Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller
| Autor: | Joschka Boedecker, Matthias Dümpelmann, Andreas Schulze-Bonhage, Manuel Watter, Farrokh Manzouri, Simon Heller, Peter Woias, Manuel Blum, Maria Hügle |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Machine Learning (stat.ML) Electroencephalography Severe epilepsy Convolutional neural network Statistics - Applications 030218 nuclear medicine & medical imaging Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning medicine Applications (stat.AP) Sensitivity (control systems) medicine.diagnostic_test business.industry Detector Pattern recognition Power (physics) Microcontroller ComputingMethodologies_PATTERNRECOGNITION Feature (computer vision) Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | IJCNN |
| DOI: | 10.48550/arxiv.1806.04549 |
| Popis: | Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation. In this paper we present a convolutional neural network for the early detection of seizures from intracranial EEG signals, designed specifically for this purpose. In addition, we investigate approximations to comply with hardware limits while preserving accuracy. We compare our approach to three previously proposed convolutional neural networks and a feature-based SVM classifier with respect to detection accuracy, latency and computational needs. Evaluation is based on a comprehensive database with long-term EEG recordings. The proposed method outperforms the other detectors with a median sensitivity of 0.96, false detection rate of 10.1 per hour and median detection delay of 3.7 seconds, while being the only approach suited to be realized on a low power microcontroller due to its parsimonious use of computational and memory resources. Comment: Accepted at IJCNN 2018 |
| Databáze: | OpenAIRE |
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