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

Wavelet Scattering Transform based Doppler signal classification

Normal blood supply to the human brain may be marred by the presence of a clot inside the blood vessels. This clot structure called emboli inhibits normal blood flow to the brain. It is considered as one of the main sources of stroke. Presence of emboli in human's can be determined by the analysis of transcranial Doppler signal. Different signal processing and machine learning algorithms have been used for classifying the detected signal as an emboli, Doppler speckle, and an artifact. In this paper, we sought to make use of the wavelet transform based algorithm called Wavelet Scattering Transform, which is translation invariant and stable to deformations for classifying different Doppler signals. With its architectural resemblance to Convolutional Neural Network, Wavelet Scattering Transform works well on small datasets and subsequently was trained on a dataset consisting of 300 Doppler signals. To check the effectiveness of extracted Scattering transform based features for Doppler signal classification, learning algorithms that included multi-class Support vector machine, k-nearest neighbor and Naive Bayes algorithms were trained. Comparative analysis was done with respect to the handcrafted Continuous wavelet transform features extracted from samples and Wavelet scattering with Support vector machine achieved an accuracy of 98.89%. Also, with set of extracted scattering coefficients, Gaussian process regression was performed and a regression model was trained on three different sets of scattering coefficients with zero order scattering coefficients providing least prediction loss of 34.95%.

A Scoping Review of Cerebral Doppler Arterial Waveforms in Infants

Cerebral Doppler ultrasound has been an important tool in pediatric diagnostics and prognostics for decades. Although the Doppler spectrum can provide detailed information on cerebral perfusion, the measured spectrum is often reduced to simple numerical parameters. To help pediatric clinicians recognize the visual characteristics of disease-associated Doppler spectra and identify possible areas for future research, a scoping review of primary studies on cerebral Doppler arterial waveforms in infants was performed. A systematic search in three online bibliographic databases yielded 4898 unique records. Among these, 179 studies included cerebral Doppler spectra for at least five infants below 1 y of age. The studies describe variations in the cerebral waveforms related to physiological changes (43%), pathology (62%) and medical interventions (40%). Characteristics were typically reported as resistance index (64%), peak systolic velocity (43%) or end-diastolic velocity (39%). Most studies focused on the anterior (59%) and middle (42%) cerebral arteries. Our review highlights the need for a more standardized terminology to describe cerebral velocity waveforms and for precise definitions of Doppler parameters. We provide a list of reporting variables that may facilitate unambiguous reports. Future studies may gain from combining multiple Doppler parameters to use more of the information encoded in the Doppler spectrum, investigating the full spectrum itself and using the possibilities for long-term monitoring with Doppler ultrasound.

A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation

A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.

Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases

This work investigates the performance of neuro-fuzzy system for analyzing and classifying EMG signals recorded from normal, neuropathy, and myopathy subjects. EMG signals were obtained from 177 subjects, 60 of them had suffered from neuropathy disorder, 60 of them had suffered from myopathy disorder, and rest of them had been normal. Coefficients that were obtained from the EMG signals using Autoregressive (AR) analysis was applied to neuro-fuzzy system. The classification performance of the feature sets was investigated for three classes.

The classification of obesity disease in logistic regression and neural network methods

The aim of this study is to establish an automated system to recognize and to follow-up obesity. In this study, the areas affected from obesity were examined with a classification considering the divergent arteries and body mass index of 30 healthy and 52 obese people by using two different mathematical models such as the traditional statistical method based on logistic regression and a multilayer perception (MLP) neural network, and then classifying performances of logistic regression and neural network were compared. As a result of this comparison, it is observed that the classifying performance of neural network is better than logistic regression; also the reasons of this result were examined. Furthermore, after these classifications it is observed that in obesity the body mass index is more affected than the divergent arteries.

A fuzzy clustering neural network architecture for classification of ECG arrhythmias

Accurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy clustering NN architecture (FCNN) for early diagnosis. The ECG signals are taken from MIT-BIH ECG database, which are used to classify 10 different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that a new proposed FCNN architecture can generalize better than ordinary MLP architecture and also learn better and faster. The advantage of proposed structure is a result of decreasing the number of segments by grouping similar segments in training data with fuzzy c-means clustering.

Examination of the Effects of Degeneration on Vertebral Artery by Using Neural Network in Cases With Cervical Spondylosis

The scope of this study is to diagnose vertebral arterial inefficiency by using Doppler measurements from both right and left vertebral arterials. Total of 96 patients' Doppler measurements, consisting of 42 of healthy, 30 of spondylosis, and 24 of clinically proven vertebrobasillary insufficiency (VBI), were examined. Patients' age and sex information as well as RPSN, RPSVN, LPSN, LPSVN, and TOTALVOL medical parameters obtained from vertebral arterials were classified by neural networks, and the performance of said classification reached up to 93.75% in healthy, 83.33% in spondylosis, and 97.22% in VBI cases. The area under ROC curve, which is a direct indication of repeating success ratio, is calculated as 92.3%, and the correlation coefficient of the classification groups is 0.9234. It is also demonstrated that those medical parameters of age and systolic velocity, which were applied into the neural networks, were more effective in developing vertebral deficiency.

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.

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, is proposed as a pattern recognition technique for 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 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 others, and can be a powerful technique to be used in analyzing Doppler ultrasound signals from various arteries.