PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

THIS WEBPAGE IS DEPRECATED - please visit our new website

F. Achilles, F. Tombari, V. Belagiannis, A.M. Loesch, S. Noachtar, N. Navab
Convolutional neural networks for real-time epileptic seizure detection
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis, 2016 (bib)

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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif