Halil Ozcan Gulcur

Time–frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time–frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time–frequency analysis of PCG signals.

Analysis of event-related potentials (ERP) by damped sinusoids

Abstract. Several researchers propose that event-related potentials (ERPs) can be explained by a superposition of transient oscillations at certain frequency bands in response to external or internal events. The transient nature of the ERP is more suitable to be modelled as a sum of damped sinusoids. These damped sinusoids can be completely characterized by four sets of parameters, namely the amplitude, the damping coefficient, the phase and the frequency. The Prony method is used to estimate these parameters. In this study, the long-latency auditory-evoked potentials (AEP) and the auditory oddball responses (P300) of 10 healthy subjects are analysed by this method. It is shown that the original waveforms can be reconstructed by summing a small number of damped sinusoids. This allows for a parsimonious representation of the ERPs. Furthermore, the method shows that the oddball target responses contain higher amplitude, slower delta and slower damped theta components than those of the AEPs. With this technique, we show that the differentiation of sensory and cognitive potentials are not inherent in their overall frequency content but in their frequency components at certain bands.

Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain–machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

Feature extraction from mammographic mass shapes and development of a mammogram database

Breast cancer is one of the most common malignancies in women and a rare malignancy in men. Women who are diagnosed at an early stage can survive this often deadly disease. Mammography provides the best screening modality for detecting early breast cancer, even before a lesion is palpable. Because of the malignant mass pathology, the shape of the mammographic mass can be used to discriminate between malignant and benign masses. In this study the use, of shape features to classify breast masses has been investigated and a classification scheme has been developed to classify masses as either benign or malignant. A mammogram database designed to store the images of the masses, calculated shape descriptor parameters and some additional data, such as patient history, category of the mass and biopsy report if performed which are required in BI-RADS is also introduced. A touch on memory system has been used as a tool that permits access to the electronic patient record in the mammogram database. The software is written in Delphi and runs on Windows operation systems.

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.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time–frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time–frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

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

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 detection rates, and overall accuracy.

Directional dual-tree complex wavelet packet transform

Doppler ultrasound systems, which are widely used in cardiovascular disorders detection, have quadrature format outputs. Various types of algorithms were described in literature to process quadrature Doppler signals (QDS), such as phasing filter technique (PFT), fast Fourier transform method, frequency domain Hilbert transform method and complex continuous wavelet transform. However for the discrete wavelet transform (DWT) case, which becomes a common method for processing QDSs, there was not a direct method to recover flow direction from quadrature signals. Traditionally, to process QDSs with DWT, firstly directional signals have to be extracted and later two DWTs must be applied. Although there exists a complex DWT algorithm called dual tree complex discrete wavelet transform (DTCWT), it does not provide directional signal decoding during analysis because of the unwanted energy leaks into its negative frequency bands. Modified DTCWT, which is a combination of PFT and DTCWT, has the capability of extracting directional information while decomposing QDSs into different frequency bands, but it uses an additional Hilbert transform filter and it increases the computational complexity of whole transform. Discrete wavelet packet transform (DWPT), which is a generalization of the ordinary DWT allowing subband analysis without the constraint of dyadic decomposition, can perform an adaptive decomposition of the frequency axis. In this study, a novel complex DWPT, which maps directional information while processing QDSs, is proposed. The success of proposed method will be measured by using simulated quadrature signals.

An RBF approach to single trial VEP estimation

The problem of extracting a single trial visual evoked potential signal, buried in the ongoing EEG activity and measurement noise has been investigated. A method for detecting the stimulus related part of the brain activity resulting from visual flash stimulation is presented. A mixed approach, based on neural networks, non-linear auto regressive moving average (NARMA) modeling which combines gradient radial basis functions (GRBF) and orthogonal forward regressions (OFR) is used. The hidden node at each GRBF node detects and reacts to the gradient of the observed data in order to counter the level and trend of the time series. In this way, the non-stationary and non-linear nature of the problem is accounted for and the proposed neural networks predictive ability is improved.

Directional dual-tree rational-dilation complex wavelet transform

Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.