signal separation in Doppler ultrasound is proposed in the wavelet time-scale domain. The Doppler signals of different clutterto-blood ratios from a femoral artery were simulated and used in the experiments. Compared with the conventional high-pass filtering and the signal separation in the spectrogram, the new method delivers improved performance for “wall thump ” removal with minimal loss of low-flow signal. I.
Correspondence Doppler Ultrasound Spectral Enhancement Using the Gabor Transform-Based Spectral Subtraction
"... Abstract—Most of the important clinical indices of blood flow are estimated from the spectrograms of Doppler ultrasound (US) signals. Any noise may degrade the readability of the spectrogram and the precision of the clinical indices, so the spectral enhancement plays an important role in Doppler US ..."
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Abstract—Most of the important clinical indices of blood flow are estimated from the spectrograms of Doppler ultrasound (US) signals. Any noise may degrade the readability of the spectrogram and the precision of the clinical indices, so the spectral enhancement plays an important role in Doppler US signal processing. A new Doppler US spectral enhancement method is proposed in this paper and implemented in three main steps: the Gabor transform is used to compute the Gabor coefficients of a Doppler US signal, the spectral subtraction is performed on the magnitude of the Gabor coefficients, and the Gabor expansion with the spectral subtracted Gabor coefficients is used to reconstruct the denoised Doppler US signal. The different analysis and synthesis windows are examined in the Gabor transform and expansion. The signal-to-noise ratio (SNR) improvement together with the overall enhancement of spectrograms are examined on the simulated Doppler US signals from a femoral artery. The results show the denoising method based on the orthogonal-like Gabor expansion achieves the best denoising performance. The experiments on some clinical Doppler US signals from umbilical arteries confirm the superior denoising performance of the new method. I.
Correspondence Denoising Quadrature Doppler Signals from Bi-Directional Flow Using the Wavelet Frame
"... Abstract—A novel approach was proposed to denoise quadrature Doppler signals from bi-directional blood flow using the wavelet frame and a soft-thresholding algorithm. A direction separation step was carried out first to avoid the phase distortion of quadrature Doppler signals, which is induced from ..."
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Abstract—A novel approach was proposed to denoise quadrature Doppler signals from bi-directional blood flow using the wavelet frame and a soft-thresholding algorithm. A direction separation step was carried out first to avoid the phase distortion of quadrature Doppler signals, which is induced from the nonlinear, soft-thresholding processing. Then real parts of separated complex signals from the unidirectional flow were denoised independently. The quadrature Doppler signals from the bi-directional flow were reconstructed from the denoised separated signals. The approach has been applied to the simulated Doppler signals from a femoral artery. It is concluded from the experimental results that this method is practical for denoising quadrature Doppler signals. I.
EXTRACTION OF GASTRIC MYOELECTRIC ACTIVITY FROM FINGER PHOTOPLETHYSMOGRAPHIC SIGNAL
"... This paper is an experimental study to examine the possibility of extracting gastric myoelectric activity (GMA) from photoplethysmographic (PPG) signals. Diagnosing GMA is a clinically challenging task because of its invasive/cumbersome methods. It is known that the PPG consists of information relat ..."
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This paper is an experimental study to examine the possibility of extracting gastric myoelectric activity (GMA) from photoplethysmographic (PPG) signals. Diagnosing GMA is a clinically challenging task because of its invasive/cumbersome methods. It is known that the PPG consists of information related to heart rate, respiratory rate and phenomena. Here we take this thread further and see whether GMA can be extracted from PPG in a simpler way and without discomfort to the patients. Since PPG and GMA signals are nonstationary, we choose discrete wavelet transform (DWT) to separate the different frequency components. PPG and Electrogastrog--ram (EGG, a method of measuring GMA) signals were acquired simultaneously at the rate of 100 Hz from 8 healthy subjects for 30 minutes duration in fasting and postprandial states. Both the signals were decomposed using DWT up to the frequency range (0- 0.1) Hz. A lower frequency oscillation ( ≈ 0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GMA in both shape and frequency. Normalized cross-correlation technique was used for comparing the two signals. Cross-correlation values were found to be high (R ≥ 0.73, R = 1.0 indicates exact agreement) for all subjects without any significant change between fasting and postprandial states. The results suggest that there is a possibility of extracting gastric related information from PPG signals using appropriate signal processing techniques. In future this novel technique could be used as a diagnostic tool for gastrointestinal system disorders.
APPLICATION OF STFT AND WAVELET TRANSFORM TO OPHTHALMIC ARTERIAL DOPPLER SIGNALS
"... In this study, short-time Fourier transform and wavelet transform were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order t ..."
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In this study, short-time Fourier transform and wavelet transform were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of ophthalmic artery stenosis. Based on the results, performance characteristics of the wavelet transform have been found extremely valuable for spectral analysis of ophthalmic arterial Doppler signals obtained from healthy subjects and unhealthy subjects having artery stenosis.
THE UNIVERSITY OF CALGARY Improving Tracking Performance of PLL in High Dynamic Applications
, 2004
"... The Phase-locked loop (PLL) is used in GPS receivers to track an incoming signal and to provide accurate carrier phase measurements. However, the PLL tracking performance and measurement accuracy are affected by a number of factors, such as signal-to-noise power ratio, Doppler frequency shift, the G ..."
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The Phase-locked loop (PLL) is used in GPS receivers to track an incoming signal and to provide accurate carrier phase measurements. However, the PLL tracking performance and measurement accuracy are affected by a number of factors, such as signal-to-noise power ratio, Doppler frequency shift, the GPS receiver’s jitter caused by vibration, and the Allan deviation. Among these factors, the thermal noise and Doppler shift are the most predominant and have a large influence on the design of the PLL. In high dynamic situations, the conflict between improving PLL tracking performance and the ability to track the signal necessitates some compromises in PLL design. This thesis investigates the strategies to resolve this conflict. Three methods are investigated to improve PLL tracking performance in high dynamic applications: a Kalman filter-based tracking algorithm, application of a wavelet de-noising technique in PLL, and an adaptive bandwidth algorithm. The Kalman filter-based tracking algorithm makes use of a carrier phase dynamic model and a measurement from the output of the discriminator to estimate the phase difference between the incoming
CLASSIFICATION OF INTERNAL CAROTID ARTERIAL DOPPLER SIGNALS USING WAVELET-BASED NEURAL NETWORKS
"... Doppler ultrasound is a noninvasive technique which is widely used in medicine for the assessment of blood flow in vessels. Therefore, Doppler ultrasonography is known as a reliable technique, which demonstrates the flow characteristics and resistance of internal carotid arteries in stenosis and occ ..."
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Doppler ultrasound is a noninvasive technique which is widely used in medicine for the assessment of blood flow in vessels. Therefore, Doppler ultrasonography is known as a reliable technique, which demonstrates the flow characteristics and resistance of internal carotid arteries in stenosis and occlusion conditions. In this study, internal carotid arterial Doppler signals recorded from 130 subjects that 45 of them had suffered from internal carotid artery stenosis, 44 of them had suffered from internal carotid artery occlusion and the rest of them had been healthy subjects were classified using waveletbased neural network. Spectral analysis of internal carotid arterial Doppler signals was performed using wavelet transform for determining the neural network inputs. Multilayer perceptron neural network employing quick propagation training algorithm was used to detect internal carotid artery stenosis and occlusion. The network was trained, cross validated and tested with subject’s internal carotid arterial Doppler signals. The correct classification rate was 95.45 % for healthy subjects, 92.00 % for subjects having internal carotid artery stenosis and 95.65 % for subjects having internal carotid artery occlusion. The classification results showed that multilayer perceptron neural network employing quick propagation training algorithm was effective to detect internal carotid artery stenosis and occlusion. Key Words: Doppler ultrasound, Wavelet transform, Multilayer perceptron neural network, Internal carotid artery
Microembolic Signal Characterization Using Adaptive Chirplet Expansion
"... Abstract—The adaptive chirplet expansion (ACE) is proposed to characterize high-intensity, transient signals from circulating microemboli. The nonnegative adaptive spectrogram based on the ACE gives a compact representation of the microembolic signal (MES) in joint-time, frequency domain. The mean i ..."
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Abstract—The adaptive chirplet expansion (ACE) is proposed to characterize high-intensity, transient signals from circulating microemboli. The nonnegative adaptive spectrogram based on the ACE gives a compact representation of the microembolic signal (MES) in joint-time, frequency domain. The mean instantaneous power (MIP) and mean instantaneous frequency (MIF) of MES are estimated from the adaptive spectrogram. Then, several important characteristics of MES, such as embolus-to-blood ratio (EBR), half width maximum (HWM), and embolic signal onset (ESO), are computed from the MIP, and the frequency modulation is examined in the MIF. To validate the new method, we improved the simulation model of the audio Doppler ultrasound signal. Some MESs together with a Doppler ultrasound signal from carotid blood flow are simulated in the simulation study. As a comparison, the adaptive Gabor expansion (AGE) also is implemented on these simulated signals. The experimental results of the simulation study show that the new method, based on the ACE, outperforms the AGE-based method in MES characterization. The consistent conclusion has been confirmed by the clinical study on some clinical MESs. I.
Research Article Noise and Speckle Reduction in Doppler Blood Flow Spectrograms Using an Adaptive Pulse-Coupled Neural Network
, 2010
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A novel method, called adaptive pulse coupled neural network (AD-PCNN) using a two-stage denoising strategy, is proposed to reduce noise and speckle in the spectrograms of Dopp ..."
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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A novel method, called adaptive pulse coupled neural network (AD-PCNN) using a two-stage denoising strategy, is proposed to reduce noise and speckle in the spectrograms of Doppler blood flow signals. AD-PCNN contains an adaptive thresholding PCNN and a threshold decaying PCNN. Firstly, PCNN pulses based on the adaptive threshold filter a part of background noise in the spectrogram while isolating the remained noise and speckles. Subsequently, the speckles and noise of the denoised spectrogram are detected by the pulses generated through the threshold decaying PCNN and then are iteratively removed by the intensity variation to speckle or noise neurons. The relative root mean square (RRMS) error of the maximum frequency extracted from the AD-PCNN spectrogram of the simulated Doppler blood flow signals is decreased 25.2 % on average compared to that extracted from the MPWD (matching pursuit with Wigner Distribution) spectrogram, and the RRMS error of the AD-PCNN spectrogram is decreased 10.8 % on average compared to MPWD spectrogram. Experimental results of synthetic and clinical signals show that the proposed method is better than the MPWD in improving the accuracy of the spectrograms and their maximum frequency curves. 1.
