Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification
2015 ◽
Vol 25
(05)
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pp. 1550023
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Keyword(s):
The goal of this study is to provide a seizure detection algorithm that is relatively simple to implement on a microcontroller, so it can be used for an implantable closed loop stimulation device. We propose a set of 11 simple time domain and power bands features, computed from one intracranial EEG contact located in the seizure onset zone. The classification of the features is performed using a random forest classifier. Depending on the training datasets and the optimization preferences, the performance of the algorithm were: 93.84% mean sensitivity (100% median sensitivity), 3.03 s mean (1.75 s median) detection delays and 0.33/h mean (0.07/h median) false detections per hour.
2021 ◽
pp. 104407
2014 ◽
Vol 149
◽
pp. 118-129
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2015 ◽
Vol 187
(5)
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Keyword(s):
2014 ◽
Vol 5
(2)
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pp. 157-164
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Keyword(s):
2019 ◽
Vol 16
(4)
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pp. 1666-1673
2012 ◽
Vol 121
◽
pp. 93-107
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