A brief overview of the project. Who is involved. What it entails. Why it important. And how it will be achieved.
Thesis Title: Generalizability of an Unsupervised Deep Learning Algorithm in Detecting Burst Suppression in Pediatric EEG Data
HSLU MSc Student: Carlos Arzaga
Supervisors: Oliver Staubli, Prof. Dr. Mirko Birbaumer
Co-Supervisors: Prof. Dr. med Georgia Ramantani, Prof. Dr. Med Emanuela Keller
Internal Support: Dr. med Andrea Rüegger, Alex Lo Biundo Santo Pietro
External Support: Jenny Schmid, Marko Seric
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Assess the generalizability (geographical/domain) via an external validation of a pre-validated unsupervised ML algorithm for BS detection in pediatric patients.
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Improve BS detection by pairing the ML algorithm with a DL architecture.
- Identify the most effective unsupervised DL architecture for EEG data analysis.
- Self Organizing Maps (SOM)
- Autoencoder (AE)
- Artificial Neural Networks (ANN)
- Evaluate new architecture pairing(s).
- Identify the most effective unsupervised DL architecture for EEG data analysis.
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Investigate the agreement between human EEG annotators and the DL algorithm.
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Data: Retrospective collection of EEG data from pediatric patients in neurocritical care at the Universitäts-Kinderspital Zürich (KISPI).
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Data Preparation: Anonymization and preprocessing of EEG and clinical data.
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Data Labelling: Manual labeling of BS patterns by experienced EEG annotators.
- Dr. Med. Rüegger Andrea | Kinderspital Zürich
- Alex Lo Biundo Santo Pietro | Kinderspital Zürich
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Model Development: Iterative process in training and validation of unsupervised ML and DL models.
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Evaluation: Assessment of model performance using metrics like sensitivity, specificity, precision, AUROC, F1-score, NPV, MAE, and Cohen's kappa.