Nicos Maglaveras

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A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the(More)
OBJECTIVE The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system. METHODS Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects(More)
A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of(More)
The detection of ischemic cardiac beats from a patient’s electrocardiogram (ECG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. In the present paper, an algorithm is developed for this(More)
The most widely used signal in clinical practice is the ECG. ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important(More)
The ongoing efforts toward continuity of care and the recent advances in information and communication technologies have led to a number of successful personal health systems for the management of chronic care. These systems are mostly focused on monitoring efficiently the patient's medical status at home. This paper aims at extending home care services(More)
Heart Rate Variability (HRV) reflects the balance between sympathetic and parasympathetic activity. Slower HRV rhythms (LF) indicate increased sympathetic and/or lower vagal activity, wakefulness characteristics, while faster HRV rhythms (HF) indicate lower sympathetic and/or increased parasympathetic and vagal activity, sleepy characteristics. In this work(More)
In this paper we propose a combined scheme of linear prediction analysis for feature extraction along with linear projection methods for feature reduction followed by known pattern recognition methods on the purpose of discriminating between normal and pathological voice samples. Two different cases of speech under vocal fold pathology are examined: vocal(More)
HeartCycle is an integrated project aiming to provide a disease management solution for cardiovascular disease patients. The project develops technologies and services to facilitate the remote management of patients at home and motivate them to be compliant to treatment regimes and to adopt a beneficial lifestyle. HeartCycle aims to develop a personalised(More)
OBJECTIVES The pathogenetic mechanisms responsible for the initiation and recurrence of PAF are not fully elucidated and vary among individuals. We evaluated the ability of a novel non-invasive approach based on P wave wavelet analysis to predict symptomatic paroxysmal atrial fibrillation (PAF) recurrences in individuals without structural heart disease. (More)