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MSc Haniye Shayeste

Center for Medical Physics and Biomedical Engineering
Position: PHD Student

ORCID: 0000-0002-9706-7775
T +43 1 67761769997
haniye.shayeste@meduniwien.ac.at

Keywords

Electroencephalography; Epilepsy; Magnetic Resonance Spectroscopy

Research interests

My main research interest is to investigate the complexities of epilepsy from a multifaceted perspective. During my Master's thesis, I conducted a comprehensive analysis of epileptic seizures utilizing electroencephalogram (EEG) signals. My research focused on combining the recurrence and spatial information of brain signals with high temporal resolution EEGs, to better understand the complex nature of epileptic seizures. 


At present, I am engaged in research at the MR Center, where I am working to develop magnetic resonance (MR) imaging and spectroscopy in 7T scanners. My research is primarily focused on investigating the efficacy and benefits of this technology for patients suffering from epilepsy. Magnetic resonance spectroscopy (MRS) images can be utilized as a pre-surgical evaluation tool for patients, thereby facilitating a better understanding of the chemical characteristics of the epileptic zone and defining the seizure onset zones. The findings of this research have the potential to significantly improve the current understanding of epilepsy and provide a more effective approach for detecting and eliminating seizures.

Techniques, methods & infrastructure

7T MR scanners are used to take spectroscopic images of the Epileptic patients and then a pipeline including pre-processing, segmentation, and quantitative analysis are applied to the data during this research.

Selected publications

  1. Shayeste, H. and Asl, B.M. (2023) ‘Heterogeneous Recurrence Analysis of Imaged-EEG for Spatio-Temporal Epileptic Seizure Detection’, IEEE Journal of Biomedical and Health Informatics, 27(1), pp. 351–362. Available at: https://doi.org/10.1109/jbhi.2022.3208598. Available at: http://dx.doi.org/.
  2. Shayeste, H. and Asl, B.M. (2023) ‘Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG’, Biomedical Signal Processing and Control, 79, p. 104109. Available at: https://doi.org/10.1016/j.bspc.2022.104109. Available at: http://dx.doi.org/.