جستجوی مقالات مرتبط با کلیدواژه « Independent component analysis (ICA) » در نشریات گروه « پزشکی »

Independent Component Analysis (ICA) is the most common and standard technique used in functional neuroscience data analysis.  In this study, two of the significant functional brain techniques are introduced as a model for neuroscience data analysis. In this experimental and analytical study, Electroencephalography (EEG) signal and functional Magnetic Resonance Imaging (fMRI) were analyzed and managed by the developed tool. The introduced package combines Independent Component Analysis (ICA) to recognize significant dimensions of the data in neuroscience. This study combines EEG and fMRI in the same package for analysis and comparison results.  The findings of this study indicated the performance of the ICA, which can be dealt with the presented easy-to-use and learn intuitive toolbox. The user can deal with EEG and fMRI data in the same module. Thus, all outputs were analyzed and compared at the same time; the users can then import the neurofunctional datasets easily and select the desired portions of the functional biosignal for further processing using the ICA method.   A new toolbox and functional graphical user interface, running in cross-platform MATLAB, was presented and applied to biomedical engineering research centers.

Schizophrenia (SZ) is a mental illness that impairs a person's mental capacity, emotionaldispositions, and personal and social quality of life. Manual SZ patient screening is timeconsuming,expensive, and prone to human mistakes. As a result, a autonomous, relativelyaccurate, and reasonably economical system for diagnosing schizophrenia patients isrequired. Machine learning methods are capable of learning subtle hidden patterns fromhigh dimensional imaging data and achieve significant correlations for the classificationof Schizophrenia. In this study, the diverse types of symptoms of the affected person areselected which have the weights assigned by cross-correlations and the model classifiesthe probability of schizophrenia in the person based on the highest weighted symptomspresent in the report of the patient using machine learning classifiers. The classificationis made by various classifiers in which the Support Vector Machine (SVM) gives thebest result. In the neuroscience domain, it has been one of the most popular machinelearningtools. SVM with Radial Basis Function kernel helps to distinguish betweenpatients and healthy controls with significant accuracy of 76% without normalization andPrincipal Component Analysis (PCA). The K nearest neighbor’s algorithm also with nonormalization and PCA showed an accuracy of 73% in predicting SZ which is remarkablyclose to the SVM given the small size dataset.

Independent component analysis (ICA) has been used for detecting and removing the eye artifactsconventionally. However, in this research, it was used not only for detecting the eye artifacts, but also fordetecting the brain-produced signals of two conceptual danger and information category words. In thiscross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode.In the first part of this research, the statistical community test case included four men and four women,who were 25–30 years old. In the designed task, three groups of traffic signs were considered, in whichtwo groups referred to the concept of danger, and the third one referred to the concept of information. Inthe second part, the three volunteers, two men and one woman, who had the best results, were chosenfrom among eight participants. In the second designed task, direction arrows (up, down, left, and right)were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from theback of the head; however, in the thinking times, they were different. According to this result, alphawaves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and itwas at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger andinformation signals were well classified; these differences for the 5/8 volunteers were observed in theright hemisphere, and, for the other three volunteers, the differences were observed in the lefthemisphere. For the second task, simulations showed that the best classification accuracies resultedwhen the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neuralnetwork except hardlim discriminator function, the classification accuracies were almost the same andnot very different. Linear discriminant analysis (LDA) in comparison with the neural network yieldedhigher classification accuracies. ICA is a suitable algorithm for recognizing of the word’s concept and itsplace in the brain. Achieved results from this experiment were the same compared with the results fromother methods such as functional magnetic resonance imaging and methods based on the brain signals(EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy wasobtained by extracting the target signal from the output of the ICA and extracting the features ofcoefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classificationaccuracy more than 60%.

This study investigates electroencephalogram (EEG) signals in positive, negative and neutral emotion states.
It is assumed that the brain draws on several independent sources in any activity that are observable by independent component algorithm (ICA). To overcome the problem of ill-posedness of extracted components from ICA algorithm, first these sources are sorted out by Shannon entropy and then based on these sources, the features of trapping time and determinism of Recurrence Quantification Analysis (RQA) are extracted as representative of determination.
The results show that the degree of determinism of sorted sources related by emotions is significantly different over time and in three positive, negative and neutral states. The degree of determinism increases in neutral, positive and negative emotional states respectively.

Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant.
Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a better and more qualified extracted fetal ECG by using a novel approach.
In this paper, a PCA/ICA-based algorithm is proposed for extracting fetal ECG, and fetal R-peaks are detected as well. The method validates the quality of extracted ECGs and selects the best candidate fetal ECG to provide the required morphological ECG features such as fetal heart rate and RR interval for more clinical examinations. The method was evaluated using the dataset which was provided by PhysioNet/Computing in Cardiology Challenge 2013. The dataset consists of 75 recordings of 4-channel ECGs each containing 1-minute length for training and 100 similar recordings for testing.
When the proposed algorithm was applied to the test set, the scores of 85.853 bpm2 for fetal heart rate and an error of 9.725 ms RMS for fetal RR-interval estimation were obtained.
The results obtained with the mentioned algorithm shows the robustness of the research, and it is suggested to be used in practical fetal ECG monitoring systems.