Nazila Ahmadi Daryakenari, M. Sc.
Personalized Digital Health and Telemedicine
Affiliation:
Department for Epileptology
University Hospital Bonn
Medical Faculty
University of Bonn
Location:
Venusberg-Campus 1,
Building 74, Room 2G-015
53127 Bonn, Germany
Telephone: +4915207271820
Email: Nazila.AhmadiDaryakenari@ukbonn.de
Short CV: Nazila Ahmadi Daryakenari earned her Bachelor’s degree in Electrical Engineering (B.Sc.) from the University of Guilan and her Master’s degree in Biomedical Engineering-bioelectric (M.Sc.) from the University of Tehran. Since December 2025, she is a PhD Researcher in Computer Science at the University of Bonn and University Hospital Bonn, specializing in artificial intelligence for healthcare. Her research focuses on secure and trustworthy AI for real-time seizure detection using multimodal physiological signals. By combining machine learning, biomedical signal processing, and neuroscience, she develops clinically relevant AI systems that are efficient, interpretable, and suitable for real-world healthcare applications. Her broader interests include clinical AI, explainable AI, multimodal learning, and neurological disorders. She is a member of IEEE, IEEE Engineering in Medicine and Biology Society (EMBS), and IEEE Young Professionals.
Publications
2025
Barzegar, Mohammad Mehdi; Daryakenari, Nazila Ahmadi; Khodatars, Marjane
Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model Journal Article
In: Journal of Research and Health, vol. 15, no. 6, 2025.
@article{Barzegar2025,
title = {Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model},
author = {Mohammad Mehdi Barzegar and Nazila Ahmadi Daryakenari and Marjane Khodatars},
url = {http://jrh.gmu.ac.ir/article-1-2987-en.html},
doi = {10.32598/JRH.15.SP.2892.1},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Research and Health},
volume = {15},
number = {6},
abstract = {Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.
2021
Daryakenari, Nazila Ahmadi
A Review of Underwater Image Enhancement Methods Journal Article
In: Peak Journal, published by the Iran Scientific Student Electrical Engineering Organization, vol. 10, pp. 30, 2021.
@article{,
title = {A Review of Underwater Image Enhancement Methods},
author = {Nazila Ahmadi Daryakenari },
year = {2021},
date = {2021-11-17},
urldate = {2021-11-17},
journal = {Peak Journal, published by the Iran Scientific Student Electrical Engineering Organization},
volume = {10},
pages = {30},
publisher = {Conference: Proceedings of the 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME 2022)},
address = {https://www.researchgate.net/publication/398573114_Classification_of_Healthy_People_and_Schizophrenics_Using_Time-_Frequency_Domain_Features_Extracted_from_Electroencephalogram_Signals},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
