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Motor imagery-based brain computer interface (BCI) technology has motor rehabilitation as one of its main fields of application. The use of a BCI as a neuroprosthetic for paralyzed limb motor restoration implies normally absence of muscle activity. It is still an open question whether residual motor activity in healthy individuals or in patients causes a bias in the modulation of a motor imagery-based...
The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric...
Gait analysis is important in diagnosing and evaluating certain neurological diseases such as Parkinson's disease (PD). In this paper, we show the ability of our wireless inertial sensor system to characterize gait abnormalities in PD. We obtain physical features of pitch, roll, and yaw rotations of the foot during walking, use principal component analysis (PCA) to select features, and use the support...
We propose a improved Gradient Vector Flow (iGVF) for active contour detection. The algorithm herein proposed allows to surpass the problems of the GVF, which occur in noisy images with cluttered background. We experimentally illustrate that the proposed modified version of the GVF algorithm has a better performance in noisy images. The main difference concerns the use of more robust and informative...
We propose a feature extraction method based on the Volterra autoregressive model's prediction power and the data's predictability for the EEG signals to automatically detect the epileptic EEG signals from the EEG recordings. The method of determining the embedding dimension based on nonlinear prediction is applied to choose the embedding dimension of the EEG data. The proposed feature extraction...
In X-ray guided bronchoscopy of peripheral pulmonary lesions, airways and nodules are hardly visible in X-ray images. Transbronchial biopsy of peripheral lesions is often carried out blindly, resulting in degraded diagnostic yield. One solution of this problem is to superimpose the lesions and airways segmented from preoperative 3D CT images onto 2D X-ray images. A feature-based 2D/3D registration...
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important tool for the diagnosis of epilepsy. In this study, an epileptic seizure classification method based on features of the Empirical Mode Decomposition (EMD) of EEG records is proposed. The Intrinsic Mode Functions (IMFs) of EEG records are first computed, and then several time and...
It is widely accepted and can be easily verified that any specific voxel in a class of brain single photon emission computed tomography (SPECT) volumes is of a univariate normal distribution. In this research, we conjecture that all the voxels in a class of SPECT volumes are also approximately of a multivariate normal (MVN) distribution from which in terms of the Bayes errors of statistics, an optimal...
Features are extracted from PET images employing exploratory matrix factorization techniques such as nonnegative matrix factorization (NMF). Appropriate features are fed into classifiers such as a support vector machine or a random forest tree classifier. An automatic feature extraction and classification is achieved with high classification rate which is robust and reliable and can help in an early...
This paper presents a new methodology of feature extraction of sleep and wake stages of a freely behaving rat based on Continuous Wavelet Transform (CWT). The automatic separation of those stages is very useful for experiments related to learning and memory consolidation since recent scientific evidence indicates that sleep is strongly involved with offline reprocessing of acquired information during...
Research in time-frequency distributions (TFDs) is limited in terms of their use of the available spatial domains and in their target applications. Most of the work up till now has been concentrated mainly on the t-f domain space. This work presents a detailed study about the ambiguity domain (AD), their resemblance in the t-f space and the significance of using such a representation. Further, a novel...
In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method...
An analog spike detector circuit is presented, which adaptively generates a threshold level for spike detection based on hard-thresholding. Operation of the circuit was tested not only with a neural signal obtained from real in-vivo recording from a live animal, but also with a large sinusoidal baseline variation intentionally added to examine the capability of the circuit to track baseline variations...
A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed. Full-night audio signals from sixty subjects were segmented into snore, noise and silence events using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% (92%) sensitivity (specificity) and produces an average of 2,000 snores per subject. A classification...
We compare the results given by different methods to reconstruct cortical sources activity in order to classify EEG in real time. Two motor imagery experiments were performed. The aim was to retrieve from 1-second windows of signal which motor imagery task the subjects were performing. The use of cortical activity reconstruction was compared to Laplacian filtering, which is often used in BCI. A recursive...
This paper presents a methodology for Obstructive Sleep Apnea (OSA) detection based on the HRV analysis, where as a measure of relevance PLS is used. Besides, two different combining approaches for the selection of the best set of contours are studied. Attained results can be oriented in research focused on finding alternative methods minimizing the HRV-derived parameters used for OSA diagnosing,...
This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows...
A context-based 3D interpolation technique is proposed to enhance the out-of plane resolution of 3D medical images. The proposed technique represents a new approach of aiding 3D interpolation and improving its performance by efficient use of domain knowledge about the anatomy, objects and imaging modalities. In the new approach a family of adaptive 3D interpolation filters are designed and conditioned...
A moment-based approach is proposed for texture analysis of medical images. The neighborhood of texture pixel is calculated by moments for texture feature extraction. After verified on Brodatz textures, the moment-based texture analysis method is applied on CT liver scan classification and prostate ultrasound segmentation. A support vector machine and a multi-channel active contour model are used...
Designing an effective classifier has been a challenging task in the previous methods proposed in the literature. In this paper, we apply a combination of feature selection algorithm and neural network classifier in order to recognize five types of white blood cells in the peripheral blood. For this purpose, first nucleus and cytoplasm are segmented using Gram-Schmidt method and snake algorithm, respectively;...
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