Biomedical subjects

Inan Güler

Publications and source records attributed to Inan Güler.

36 records · Page 2Linked to original sources

PubMed2004

Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform.

In this study, short-time Fourier transform (STFT) and wavelet transform (WT) 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 spectral broadening in the presence of ophthalmic artery stenosis. A qualitative improvement in the appearance of the sonograms obtained using the WT over the STFT was noticeable. Despite the qualitative improvement in the individual sonograms, no quantitative advantage in using the WT over the STFT for the determination of spectral broadening index was obtained due to the poorer variance of the wavelet transform-based spectral broadening index and the additional computational requirements of the wavelet transform.

Adult↗

PubMed2004

The examination of the effects of obesity on a number of arteries and body mass index by using expert systems.

In this study, the areas affected from obesity were examined by classifying divergent arteries and body mass index (BMI) of 30 healthy persons and 52 obese persons by using expert systems, and the classifying performances of NEFCLASS and CANFIS, which are expert systems were compared. As a result of this comparison, it is observed that the classifying performance of NEFCLASS is better than that of CANFIS, and the causes of this are examined. Furthermore, it is observed that after these classifications, obesity affects the BMI rather than divergent arteries.

Arteries↗

PubMed2004

A neurofuzzy classification system for the effects of diabetes mellitus on ophtalmic artery.

Here, we present a classification system for the effects of diabetes mellitus (DM) on blood flow hemodynamics of the ophthalmic arteries by using neurofuzzy system. Blood flow hemodynamics were obtained from 80 ophthalmic arteries of 20 healthy persons and 20 patients with DM by using 7.5 MHz transducer and Doppler-M unit. Peak systole, peak diastole, resistive index (RI), pulsatile index (PI), and systole/diastole rate (SDR) were measured with the use of Doppler sonography. These values were applied to neurofuzzy system using NEFCLASS model. The performance of this classification system was examined with the application of the data obtained from Doppler analyses of the right and left ophthalmic arteries to the neurofuzzy system. After learning and testing processes, 85% success rates were reached from the data of right ophthalmic arteries, and 87.5% success rates were reached from the data of left ophthalmic arteries. Our findings suggest that neurofuzzy system may provide a successful classification system for the effects of DM on either right or left ophthalmic arteries with the application of Doppler signal parameters from carotid arteries to neurofuzzy system may produce a new and reliable classification system for diagnosing diameter stenosis.

Blood Flow Velocity↗

PubMed2004

Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system.

Cardiac Doppler signals recorded from mitral valve of 60 patients were transferred to a personal computer by using a 16-bit sound card. The power spectral density (PSD) was applied to the recorded signal from each patient. In order to do a good interpretation and rapid diagnosis, PSD values classified using multilayer perceptron (MLP) and neuro-fuzzy system. Our findings demonstrated that 93.33% classification success rate was obtained from MLP, 90% classification success rate was obtained from neuro-fuzzy system. The classification results show that MLP offers best results in the case of diagnosis.

Diagnosis, Computer-Assisted↗

PubMed2004

Multimedia based medical instrumentation course in biomedical engineering.

Computer assisted instruction in education, including biomedical engineering education, has been explored and changed dramatically for more than two decades. The Internet, with its capacity to transmit synchronous and asynchronous audio, text, and graphics, presents educators with tremendous opportunies for distance education and independent learning. In this work, we have developed a new educational hypermedia for medical instrumentation courses. It is designed to be suitable for biomedical and technical curricula where these courses are scheduled. The courseware provides support for the education of medical instrumentation. The work is presented herein to provide multimedia course material with animations to assist learning some key Medical Instrumentation topics on the World Wide Web.

Biomedical Engineering↗

PubMed2004

Classification of MCA stenosis in diabetes by MLP and RBF neural network.

For the classification of Middle Cerebral Artery (MCA) stenosis, Doppler signals have been received from the diabetes and control group by using 2 MHz Transcranial Doppler. After the Fast Fourier Transform (FFT) analyses of the Doppler signals, Power Spectrum Density (PSD) estimations have been made and Multilayer Perceptron (MLP) and Radial Basis Function (RBF) have been dealt to apply to the neural networks. PSD estimations of Doppler signals received from MCA of 104 subjects have been successfully classified by MLP (correct classification = 94.2%) and RBF (correct classification = 88.4%) neural network. As we have seen in the area under ROC curve (AUC), MLP neural network (AUC = 0.934) has classified more successfully when compared with RBF neural network (AUC = 0.873).

Brain↗

PubMed2004

Classification of the frequency of carotid artery stenosis with MLP and RBF neural networks in patients with coroner artery disease.

For the classification of left and right Internal Carotid Arteries (ICA) stenosis, Doppler signals have been received from the patients with coroner arteries stenosis by using 6.2-8.4 MHz linear transducer. To be able to classify the data obtained from LICA and RICA in artificial intelligence, MLP and RBF neural networks were used. The number of obstructed veins from the coroner angiography, intimal thickness, and plaque formation from the power Doppler US and resistive index values were used as the input data for the neural networks. Our findings demonstrated that 87.5% correct classification rate was obtained from MLP neural network and 80% correct classification rate was obtained from RBF neural network. MLP neural network has classified more successfully when compared with RBF neural network.

Artificial Intelligence↗

PubMed2004

A simple computer-based measurement and analysis system of pulmonary auscultation sounds.

Listening to various lung sounds has proven to be an important diagnostic tool for detecting and monitoring certain types of lung diseases. In this study a computer-based system has been designed for easy measurement and analysis of lung sound using the software package DasyLAB. The designed system presents the following features: it is able to digitally record the lung sounds which are captured with an electronic stethoscope plugged to a sound card on a portable computer, display the lung sound waveform for auscultation sites, record the lung sound into the ASCII format, acoustically reproduce the lung sound, edit and print the sound waveforms, display its time-expanded waveform, compute the Fast Fourier Transform (FFT), and display the power spectrum and spectrogram.

Algorithms↗

PubMed2003

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%.

Adolescent↗

PubMed2003

Application of classical and model-based spectral methods to ophthalmic arterial Doppler signals with uveitis disease.

In this study, Doppler signals recorded from ophthalmic artery of 75 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregressive, moving average, least-squares modified Yule-Walker autoregressive moving average methods) were selected for processing ophthalmic arterial Doppler signals with uveitis disease. Doppler power spectra of ophthalmic arterial Doppler signals were obtained by using these spectrum analysis techniques. 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 Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of uveitis disease.

Adolescent↗

PubMed2003

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.

Arterial Occlusive Diseases↗

PubMed2003

Determination of stenosis and occlusion in arteries with the application of FFT, AR, and ARMA methods.

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. Therefore, Doppler ultrasonography is known as reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, arterial Doppler signals recorded from 105 subjects were processed by PC-computer using fast Fourier transform, Burg autoregressive, and least squares modified Yule-Walker autoregressive moving average methods. Doppler power spectrums of arterial Doppler signals were obtained by using these spectrum analysis techniques. The variations in the shape of the Doppler power spectrums 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 stenosis and occlusion in arteries. Reliable information on hemodynamic alterations in arteries can be obtained by evaluation of these sonograms.

Blood Flow Velocity↗

PubMed2003

Classification of transcranial Doppler signals using artificial neural network.

Transcranial Doppler signals, recorded from the temporal region of brain on 110 patients were transferred to a personal computer by using a 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently can not offer a good spectral resolution at jet blood flows, it sometimes causes wrong interpretation of transcranial Doppler signals. To do a correct and rapid diagnosis, transcranial Doppler blood flow signals were statistically arranged so that they were classified in artificial neural network. Back propagation neural network and self-organization map algorithms of artificial neural network were used for training, whereas momentum and delta-bar-delta algorithms were used for learning. The results of these algorithms were compared in the case of classification and learning.

Adult↗

PubMed2003

Determination of coronary failure with the application of FFT and AR methods.

In this study, Doppler signals recorded from the output of carotid artery of 30 patients were transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the patients, and then analyzed using fast Fourier transform (FFT) and least squares autoregressive (AR) methods to obtain their sonograms. These sonograms are then used to compare with the applied methods in terms of medical evaluation.

Cardiac Output, Low↗

PubMed2003

Neurofuzzy classification of the effect of diabetes mellitus on carotid artery.

Here, we present a new clasification system for the effects of diabetes mellitus (DM) on carotid artery by using neurofuzzy system. Blood flow hemodynamics were obtained from 118 carotid arteries of 59 patients with DM by using 7.5 MHz transducer and Doppler-M unit. Vmax = peak systole, Vmin = end diastole, resistive index (RI), and pulsatile index (PI) were measured with the use of Doppler sonography. These values were applied to neurofuzzy system using NEFCLASS model. With the increase of epoch from 200 to 500 and pruning of fuzzy rules, our classification system was found to be successful in 85% of the cases. In 100 of 118 patients the classificitaion system was found to be correct. Our findings suggest that with the application of Doppler signal parameters from carotid arteries to neurofuzzy system may produce a new and reliable classification system for diagnosing diameter stenosis.

Algorithms↗

PubMed2002

Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm.

Doppler signals, recorded from the output of tricuspid, mitral, and aorta valves of 60 patients, were transferred to a personal computer via 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at highly turbulent blood flows, it sometimes leads to wrong interpretation of cardiac Doppler signals. In order to avoid this problem, firstly six known diseased heart signals such as hypertension, mitral stenosis, mitral failure, tricuspid stenosis, aorta stenosis, aorta insufficiency were introduced to fuzzy algorithm. Then, the unknown heart diseases from 15 patients were applied to the same fuzzy algorithm in order to detect the kinds of diseases. It is observed that the fuzzy algorithm gives true results for detecting the kind of diseases.

Algorithms↗

PubMed2002

Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels.

In this work, transcranial Doppler signals recorded from the temporal region of the brain on 35 patients were transferred to a personal computer by using a 16-bit sound card. Fast Fourier transform and adaptive auto regressive-moving average (A-ARMA) methods were applied to transcranial Doppler frequencies obtained from the middle cerebral artery in the temporal region. Spectral analyses were obtained to compare both methods for medical diagnoses. The sonograms obtained using A-ARMA method give better results for spectral resolution than the FFT method. The sonograms of A-ARMA method offer net envelope and better imaging, so that the determination of blood flow and brain pressure can be calculated more accurately. All diseases show higher resistance to flow than controls with no difference between males and females. Whereas values between disease classes differed, resistance within each class was remarkably constant.

Adolescent↗

PubMed2002

Determination of Behcet disease with the application of FFT and AR methods.

In this study, Doppler signals recorded from ophthalmic artery of 86 patients were processed by personal computer using fast Fourier transform, Burg autoregressive (AR), and least-squares AR methods. By using these spectrum analysis techniques, the variations in the shape of the Doppler spectrums 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 Behcet disease.

Adult↗