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Bayesian Networks provide a flexible way of incorporating different types of information into a single probabilistic model. In a medical setting, one can use these networks to create a patient model that incorporates lab test results, clinician observations, vital signs, and other forms of patient data. In this paper, we explore a simple Bayesian Network model of the cardiovascular system and evaluate...
Bayesian Networks provide a flexible way of incorporating different types of information into a single probabilistic model. In a medical setting, one can use these networks to create a patient model that incorporates lab test results, clinician observations, vital signs, and other forms of patient data. In this paper, we explore a simple Bayesian Network model of the cardiovascular system and evaluate...
Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT)...
Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT)...
Recent evidence indicates that pericardial fat may be a significant cardiovascular risk factor. Although pericardial fat is routinely imaged during computed tomography (CT) for coronary calcium scoring, it is currently ignored in the analysis of CT images. The primary reason for this is the absence of a tool capable of automatic quantification of pericardial fat. Recent studies on pericardial fat...
Recent evidence indicates that pericardial fat may be a significant cardiovascular risk factor. Although pericardial fat is routinely imaged during computed tomography (CT) for coronary calcium scoring, it is currently ignored in the analysis of CT images. The primary reason for this is the absence of a tool capable of automatic quantification of pericardial fat. Recent studies on pericardial fat...
Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population...
Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population...
A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a high-dimensional template warping technique is used to compute morphological and functional representations for each individual...
A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a high-dimensional template warping technique is used to compute morphological and functional representations for each individual...
Classification of breast lesions is clinically most relevant for breast radiologists and pathologists for early breast cancer detection. This task is not easy due to poor ultrasound resolution and large amount of patient data size. This paper proposes a five step novel and automatic methodology for breast lesion classification in 3-D ultrasound images. The first three steps yield an accurate segmentation...
Classification of breast lesions is clinically most relevant for breast radiologists and pathologists for early breast cancer detection. This task is not easy due to poor ultrasound resolution and large amount of patient data size. This paper proposes a five step novel and automatic methodology for breast lesion classification in 3-D ultrasound images. The first three steps yield an accurate segmentation...
Estimating head tissue conductivity for each layer is a high dimensional, non-linear and ill-posed problem which is part of Electrical Impedance Tomography (EIT) inverse problem. Traditional methods have many difficulties in resolving this problem. Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) is a new kind of learning method including Support Vector Classification (SVC)...
Estimating head tissue conductivity for each layer is a high dimensional, non-linear and ill-posed problem which is part of Electrical Impedance Tomography (EIT) inverse problem. Traditional methods have many difficulties in resolving this problem. Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) is a new kind of learning method including Support Vector Classification (SVC)...
We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic...
We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic...
Brain-computer interfaces (BCIs) aim to provide an alternative channel for paralyzed patients to communicate with external world. Reducing the time needed for the initial calibration is one important objective in P300 based BCI research. In this paper, the training time is reduced by a semi-supervised learning approach. A model is trained by small training set first. The on-line test data with predicted...
Brain-computer interfaces (BCIs) aim to provide an alternative channel for paralyzed patients to communicate with external world. Reducing the time needed for the initial calibration is one important objective in P300 based BCI research. In this paper, the training time is reduced by a semi-supervised learning approach. A model is trained by small training set first. The on-line test data with predicted...
This paper discusses the development of a four command BCI system. This system is composed of a wearable electroencephalogram acquisition unit interfaced to a computer by a wireless Bluetoothreg (BT) connection. The implemented system relies on the steady-state visual evoked potential (SSVEP) protocol applied to a four selection system. In order to achieve the maximum reliability against false positives...
This paper discusses the development of a four command BCI system. This system is composed of a wearable electroencephalogram acquisition unit interfaced to a computer by a wireless Bluetoothreg (BT) connection. The implemented system relies on the steady-state visual evoked potential (SSVEP) protocol applied to a four selection system. In order to achieve the maximum reliability against false positives...
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