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Convolutional neural networks for real-time epileptic seizure detection - 2018

Convolutional Neural Networks For Real-Time Epileptic Seizure Detection

Research Paper on Convolutional Neural Networks For Real-Time Epileptic Seizure Detection

Research Area:  Machine Learning

Abstract:

Epileptic seizures constitute a serious neurological condition for patients and, if untreated, considerably decrease their quality of life. Early and correct diagnosis by semiological seizure analysis provides the main approach to treat and improve the patients’ condition. To obtain reliable and quantifiable information, medical professionals perform seizure detection and subsequent analysis using expensive video-EEG systems in specialized epilepsy monitoring units. However, the detection of seizures, especially under difficult circumstances such as occlusion by the blanket or in the absence of predictive EEG patterns, is highly subjective and should therefore be supported by automated systems. In this work, we conjecture that features learned via a convolutional neural network provide the ability to distinctively detect seizures from video, and even allow our system to generalize to different seizure types. By comparing our method to the state of the art we show the superior performance of learned features for epileptic seizure detection.

Keywords:  
Convolutional Neural Networks
Epileptic Seizure Detection
EEG
Machine Learning
Deep Learning

Author(s) Name:  Felix Achilles, Federico Tombari, Vasileios Belagiannis, Anna Mira Loesch, Soheyl Noachtar and Nassir Navab

Journal name:  Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Conferrence name:  

Publisher name:  Taylor & Francis

DOI:  10.1080/21681163.2016.1141062

Volume Information: