An efficient system for online training of a seizure detection model-Epilepsy seizure detection

Abstract

This thesis presents the advantages of a flexible, scalable machine learning system that can be merged into cloud systems. The ones capable of offering computer system resources over the internet. The main objective of the thesis consists of providing to Neuro Event Labs a solution to generate machine learning classifier models in a short execution time, capable of increasing the accuracy over existing company baseline model. The developed solution is capable of training a binary classifier models with different param- eters and is able to handle large amounts of input data. The creation of a new binary classifier models is done by running a single model training script. The results of creating a new classifier training implementation show a reduced execution time when creating a model by optimizing the processing of the data and automating the generation of a model. Comparing the current model used by the company against the new binary classifier model shows improvement. This improvement consists of reduction of the amount of false-positive events across several patients. The work of this thesis demonstrates that an adaptable system for training binary classifiers reduces the amount of training time and generates stable models by reducing the number of false-positive epilepsy seizure detection events.