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We investigated the potential of adding cardiac and respiratory activity information to actigraphy for sleep-wake staging. A dataset of 35 recordings with full polysomnography and actigraphy was used to assess the performance of an automated sleep/wake Bayesian classifier using electrocardiogram, inductance plethysmogram estimate of respiratory effort and actigraphy. The best performance was achieved...
Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection is proposed by analyzing the motor activity during sleep. Method: A total number of twelve patients have been...
In this paper, we present several implantable micro-devices targeted towards improving the efficacy of radiation therapy. Three micro-devices are discussed: a self-biased solid state dosimeter to be used for wireless monitoring of the delivered dose, an electromagnetic tracking system to locate the position of tumor in real-time, and a Guyton-chamber-embedded capacitive pressure sensor for wireless...
In this paper, we present a classification method of dermoscopy images between melanocytic skin lesions (MSLs) and non-melanocytic skin lesions (NoMSLs). The motivation of this research is to develop a pre-processor of an automated melanoma screening system. Since NoMSLs have a wide variety of shapes and their border is often ambiguous, we developed a new tumor area extraction algorithm to account...
To detect changes in gene expression data from DNA microarrays, a fixed threshold value is used in various studies. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for genes with low expression. To address this issue, we have proposed adaptive threshold, which has different values for different expression levels. In this study,...
A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected...
Most of the automatic seizure detection schemes reported in the literature are complex for detecting seizures that are of (a) short duration, (b) minimal amplitude evolution, or (c) non-rhythmic mixed frequency epileptic activity. We present a novel morphology-based classifier to detect epileptic seizures for intracranial EEG recording. The method characterizes epileptic seizure by detecting continual...
This paper is concerned with the classification of tumoral tissue in the small bowel by using capsule endoscopic images. The followed approach is based on texture classification. Texture descriptors are derived from selected scales of the Discrete Curvelet Transform (DCT). The goal is to take advantage of the high directional sensitivity of the DCT (16 directions) when compared with the Discrete Wavelet...
This paper presents a new method for performing supervised learning (classification) and demonstrates the technique by applying it to the detection of breast cancer from the dynamic information obtained in magnetic resonance imaging examinations. The proposed method is a vector machine similar to the established support vector machine (SVM) method, however, our method involves a reformulation of the...
We present a technique for automatic diagnosis of malignant melanoma based exclusively on local pattern analysis. The technique relies on local binary patterns in small sections in the image, and automatically selects the relevant texture features from those that discriminate best between benign and malignant skin lesions. The classification is performed using support vector machines, and the feature...
The cardiac magnetic resonance (CMR) images from a group of patients with myocardial scars and implanted cardioverter-defibrillator (ICD) are divided into a group with low risk of arrhythmias (late incidents) and a group with high risk of arrhythmias (early incidents). Several hundred quantitative features describing sizes, statistics and textures of the segmented and defined areas of the images are...
Developing countries have a large population of children living with undiagnosed heart murmurs. As a result of an accompanying skills shortage, most of these children will not get the necessary treatment. The objective of this paper was to develop a decision support system. This could enable health care providers in developing countries with tools to screen large amounts of children without the need...
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...
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...
Leprosy is an infectious disease caused by Mycobacterium Leprae, and generally compromises neural fibers, leading to the development of disabilities. These limit daily activities or social life. In leprosy, the study of disability considered functional (physical) and activity limitations; and social participation. These are measured respectively by EHF and SALSA scales; by and PARTICIPATION SCALE...
Breast cancer is the most commonly diagnosed form of cancer in women accounting for about 30% of all cases. From a computational point of view, breast cancer diagnosis can be viewed as a pattern classification problem. In this paper, we present a cost-sensitive approach to classifying breast cancer data. In particular, we employ a fuzzy rule base that allows incorporation of a misclassification cost...
This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter-...
In this paper, we present an algorithm to identify umbilical and uterine arteries from a set of four different maternal and fetal arteries using their Doppler signatures. To distinguish these arteries, we use 132 Doppler signals collected from pregnant women with gestational ages between 24 to 40 weeks. Initially we filter them to remove noise; spectrograms are generated to extract good cycles, which...
Several algorithms are available to quantify nystagmus beats in electro nystagmography (ENG) and video-oculography (VOG) recordings. These algorithms use parameterized approaches to detect the fast components of nystagmus beats. This paper proposes a wavelet approach to detect fast components of nystagmus beats. The main advantage of this approach compared to alternatives, is the completely unsupervised...
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...
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