Classification of transcranial Doppler signals using artificial neural network

Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease

Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.

Comparing methods for determining motor-hand lateralization based on fTCD signals

The lateralization index (LI) as determined from functional transcranial Doppler sonography (fTCD) can be used to determine the hemispheric organization of neural activation during a behavioral task. Previous studies have proposed different methods to determine this index, but to our knowledge no studies have compared the performance of these methods. In this study, we compare two established methods with a simpler method proposed here. The aim was to see whether similar results could be achieved with a simpler method and to give an indication of the analysis steps required to determine the LI. A simple unimanual motor task was performed while fTCD was acquired, and the LI determined by each of these methods was compared. In addition, LI determined by each method was related to behavioural output in the form of degree of handedness. The results suggest that although the methods differed in complexity, they yielded similar results when determining the lateralization of motor functions, and its correlation with behavior. Further investigation is needed to expand the conclusions of this preliminary study, however the new method proposed in the paper has great potential as it is much simpler than the more established methods yet yields similar results.

A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals

A transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage.

A survey on application of quantitative methods on analysis of brain parameters changing with temperature

Brain temperature fluctuations occur in consequence of physiological and pathophysiological conditions and indicate changes in brain metabolism, cerebral blood flow (CBF), brain functions and neural damage. Lowering the brain temperature of patients with traumatic brain injuries achieves considerable improvements. When the human brain is cooled down to 30°C, it switches to a sub functional regime where it can live longer with less oxygen, glucose and other supplies. Fluctuations in brain temperature cause changes in brain parameters which can be measured by electroencephalogram (EEG) and transcranial Doppler (TCD). It is very important to understand the temperature dependencies of brain's electrical activity and blood flow and their interrelations considering the good clinical results achieved by lowering the brain temperature of neurologically injured patients. Since protecting the patient's brain is of primary importance in many fields including cardiology, neurology, traumatology and anesthesia it can be clearly seen that this subject is very important. In this study, we survey the "state-of-the-art" in analysis of EEG and TCD brain parameters changing with temperature and present further research opportunities.

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.

Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels

Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.

Classification of transcranial Doppler signals using their chaotic invariant measures

In this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases.

Complex-valued wavelet artificial neural network for Doppler signals classifying

OBJECTIVE

In this paper, the new complex-valued wavelet artificial neural network (CVWANN) was proposed for classifying Doppler signals recorded from patients and healthy volunteers. CVWANN was implemented on four different structures (CVWANN-1, -2, -3 and -4).

MATERIALS AND METHODS

In this study, carotid arterial 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. In implemented structures in this paper, Haar wavelet and Mexican hat wavelet functions were used as real and imaginary parts of activation function on different sequence in hidden layer nodes. CVWANN-1, -2 -3 and -4 were implemented by using Haar-Haar, Mexican hat-Mexican hat, Haar-Mexican hat, Mexican hat-Haar as real-imaginary parts of activation function in hidden layer nodes, respectively.

RESULTS AND CONCLUSION

In contrast to CVWANN-2, which reached classification rates of 24.5%, CVWANN-1, -3 and -4 classified 40 healthy and 38 unhealthy subjects for both training and test phases with 100% correct classification rate using leave-one-out cross-validation. These networks have 100% sensitivity, 100% specifity and average detection rate is calculated as 100%. In addition, positive predictive value and negative predictive value were obtained as 100% for these networks. These results shown that CVWANN-1, -3 and -4 succeeded to classify Doppler signals. Moreover, training time and processing complexity were decreased considerable amount by using CVWANN-3. As conclusion, using of Mexican hat wavelet function in real and imaginary parts of hidden layer activation function (CVWANN-2) is not suitable for classifying healthy and unhealthy subjects with high accuracy rate. The cause of unsuitability (obtaining the poor results in CVWANN-2) is lack of harmony between type of activation function in hidden layer and type of input signals in neural network.

Analysis of ultraviolet absorption spectrum of Chinese herbal medicine-Cortex Fraxini by double ANN

A fast, accurate and convenient method for the simultaneous determination of multi-component in the Chinese herbal medicine was proposed by using ultraviolet absorption spectrum. In this method, dummy components were added to training sample, and a double artificial neural network (DANN) that has the function of high self-revision and self-simulation was used. Effect of other interference components could be eliminated by adjusting concentration of dummy components. Therefore, the accuracy of concentration prediction for multi-component in the complicated Chinese herbal medicine was improved. It has been realized that two effective components of Cortex Fraxini, aesculin and aesculetin, were simultaneously determined, without any separation. The predicted accuracy was 92% within the permitted relative errors. The measurement precisions of the aesculin and aesculetin were 0.37% and 1.5%, respectively.

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.

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.