Abstract
| Original language | English |
|---|---|
| Title of host publication | 2006 International Conference of the IEEE Engineering in Medicine and Biology Society |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Pages | 6343-6346 |
| ISBN (Print) | 1-4244-0032-5 |
| DOIs | |
| Publication status | Published - 2006 |
| MoE publication type | A4 Article in a conference publication |
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Keywords
- EEG
- Anesthesia
- Sevoflurane
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Method for the Automatic Detection of Epileptiform Waveforms in Sevoflurane-induced Anesthesia EEG. / Ermes, Miikka; Särkelä, Mika; van Gils, Mark; Vakkuri, Anne; Yli-Hankala, Arvi; Jäntti, Ville.
2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Institute of Electrical and Electronic Engineers , 2006. p. 6343-6346.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Method for the Automatic Detection of Epileptiform Waveforms in Sevoflurane-induced Anesthesia EEG
AU - Ermes, Miikka
AU - Särkelä, Mika
AU - van Gils, Mark
AU - Vakkuri, Anne
AU - Yli-Hankala, Arvi
AU - Jäntti, Ville
PY - 2006
Y1 - 2006
N2 - Sevoflurane is a volatile anesthetic which is reported to cause epileptiform EEG changes together with undesired symptoms such as convulsions. In this paper, an algorithm for the automatic detection of these EEG changes is presented which could enable safer induction of anesthesia with sevoflurane by informing the clinicians about the epileptiform EEG. EEG was recorded from 60 healthy female patients during sevoflurane anesthesia. A neurophysiologist classified the EEG waveforms. Each anesthesia period lasted 6 minutes. 48 signal features were extracted from the raw EEG. 5-sec segments of EEG were classified based on the extracted features using a decision tree with a logistic regression based decisions and the classification results were compared to the neurophysiologist's classifications. Awake EEG was recognized with 69% / 96% (sensitivity / specificity), Burst suppression with 56% / 98%, Epileptiform EEG with 83% / 87 %, normal slow anesthesia EEG with 86% / 64 %, slow anesthesia EEG with monophasic pattern with 65% / 80 %, and slow anesthesia EEG with monophasic pattern and spikes with 54% / 84%.
AB - Sevoflurane is a volatile anesthetic which is reported to cause epileptiform EEG changes together with undesired symptoms such as convulsions. In this paper, an algorithm for the automatic detection of these EEG changes is presented which could enable safer induction of anesthesia with sevoflurane by informing the clinicians about the epileptiform EEG. EEG was recorded from 60 healthy female patients during sevoflurane anesthesia. A neurophysiologist classified the EEG waveforms. Each anesthesia period lasted 6 minutes. 48 signal features were extracted from the raw EEG. 5-sec segments of EEG were classified based on the extracted features using a decision tree with a logistic regression based decisions and the classification results were compared to the neurophysiologist's classifications. Awake EEG was recognized with 69% / 96% (sensitivity / specificity), Burst suppression with 56% / 98%, Epileptiform EEG with 83% / 87 %, normal slow anesthesia EEG with 86% / 64 %, slow anesthesia EEG with monophasic pattern with 65% / 80 %, and slow anesthesia EEG with monophasic pattern and spikes with 54% / 84%.
KW - EEG
KW - Anesthesia
KW - Sevoflurane
U2 - 10.1109/IEMBS.2006.260442
DO - 10.1109/IEMBS.2006.260442
M3 - Conference article in proceedings
SN - 1-4244-0032-5
SP - 6343
EP - 6346
BT - 2006 International Conference of the IEEE Engineering in Medicine and Biology Society
PB - IEEE Institute of Electrical and Electronic Engineers
ER -