M
Morteza Zabihi
Researcher at Tampere University of Technology
Publications - 26
Citations - 623
Morteza Zabihi is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Convolutional neural network & Support vector machine. The author has an hindex of 7, co-authored 24 publications receiving 400 citations. Previous affiliations of Morteza Zabihi include Qatar University.
Papers
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Proceedings ArticleDOI
Heart sound anomaly and quality detection using ensemble of neural networks without segmentation
TL;DR: The objective of this study is to develop an automatic classification method for anomaly and quality detection of PCG recordings without segmentation in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease.
Proceedings ArticleDOI
Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier
Morteza Zabihi,Ali Bahrami Rad,Aggelos K. Katsaggelos,Serkan Kiranyaz,Susanna Narkilahti,Moncef Gabbouj +5 more
TL;DR: This study is focused on detection of AF rhythm using hand-held ECG monitoring devices, in addition to three other classes: normal or sinus rhythm, other rhythms, and too noisy to analyze.
Journal ArticleDOI
Analysis of High-Dimensional Phase Space via Poincaré Section for Patient-Specific Seizure Detection
Morteza Zabihi,Serkan Kiranyaz,Ali Bahrami Rad,Aggelos K. Katsaggelos,Moncef Gabbouj,Turker Ince +5 more
TL;DR: A novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data.
Journal ArticleDOI
Automated patient-specific classification of long-term Electroencephalography
TL;DR: A novel systematic approach for patient-specific classification of long-term Electroencephalography (EEG) using multi-dimensional particle swarm optimization (MD PSO) and a morphological filter forms the final epileptic segments while filtering out the outliers in the form of classification noise.
Journal ArticleDOI
Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines
TL;DR: This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown and can be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.