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Emina Alickovic
Researcher at Linköping University
Publications - 34
Citations - 1169
Emina Alickovic is an academic researcher from Linköping University. The author has contributed to research in topics: Computer science & Electroencephalography. The author has an hindex of 10, co-authored 28 publications receiving 710 citations. Previous affiliations of Emina Alickovic include University of Banja Luka & International Burch University.
Papers
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Journal ArticleDOI
Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction
TL;DR: A new model which is fully specified for automated seizure onset detection and seizure onset prediction based on electroencephalography (EEG) measurements is proposed which could outperform the state-of-the art models.
Journal ArticleDOI
Ensemble SVM Method for Automatic Sleep Stage Classification
TL;DR: Classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.
Journal ArticleDOI
Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier
TL;DR: Results demonstrate that the proposed Random Forests classifier has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).
Journal ArticleDOI
Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases
TL;DR: The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods, and the numerical results indicated the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling.
Book ChapterDOI
Diagnosis of Chronic Kidney Disease by Using Random Forest
TL;DR: A large number of patients diagnosed with chronic kidney disease do not have any known underlying cause of disease, and the prognosis is poor for those who do have a history of kidney disease.