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Statistical shape models (SSMs) have been used widely as a basis for segmenting and interpreting complex anatomical structures. The robustness of these models are sensitive to the registration procedures, i.e., establishment of a dense correspondence across a training data set. In this work, two SSMs based on the same training data set of scoliotic vertebrae, and registration procedures were compared...
The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG)...
Carotid plaques are the main cause of neurological symptoms due to distal embolization or flow reduction. An objective classification of such lesions into symptomatic or asymptomatic is crucial for optimal treatment planning. The paper proposes a diagnostic framework to tackle this problem which consists of image processing, plaque detection, feature extraction and classification using AdaBoost in...
This study investigates the effects of exposure to intermittent hypoxia on cardiovascular autonomic control and metabolic function in obese children with obstructive sleep apnea (OSA). Each subject underwent: (1) a polysomnography; (2) morning fasting blood samples and a subsequent FSIVGTT; (3) noninvasive measurement of respiration, arterial blood pressure, and heart rate during supine and standing...
The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been...
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...
In this paper, we present the design of an epilepticseizure detector. This circuit is part of an implantable device used to continuously record intracerebral electroencephalographic signals through subdural and depth electrodes. The implemented seizure detector is based on a detection algorithm validated in Matlab tools and the circuits were implemented using CMOS 0.18-μm process. The proposed system...
Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel...
The aim of this study was to explore the ability of several spectral entropies and disequilibrium measures to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 18 mild cognitive impairment (MCI) patients and 24 controls. The Shannon spectral entropy (SSE), Tsallis spectral entropy (TSE), and Rényi spectral entropy (RSE) were calculated from the normalized power spectral...
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,...
In this paper, an algorithm able to detect epilepsy seizure based on 3D accelerometers and with patient adaptation is presented. This algorithm is based on a Bayesian approach using hidden Markov models for statistical modelling of moves signals. A particular focus is set on the learning procedure and in particular on its initialisation to ensure a good learning and to avoid numerical instability...
The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors...
Ambulatory physiological monitoring devices benefit patients, medical staff and hospitals by allowing patients to return home with the devices for monitoring. The main problem associated with designing such devices is that of power consumption. Wireless communications and complex processing are generally part of such devices and are power hungry components. These problems are magnified when dealing...
Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work presented in this paper examines the effect...
To develop a method of extract magnocellular (M) and parvocellular (P) components from VEP, the nonlinear system identification method using pseudorandom binary sequence (PRBS) stimulation combined with swept parameter technique was examined. VEP elicited by achromatic sinusoidal grating reverse based on PRBS was recorded and their binary kernels were calculated as cross-correlation between PRBS and...
Objective and quantitative measurement is crucial in the definition of functional impairment and in the tracking of disease progress over time of patients affected by progressive pathologies, such as ataxia. A new experimental procedure for the quantitative description of upper limb movement and coordination analysis was developed by the integration of an optoelectronic system and a dedicated electronic...
Penicillin-induced focal epilepsy is a well-known model in epilepsy research. In this model, epileptic activity is generated by delivering penicillin focally to the cortex. The drug induces interictal electroencephalographic (EEG) spikes which evolve in time and may later change to ictal discharges. This paper proposes a method for automatic classification of these interictal epileptic spikes using...
Artificial pacemakers are one of the most widely-used implantable devices today, with millions implanted worldwide. The main purpose of an artificial pacemaker is to treat bradycardia, or slow heart beats, by pacing the atrium and ventricles at a faster rate. While the basic functionality of the device is fairly simple, there are many documented cases of death and injury due to device malfunctions...
AngioLab is a software tool developed within the GIMIAS framework and is part of a more ambitious pipeline for the integrated management of cerebral aneurysms. AngioLab currently includes three plug-ins: angio segmentation, angio morphology and stenting, as well as supports advanced rendering techniques for the visualization of virtual angiographies. In December 2009, 23 clinicians completed an evaluation...
Dealing with a chronic disease and, more specifically, with Diabetes Mellitus and other metabolic disorders, represents a great challenge for care givers, patients and the Health Care Systems as their treatment requires continuous medical care and patient self management. The engagement of patients in the adoption of healthy lifestyles with a positive impact in the progression of their diseases is...
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