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.) in 2014 from the University of Guilan and her Master’s degree in Biomedical Engineering-bioelectric (M.Sc.) in 2022 from the University of Tehran. Since December 2025, she has been continuing her Ph.D. in Computer Science at the University Hospital Bonn/University of Bonn.
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.
Daryakenari, Nazila Ahmadi; Setarehdan, Seyed Kamaledin
Proceedings of the 32nd National and 10th International Iranian Conference on Biomedical Engineering (ICBME 2025), 2025.
@conference{nokey,
title = {EEG-based Schizophrenia Detection Using Spectral, Entropy, and Graph Connectivity Features with Machine Learning},
author = {Nazila Ahmadi Daryakenari and Seyed Kamaledin Setarehdan},
url = {https://www.researchgate.net/publication/398572103_EEG-based_Schizophrenia_Detection_Using_Spectral_Entropy_and_Graph_Connectivity_Features_with_Machine_Learning},
year = {2025},
date = {2025-11-20},
urldate = {2025-11-20},
publisher = {Proceedings of the 32nd National and 10th International Iranian Conference on Biomedical Engineering (ICBME 2025)},
abstract = {Schizophrenia is a serious mental disorder that changes the way people think, perceive, and manage daily life. Getting the diagnosis right is critical for proper treatment, but in practice it is often difficult. Current evaluations depend mostly on a clinician's judgment, and the overlap of symptoms with bipolar disorder or major depression makes the task even harder. EEG offers a safe and noninvasive way to study brain activity, yet no single EEG feature has been reliable enough to stand on its own. This makes it important to look at integrative approaches that bring together different aspects of brain dynamics. In this study, we analyzed EEG features to distinguish patients with schizophrenia from healthy controls. Spectral power was measured across δ, θ, α, β, and γ bands. Temporal irregularity was quantified with Multiscale Permutation Entropy (MPE), which to our knowledge represents the first application of MPE to EEG in schizophrenia. Functional connectivity was estimated with the weighted Phase Lag Index in θ, α, and β bands, followed by extraction of graph measures including global efficiency, clustering coefficient, characteristic path length, and mean strength. These features were used to train Random Forest, Multi-Layer Perceptron, and Support Vector Machine classifiers. Among the models, Random Forest achieved the most reliable performance, reaching 99.7% accuracy under stratified 5-fold validation and 99.6% under leave-one-subject-out validation. Feature analysis showed that connectivity in θ and α bands contributed most strongly to classification. Topographic maps of θ, α, and β activity also revealed regional group differences. Overall, the results suggest that combining spectral, entropy, and connectivity measures offers a promising framework for EEG-based detection of schizophrenia. Nevertheless, these findings are preliminary given the limited sample size (N=28), and replication in larger and more diverse cohorts is required before clinical translation.},
keywords = {},
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Schizophrenia is a serious mental disorder that changes the way people think, perceive, and manage daily life. Getting the diagnosis right is critical for proper treatment, but in practice it is often difficult. Current evaluations depend mostly on a clinician's judgment, and the overlap of symptoms with bipolar disorder or major depression makes the task even harder. EEG offers a safe and noninvasive way to study brain activity, yet no single EEG feature has been reliable enough to stand on its own. This makes it important to look at integrative approaches that bring together different aspects of brain dynamics. In this study, we analyzed EEG features to distinguish patients with schizophrenia from healthy controls. Spectral power was measured across δ, θ, α, β, and γ bands. Temporal irregularity was quantified with Multiscale Permutation Entropy (MPE), which to our knowledge represents the first application of MPE to EEG in schizophrenia. Functional connectivity was estimated with the weighted Phase Lag Index in θ, α, and β bands, followed by extraction of graph measures including global efficiency, clustering coefficient, characteristic path length, and mean strength. These features were used to train Random Forest, Multi-Layer Perceptron, and Support Vector Machine classifiers. Among the models, Random Forest achieved the most reliable performance, reaching 99.7% accuracy under stratified 5-fold validation and 99.6% under leave-one-subject-out validation. Feature analysis showed that connectivity in θ and α bands contributed most strongly to classification. Topographic maps of θ, α, and β activity also revealed regional group differences. Overall, the results suggest that combining spectral, entropy, and connectivity measures offers a promising framework for EEG-based detection of schizophrenia. Nevertheless, these findings are preliminary given the limited sample size (N=28), and replication in larger and more diverse cohorts is required before clinical translation.
2022
Daryakenari, Nazila Ahmadi; Setarehdan, Seyed Kamaledin
Conference: Proceedings of the 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME 2022), 2022.
@conference{nokey,
title = {Classification of Healthy People and Schizophrenics Using Time- Frequency Domain Features Extracted from Electroencephalogram Signals},
author = {Nazila Ahmadi Daryakenari and Seyed Kamaledin Setarehdan},
url = {https://www.researchgate.net/publication/398573114_Classification_of_Healthy_People_and_Schizophrenics_Using_Time-_Frequency_Domain_Features_Extracted_from_Electroencephalogram_Signals},
year = {2022},
date = {2022-12-23},
urldate = {2022-12-23},
publisher = {Conference: Proceedings of the 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME 2022)},
abstract = {Schizophrenia (SZ) is a chronic and complex mental disorder associated with neurobiological deficits. The complexity and heterogeneity of schizophrenia symptoms pose challenges for objective diagnosis, which is currently based on behavioral and clinical manifestations. Furthermore, other psychiatric disorders such as bipolar disorder or major depressive disorder are often misdiagnosed as schizophrenia. Consequently, manual screening through psychiatrist-patient interviews is not entirely reliable. This study aims to develop an automated SZ diagnosis scheme using electroencephalogram (EEG) signals as a complementary tool to assist psychiatrists. A novel method is proposed, utilizing features from time, frequency, and time-frequency domains to classify EEG signals from schizophrenia patients and healthy individuals. Time-domain features, frequency-domain features, as well as nonlinear and statistical features were extracted, and 10 feature combinations were selected based on importance using a hybrid mutual information and Sequential Forward Feature Selection approach. Classification was performed using K-nearest neighbors (KNN), weighted KNN, linear and nonlinear support vector machines (SVM) with radial basis function kernels, decision trees, linear discriminant analysis, and the naive Bayes method. Remarkably, three classifiers achieved 100% accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Schizophrenia (SZ) is a chronic and complex mental disorder associated with neurobiological deficits. The complexity and heterogeneity of schizophrenia symptoms pose challenges for objective diagnosis, which is currently based on behavioral and clinical manifestations. Furthermore, other psychiatric disorders such as bipolar disorder or major depressive disorder are often misdiagnosed as schizophrenia. Consequently, manual screening through psychiatrist-patient interviews is not entirely reliable. This study aims to develop an automated SZ diagnosis scheme using electroencephalogram (EEG) signals as a complementary tool to assist psychiatrists. A novel method is proposed, utilizing features from time, frequency, and time-frequency domains to classify EEG signals from schizophrenia patients and healthy individuals. Time-domain features, frequency-domain features, as well as nonlinear and statistical features were extracted, and 10 feature combinations were selected based on importance using a hybrid mutual information and Sequential Forward Feature Selection approach. Classification was performed using K-nearest neighbors (KNN), weighted KNN, linear and nonlinear support vector machines (SVM) with radial basis function kernels, decision trees, linear discriminant analysis, and the naive Bayes method. Remarkably, three classifiers achieved 100% accuracy.
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},
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pubstate = {published},
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