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In this paper we discuss an efficient methodology for the characterization of Microelectrode Recordings (MER) obtained during deep brain stimulation surgery for Parkinson's disease using Support Vector Machines and present the results of a preliminary study. The methodology is based in two algorithms: (1) an algorithm extracts multiple computational features from the microelectrode neurophysiology,...
This paper reports on the preparation and characterization of highly porous platinum electrodes for functional electrical stimulation. Thin-film platinum electrodes were roughened by electrochemical deposition of platinum-copper alloys and subsequent removal of copper using cyclic voltammetry (CV). Prepared samples were characterized by electrochemical impedance spectroscopies (EIS), CVs and long-term...
Functional motor impairment due to Parkinson's disease and other movement disorders are currently assessed with visual rating scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). These methods rely on the subjective judgment of a rater to assign scores representing the extent of impairment while subjects perform prescribed activities. We describe a new model-based framework that uses...
A new methodology to automatically extract features from mammograms and classify them is presented. It relies on a hybrid processing system that sequentially uses the discrete cosine transform (DCT) to obtain the high frequency component of the mammogram and then applies the Radon transform to the obtained DCT image in order to extract its directional features. The features are subsequently fed to...
This paper reports the brain activation patterns of five subjects who were abruptly awakened from microsleeps in a simulated automotive driving experiment. By comparing the BOLD signals between behavioral microsleep (BM), abrupt awakening (AA) and post-abrupt awakening (post-AA) stages, we observed that visual area, frontal cortex, limbic lobe manifested more intense activation during the AA stage...
Connectivity evaluations have been performed in a noninvasive manner by examining resting state fMRI alongside diffusion-weighted images (DWI). The spatial structures of coherent spontaneous BOLD fluctuations provided the most convincing preliminary evidence that the BOLD signal was predominantly of neuronal origin rather than non-neuronal, artifactual noise. In this study we have shown that in thalamocortical...
The global framework of this paper is the synchronization analysis in EEG recordings. Two main objectives are pursued: the evaluation of the synchronization estimation for lateralization purposes in epileptic EEGs and the evaluation of the effect of the preprocessing (artifact and noise cancelling by blind source separation, wavelet denoising and classification) on the synchronization analysis. We...
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into...
The quality of automated real-time critical care monitoring is impacted by the degree of signal artifact present in clinical data. This is further complicated when different clinical rules applied for disease detection require source data at different frequencies and different signal quality. This paper proposes a novel multidimensional framework based on service oriented architecture to support real-time...
Wireless capsule endoscopy (WCE) has been validated to be an important tool in the evaluation of gastrointestinal (GI) tract. Compared with traditional endoscope technologies, its non-invasiveness property meets with great favor of patients. However, from physician's point of view, WCE video suffers from low resolution, limited illumination, irregular movement, more importantly, imbalanced rate of...
The control of powered upper limb prostheses using the surface electromyogram (EMG) is an important clinical option for amputees. There have been considerable recent improvements in prosthetic hands, but these currently lack a control scheme that can decode movement intent from the EMG to exploit their mechanical dexterity. Pattern recognition based control has the potential to decode many classes...
Steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI) works on the basis that an attended stimulus shows an enhanced visual evoked response. By examining EEG power at the frequency of the dominant evoked response, we are able to determine which stimulus the subject is attending. However, due to the limited processing capability of human visual system, when presented with...
Co-adaptation between the human brain and computers is an important issue in brain-computer interface (BCI) research. However, most of the research has focused on the computer side of BCI, such as developing powerful machine-learning algorithms, while less research has focused on investigating how BCI users may optimally adapt. This paper assesses the influences of positive and negative visual feedback...
The creation of an automatic diabetic retinopathy screening system using retina cameras is currently receiving considerable interest in the medical imaging community. The detection of microaneurysms is a key element in this effort. In this work, we propose a new microaneurysms segmentation technique based on a novel application of the radon transform, which is able to identify these lesions without...
A wireless multichannel data acquisition system is being designed for ElectroEncephaloGraphy (EEG) recording. The system is based on a custom integrated circuit (ASIC) for signal conditioning, amplification and digitization and also on commercial components for RF transmission. It supports the RF transmission of a 32-channel EEG recording sampled at 1 kHz with a 12-bit resolution. The RF communication...
This paper describes an approach to improve the contrast and signal to noise ratio on ultrasound images. Images with sub-pixel lateral displacements were re-sampled using a hexagonal grid, registered and compounded. The resultant image was filtered using a hexagonal adaptive masking filter. This approach was evaluated with simulated images and real images from a breast phantom. The results show total...
Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary activities of daily life. Our approach is the development...
We propose a new methodology to model high-level descriptions of physical activities using multimodal sensor signals (ambulatory electrocardiogram (ECG) and accelerometer signals) obtained by a wearable wireless sensor network. We introduce a two-step strategy where the first step estimates likelihood scores over the low-level descriptions of physical activities such as walking or sitting directly...
Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used...
We propose a novel approach to calculate the conduction velocity (CV) of the uterine contraction bursts in magnetomyogram (MMG) signals measured using a multichannel SQUID array. For this purpose, we partition the sensor coordinates into four different quadrants and identify the contractile bursts using a previously proposed Hilbert-wavelet transform approach. If contractile burst is identified in...
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