Feature extraction from Doppler ultrasound signals for automated diagnostic systems

This paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases.     

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.     

Wavelet-based neural network analysis of ophthalmic artery Doppler signals

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.     

Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals

The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the PNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the PNNs trained on these features achieved high classification accuracies.     

Detection of ophthalmic arterial doppler signals with Behcet disease using multilayer perceptron neural network

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 106 subjects, 54 of whom suffered from ocular Behcet disease while the rest were healthy subjects. Multilayer perceptron neural network (MLPNN) employing delta-bar-delta training algorithm was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by least squares (LS) autoregressive (AR) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 96.43% for healthy subjects and 93.75% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the MLPNN employing delta-bar-delta training algorithm was effective to detect the ophthalmic arterial Doppler signals with Behcet disease.     

Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the results confirmed that the proposed ANFIS classifiers have some potential in detecting the internal carotid artery stenosis and occlusion. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Comparison of eigenvector methods with classical and model-based methods in analysis of internal carotid arterial Doppler signals

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, internal carotid arterial Doppler signals recorded from 105 subjects were processed by PC-computer using classical, model-based, and eigenvector methods. The classical method (fast Fourier transform), two model-based methods (Burg autoregressive, least-squares modified Yule-Walker autoregressive moving average methods), and three eigenvector methods (Pisarenko, multiple signal classification, and Minimum-Norm methods) were selected for processing internal carotid arterial Doppler signals. Doppler power spectra of internal carotid arterial Doppler signals were obtained using these spectrum analysis techniques. The variations in the shape of the Doppler power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in internal carotid arteries.     

Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods

In this study, Doppler signals recorded from internal carotid artery of 45 subjects were processed by PC-computer using classical (fast Fourier transform) and model-based (autoregressive, moving average, autoregressive moving average (ARMA) methods) methods. Power spectral density estimates of internal carotid arterial Doppler signals were obtained using these spectral analysis methods. The variations in the shape of the Doppler power spectra as a function of time were presented in the form of sonograms in order to determine the degree of internal carotid artery stenosis. These Doppler power spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the impact on determining stenosis in internal carotid arteries. Based on the results, performance characteristics of the autoregressive and ARMA methods were found extremely valuable for spectral analysis of internal carotid arterial Doppler signals obtained from healthy subjects and unhealthy subjects having artery stenosis.     

Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.     

Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. In this study, ophthalmic artery Doppler signals were obtained from 105 subjects, 48 of whom had suffered from ophthalmic artery stenosis. A least-mean squares backpropagation neural network was used to detect the presence or absence of ophthalmic artery stenosis. Spectral analysis of ophthalmic artery Doppler signals was done by the Welch method for determining the neural network inputs. The network was trained, cross validated and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the neural network. Ophthalmic artery Doppler signals were classified with the accuracy varying from 88.9% to 90.6%.     

Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

In this study, carotid artery Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Doppler signals were processed using fast Fourier transform (FFT) with different window types, Hilbert transform and Welch methods. After these processes, Doppler signals were classified using complex-valued artificial neural network (CVANN). Effects of window types in classification were interpreted. Results for three methods and five window types (Bartlett, Blackman, Boxcar, Hamming, Hanning) were presented as comparatively. CVANN is a new technique for solving classification problems in Doppler signals. Furthermore, examining the effects of window types in addition to CVANN in this classification problem is also the first study in literature related with this subject. Results showed that CVANN, whose input data were processed by Welch method for each window types stated above, had classified all training and test patterns, which consist of 36 healthy, 34 unhealthy and four healthy, four unhealthy subjects, respectively, with 100% classification accuracy for both training and test phases.     

Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals

Cardiac auscultatory proficiency of physicians is crucial for accurate diagnosis of many heart diseases. Plenty of diverse abnormal heart sounds with identical main specifications and different details representing the ambient noise are indispensably needed to train, assess and improve the skills of medical students in recognizing and distinguishing the primary symptoms of the cardiac diseases. This paper proposes a versatile multiresolution wavelet-based algorithm to first extract the main statistical characteristics of three well-known heart valve disorders, namely the aortic insufficiency, the aortic stenosis, and the pulmonary stenosis sounds as well as the normal ones. An artificial neural network (ANN) and statistical classifier are then applied alternatively to choose proper exclusive features. Both classification approaches suggest using Daubechies wavelet filter with four vanishing moments within five decomposition levels for the most prominent distinction of the diseases. The proffered ANN is a multilayer perceptron structure with one hidden layer trained by a back-propagation algorithm (MLP-BP) and it elevates the percentage classification accuracy to 94.42. Ultimately, the corresponding main features are manipulated in wavelet domain so as to sequentially regenerate the individual counterparts of the underlying signals.