Application of autoregressive analysis to 20 MHz pulsed Doppler data in real time

A biomedical decision support system using LS-SVM classifier with an efficient and new parameter regularization procedure for diagnosis of heart valve diseases

Classification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and σ). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.

Diagnosis of valvular heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.

An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases

In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.

A decision support system based on support vector machines for diagnosis of the heart valve diseases

In this paper, a decision support system that classifies the Doppler signals of the heart valve to two classes (normal and abnormal) is presented to support the cardiologist. The paper uses our previous paper where ANN is used as a classifier, as feature extractor from measured Doppler signal. To make this, it uses wavelet transforms and short time Fourier transform methods. Before it classifies these features, it applies Wavelet entropy to them. In this paper, our aim is to develop our previous work by using least-squares support vector machine (LS-SVM) classifier instead of ANN. We use LS-SVM and backpropagation artificial neural network (BP-ANN) to classify the extracted features. In addition, we use receiver operator characteristic (ROC) curves to compare sensitivities and specificities of these classifiers and compute the area under the curves. Finally, we evaluate two classifiers in all aspects.

Order determination in autoregressive modeling of diastolic heart sounds

Previous studies demonstrated that spectral analysis of diastolic heart sounds may provide valuable information for the detection of coronary artery disease. Although parametric modeling methods were successfully used to achieve this goal, and showed considerable performance, the accuracy and precision of the analysis is strongly dependent on the model order selected. In order to investigate the effects of model order selection on the analysis, diastolic heart sound recorded from both normal and diseased patients were analyzed using the AR modeling, which is computationally the most efficient parametric spectral analysis method. The model order were determined by using four different model order selection criteria. The results showed that the four criteria yielded different order for the same data set. On the other hand, different criteria showed different performance in different measurement conditions. Effect of arbitrary order selection was also discussed. As a result, an optimal AR model order that may be used for every case was determined.

Comparison of FFT- and AR-based sonogram outputs of 20 MHz pulsed Doppler data in real time

Real time sonogram outputs of autoregressive (AR) and Fast Fourier Transform (FFT) spectral analysis of 20 MHz pulsed ultrasonic Doppler blood flowmeter are presented. Data obtained from coronary, renal, iliac, digital and mesenteric arteries were processed using AR- and FFT-based spectral analysis techniques and interpretable sonograms were constructed. In comparison with the FFT-based sonogram outputs. AR-based sonogram outputs for 20 MHz pulsed Doppler data provide better results. Hence, the AR modeling is strongly recommended for small vessels with diameters between 1 and 2 mm.