Nizamettin Aydin

THE USE OF THE WAVELET TRANSFORM TO DESCRIBE EMBOLIC SIGNALS

A number of methods to detect cerebral emboli and differentiate them from artefacts using Doppler ultrasound have been described in the literature. In most, Fourier transform-based (FT) spectral analysis has been used. The FT is not ideally suited to analysis of short-duration embolic signals due to an inherent trade-off between temporal and frequency resolution. An alternative approach that might be expected to describe embolic signals well is the wavelet transform. Wavelets are ideally suited for the analysis of sudden short-duration signal changes. Therefore, we have implemented a wavelet-based analysis and compared the results of this with a conventional FFT-based analysis. The temporal resolution, as measured by the half-width maximum, was significantly better for the continuous wavelet transform (CWT), mean (SD) 8.40 (8.82) ms, compared with the 128-point FFT, 12.92 (9.70) ms, and 64-point FFT, 10.80 (5.69) ms. Time localization of the CWT for the embolic signal was also significantly better than the FFT. The wavelet transform appears well suited to the analysis of embolic signals offering superior time resolution and time localization to the FFT.

Embolic Doppler ultrasound signal detection using discrete wavelet transform

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.

Design and implementation considerations for an advanced wireless interface in miniaturized integrated sensor microsystems

Developments in system-on-chip and wireless technologies have led to complex electronic systems to be miniaturized to size of ingestible capsule and implantable microsystems. Inevitably such miniaturized complex systems impose some constraints on the case of an ingestible diagnostic capsule. It is desirable that system be wireless, programmable, and reusable. In this paper, we describe a wireless interface link developed for such an ingestible microsystem. It is programmable and directly controlled by the on-chip microcontroller. It is also suitable for developing complex communication protocols for conveying the data to a remote basestation. At the heart of the system lie a direct sequence spread spectrum encoder.

Quadrature-to-directional format conversion of Doppler signals using digital methods

Four possible quadrature-to-directional format conversion methods using digital techniques are described. These are the phasing-filter technique, the extended Weaver receiver technique, the Hilbert transform in the frequency domain, and the complex FFT. All methods are implemented to give separated time domain outputs as well as frequency domain outputs. The theoretical descriptions are verified by practical implementations. Each of the methods has been implemented in real-time using a commercially available digital signal processing board.

Implementation of directional Doppler techniques using a digital signal processor

Three methods of deriving directional signals from phase quadrature Doppler signals, using digital techniques, are described. These are the phasing-filter technique, the Weaver receiver technique and the complex FFT. The basic theory behind the three methods is presented, together with the results of digital simulations. Each of the methods has been implemented in real time using a commercially available digital signal-processing board, and their relative processing times are compared. All the methods work well, and the decision to implement one or other in a specific application is likely to rest on secondary factors, such as the need to tape-record the time domain output.

Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral Doppler ultrasound waveforms

Compensatory fuzzy neural networks (CFNN) without normalization, which can be trained with a backpropagation learning algorithm, are proposed as a pattern recognition technique for the intelligent detection of Doppler ultrasound waveforms of abnormal neonatal cerebral hemodynamics. Doppler ultrasound signals were recorded from the anterior cerebral arteries of 40 normal full-term babies and 14 mature babies with intracranial pathology. The features of normal and abnormal groups as inputs to the pattern recognition algorithms were extracted from the maximum-velocity waveforms by using principal component analysis. The proposed technique is compared with the CFNN with normalization and other pattern recognition techniques applied to Doppler ultrasound signals from various arteries. The results show that the proposed method is superior to the other techniques, and can be a powerful way to analyze Doppler ultrasound signals from various arteries.

Pulmonary crackle detection using time-frequency and time-scale analysis

Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders in auscultation. Crackles are very common adventitious transient sounds. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases may be assessed. In this study, a method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis from pulmonary signals. In order to understand the effect of using different window and wavelet types in time-frequency and time-scale analysis in detecting crackles, different windows and wavelets are tested such as Gaussian, Blackman, Hanning, Hamming, Bartlett, Triangular and Rectangular windows for time-frequency analysis and Morlet, Mexican Hat and Paul wavelets for time-scale analysis. The extracted feature sets, both individually and as an ensemble of networks, are fed into three different machine learning algorithms: Support Vector Machines, k-Nearest Neighbor and Multilayer Perceptron. Moreover, in order to improve the success of the model, prior to the time-frequency/scale analysis, frequency bands containing no-crackle information are removed using dual-tree complex wavelet transform, which is a shift invariant transform with limited redundancy compared to the conventional discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets, which are extracted using different window and wavelet types, for both pre-processed and non-pre-processed data with different machine learning algorithms, are extensively evaluated and compared.

Optimization of processing parameters for the analysis and detection of embolic signals

The fast Fourier transform (FFT), which is employed by all commercially available ultrasonic systems, provides a time-frequency representation of Doppler ultrasonic signals obtained from blood flow. The FFT assumes that the signal is stationary within the analysis window. However, the presence of short duration embolic signals invalidates this assumption. For optimal detection of embolic signals if FFT is used for signal processing, it is important that the FFT parameters such as window size, window type, and required overlap ratio should be optimized. The effect of varying window type, window size and window overlap ratio were investigated for both simulated embolic signals, and recorded from patients with carotid artery stenosis. An optimal compromise is the use of a Hamming or Hanning window with a FFT size of 64 (8.9 ms) or 128 (17.9 ms). A high overlap ratio should also be employed in order not to miss embolic events occurring at the edges of analysis windows. The degree of overlap required will depend on the FFT size. The minimum overlap should be 65% for a 64-point window and 80% for a 128-point window.

Directional wavelet transform in the context of complex quadrature Doppler signals

Most Doppler ultrasound systems employ quadrature demodulation techniques at the detection stage. The information concerning flow direction encoded in the phase relationship between in-phase and quadrature phase channels is not obvious at this stage. A method based on the utilization of complex wavelets and negative scales has been described. It eliminates the intermediate processing stages by mapping directional information in the scale domain.

Modified dual tree complex wavelet transform for processing quadrature signals

Abstract Dual-tree complex wavelet transform (DTCWT) is a shift invariant transform with limited redundancy. Complex quadrature signals are dual channel signals obtained from the systems employing quadrature demodulation. An example of such signals is quadrature Doppler signal obtained from blood flow analysis systems. Prior to processing Doppler signals using the DTCWT, directional flow signals must be obtained and then two separate DTCWT applied, increasing the computational complexity. In order to decrease computational complexity, a modified DTCWT algorithm is proposed. A comparison between the new transform and the phasing-filter technique is presented. The results show that the proposed method gives the same output as the phasing-filter method and the computational complexity for processing quadrature signals using DTCWT is greatly reduced.