N
Nicos Maglaveras
Researcher at Aristotle University of Thessaloniki
Publications - 292
Citations - 6128
Nicos Maglaveras is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Health care & Telemedicine. The author has an hindex of 29, co-authored 281 publications receiving 5027 citations. Previous affiliations of Nicos Maglaveras include Northwestern University & New York Academy of Sciences.
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Journal ArticleDOI
Machine Learning and Data Mining Methods in Diabetes Research.
Ioannis Kavakiotis,O. Tsave,Athanasios Salifoglou,Nicos Maglaveras,Ioannis Vlahavas,Ioanna Chouvarda +5 more
TL;DR: A systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular.
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Hybrid image segmentation using watersheds and fast region merging
TL;DR: A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds and additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced.
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Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents.
Christos Papadelis,Zhiyong Chen,Zhiyong Chen,Chrysoula Kourtidou-Papadeli,Panagiotis D. Bamidis,Ioanna Chouvarda,Evangelos Bekiaris,Nicos Maglaveras +7 more
TL;DR: Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.
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ECG pattern recognition and classification using non-linear transformations and neural networks: A review
Nicos Maglaveras,Telemachos Stamkopoulos,Konstantinos I. Diamantaras,Costas Pappas,Michael G. Strintzis +4 more
TL;DR: A generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques.
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ECG analysis using nonlinear PCA neural networks for ischemia detection
TL;DR: The NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact) and test results show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate.