Permutation Entropy for Discriminating ‘Conscious’ and ‘Unconscious’ State in General Anesthesia

Abstract : Brain-Computer Interfaces (BCIs) are devices offering alternative means of communication when conventional means are permanently, or nonpermanently, impaired. The latter is commonly induced in general anesthesia and is necessary for the conduction of the surgery. However, in some cases it is possible that the patient regains consciousness during surgery, but cannot directly communicate this to the anesthetist due to the induced muscle paralysis. Therefore, a BCI-based device that monitors the spontaneous brain activity and alerts the anesthetist is an essential addition to routine surgery. In this paper the use of Permutation Entropy (PE) as a feature for ‘conscious’ and ‘unconscious’ brain state classification for a BCI-based anesthesia monitor is investigated. PE is a linear complexity measure that tracks changes in spontaneous brain activity resulting from the administration of anesthetic agents. The overall classification performance for 10 subjects, as assessed with a linear Support Vector Machine, exceeds 95%, indicating that PE is an appropriate feature for such a monitoring device.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.280-288, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_32〉
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Nicoletta Nicolaou, Saverios Houris, Pandelitsa Alexandrou, Julius Georgiou. Permutation Entropy for Discriminating ‘Conscious’ and ‘Unconscious’ State in General Anesthesia. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.280-288, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_32〉. 〈hal-01571333〉

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