The closed-loop application of electrical stimulation via chronically implanted electrodes is a novel approach to stop seizures in patients with focal-onset epilepsy. To this end, an energy efficient seizure detector that can be implemented in an implantable device is of crucial importance. In this study, we first evaluated the performance of two machine learning algorithms (Random Forest classifier and support vector machine (SVM)) by using selected time and frequency domain features with a limited need of computational resources. Performance of the algorithms was further compared to a detection strategy implemented in an existing closed loop neurostimulation device for the treatment of epilepsy. The results show a superior performance of the Random Forest classifier compared to the SVM classifier and the reference approach. Next, we implemented the feature extraction and classification process of the Random Forest classifier on a microcontroller to evaluate the energy efficiency of this seizure detector. In conclusion, the feature set in combination with Random Forest classifier is an energy efficient hardware implementation that shows an improvement of detection sensitivity and specificity compared to the presently available closed-loop intervention in epilepsy while preserving a low detection delay.


    Access

    Download


    Export, share and cite



    Title :

    A comparison of machine learning classifiers for energy-efficient implementation of seizure detection



    Publication date :

    2018-01-01


    Remarks:

    Frontiers in systems neuroscience. - 12 (2018) , 43, ISSN: 1662-5137



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    006 / 600 / 629




    Comparison of Machine Learning Pixel-Based Classifiers for Detecting Archaeological Ceramics

    Argyro Argyrou / Athos Agapiou / Apostolos Papakonstantinou et al. | DOAJ | 2023

    Free access

    A comparison of machine learning classifiers for smartphone-based gait analysis

    Altilio, Rosa / Rossetti, Andrea / Fang, Qiang et al. | BASE | 2021

    Free access