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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...
Electrocardiographic T wave peak-to-end interval (TpTe) is one parameter of T wave morphology, which contains indicators for hypoglycaemia. This paper shows the corrected TpTe (TpTec) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in...
A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This...
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 describe methods for assessment of exercise quality using body-worn tri-axial accelerometers. We assess exercise quality by building a classifier that labels incorrect exercises. The incorrect performances are divided into a number of classes of errors as defined by a physical therapist. We focus on exercises commonly prescribed for knee osteoarthritis: standing hamstring curl, reverse...
Point-of-care diagnostic devices typically require six distinct qualities: they must deliver at least the same sensitivity and selectivity, and for a cost per assay no greater than that of today's central lab technologies, deliver results in a short period of time (<;15 min at GP; <;2h in hospital), be portable or at least small in scale, and require no or extremely little sample preparation...
High Frequency Oscillations (HFOs) in the EEG are a promising biomarker of epileptogenic tissue. Given that the visual marking of HFOs is highly time-consuming and subjective, automatic detectors are necessary. In this study, we present a novel automatic detector that detects HFOs by incorporating information of previously detected baselines. The detector was trained on 72 channels and tested on 278,...
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,...
In this paper, we present an automatic method for K-complexes detection based on features extraction and the use of fuzzy thresholds. The validity of our process was examined on the basis of two visual K-complexes scorings performed on 5 excerpts of 30 minutes. Results were investigated through all different sleep stages. The algorithm provides global true positive rates of 61.72% and 60.94%, respectively...
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...
An automatic Uni- or Multi-modal Intelligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic algorithm on motion data is extracting features as “log-sum” measures of discrete wavelet components...
This study aims to evaluate a variety of existing and novel fall detection algorithms, for a waist mounted accelerometer based system. Algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities...
In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation,...
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...
This study aims to investigate the measurement performance on different sensor deployment and to determine the optimal position for monitoring heart rate (HR) and respiration rate (RR) during sleep. Five identical sensor boards were deployed on different positions simultaneously during sleep to detect changes of applied pressure due to heart beating and breathing. One board was set beneath the pillow;...
In this paper a new non-invasive method for screening patients with obstructive sleep apnea (OSA) during wakefulness is proposed. Eight people with OSA and eight non-apneic individuals participated in this study. The tracheal breath sound was recorded in supine and upright positions during both nose and mouth breathing maneuvers. Spectral analysis of the respiratory sound signals showed the variation...
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...
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...
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...
The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. In this paper, we present the results of an investigation into appropriate features for artefact detection in the REACT ambulatory EEG system. The study focuses on EEG artefacts arising from head movement. The use of one generalised movement artefact class to detect movement artefacts is proposed. Temporal,...
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