The Strength of Alpha Oscillations in the Electroencephalogram Differently Affects Algorithms Used for Anesthesia Monitoring

Intraoperative patient monitoring using the electroencephalogram (EEG) can help to adequately adjust the anesthetic level. Therefore, the processed EEG (pEEG) provides the anesthesiologist with the estimated anesthesia level. The commonly used approaches track the changes from a fast- and a low-ampl...

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Veröffentlicht in:Anesthesia and analgesia 2021-12, Vol.133 (6), p.1577-1587
Hauptverfasser: Weyer, Clara, Prötzl, Eva, Kinateder, Thomas, Nowak, Fabian, Husemann, Cornelius, Hautmann, Hubert, Kratzer, Stephan, Schneider, Gerhard, Kreuzer, Matthias
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container_end_page 1587
container_issue 6
container_start_page 1577
container_title Anesthesia and analgesia
container_volume 133
creator Weyer, Clara
Prötzl, Eva
Kinateder, Thomas
Nowak, Fabian
Husemann, Cornelius
Hautmann, Hubert
Kratzer, Stephan
Schneider, Gerhard
Kreuzer, Matthias
description Intraoperative patient monitoring using the electroencephalogram (EEG) can help to adequately adjust the anesthetic level. Therefore, the processed EEG (pEEG) provides the anesthesiologist with the estimated anesthesia level. The commonly used approaches track the changes from a fast- and a low-amplitude EEG during wakefulness to a slow- and a high-amplitude EEG under general anesthesia. However, besides these changes, another EEG feature, a strong oscillatory activity in the alpha band (8-12 Hz), develops in the frontal EEG. Strong alpha-band activity during general anesthesia seems to reflect an appropriate anesthetic level for certain anesthetics, but the way the common pEEG approaches react to changes in the alpha-band activity is not well explained. Hence, we investigated the impact of an artificial alpha-band modulation on pEEG approaches used in anesthesia research. We performed our analyses based on 30 seconds of simulated sedation (n = 25) EEG, simulated anesthesia (n = 25) EEG, and EEG episodes from 20 patients extracted from a steady state that showed a clearly identifiable alpha peak in the density spectral array (DSA) and a state entropy (GE Healthcare) around 50, indicative of adequate anesthesia. From these traces, we isolated the alpha activity by band-pass filtering (8-12 Hz) and added this alpha activity to or subtracted it from the signals in a stepwise manner. For each of the original and modified signals, the following pEEG values were calculated: (1) spectral edge frequency (SEF95), (2) beta ratio, (3) spectral entropy (SpEntr), (4) approximate entropy (ApEn), and (5) permutation entropy (PeEn). The pEEG approaches showed different reactions to the alpha-band modification that depended on the data set and the amplification step. The beta ratio and PeEn decreased with increasing alpha activity for all data sets, indicating a deepening of anesthesia. The other pEEG approaches behaved nonuniformly. SEF95, SpEntr, and ApEn decreased with increasing alpha for the simulated anesthesia data (arousal) but decreased for simulated sedation. For the patient EEG, ApEn indicated an arousal, and SEF95 and SpEntr showed a nonuniform change. Changes in the alpha-band activity lead to different reactions for different pEEG approaches. Hence, the presence of strong oscillatory alpha activity that reflects an adequate level of anesthesia may be interpreted differently, by an either increasing (arousal) or decreasing (deepening) pEEG value. This could co
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Therefore, the processed EEG (pEEG) provides the anesthesiologist with the estimated anesthesia level. The commonly used approaches track the changes from a fast- and a low-amplitude EEG during wakefulness to a slow- and a high-amplitude EEG under general anesthesia. However, besides these changes, another EEG feature, a strong oscillatory activity in the alpha band (8-12 Hz), develops in the frontal EEG. Strong alpha-band activity during general anesthesia seems to reflect an appropriate anesthetic level for certain anesthetics, but the way the common pEEG approaches react to changes in the alpha-band activity is not well explained. Hence, we investigated the impact of an artificial alpha-band modulation on pEEG approaches used in anesthesia research. We performed our analyses based on 30 seconds of simulated sedation (n = 25) EEG, simulated anesthesia (n = 25) EEG, and EEG episodes from 20 patients extracted from a steady state that showed a clearly identifiable alpha peak in the density spectral array (DSA) and a state entropy (GE Healthcare) around 50, indicative of adequate anesthesia. From these traces, we isolated the alpha activity by band-pass filtering (8-12 Hz) and added this alpha activity to or subtracted it from the signals in a stepwise manner. For each of the original and modified signals, the following pEEG values were calculated: (1) spectral edge frequency (SEF95), (2) beta ratio, (3) spectral entropy (SpEntr), (4) approximate entropy (ApEn), and (5) permutation entropy (PeEn). The pEEG approaches showed different reactions to the alpha-band modification that depended on the data set and the amplification step. The beta ratio and PeEn decreased with increasing alpha activity for all data sets, indicating a deepening of anesthesia. The other pEEG approaches behaved nonuniformly. SEF95, SpEntr, and ApEn decreased with increasing alpha for the simulated anesthesia data (arousal) but decreased for simulated sedation. For the patient EEG, ApEn indicated an arousal, and SEF95 and SpEntr showed a nonuniform change. Changes in the alpha-band activity lead to different reactions for different pEEG approaches. Hence, the presence of strong oscillatory alpha activity that reflects an adequate level of anesthesia may be interpreted differently, by an either increasing (arousal) or decreasing (deepening) pEEG value. 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We performed our analyses based on 30 seconds of simulated sedation (n = 25) EEG, simulated anesthesia (n = 25) EEG, and EEG episodes from 20 patients extracted from a steady state that showed a clearly identifiable alpha peak in the density spectral array (DSA) and a state entropy (GE Healthcare) around 50, indicative of adequate anesthesia. From these traces, we isolated the alpha activity by band-pass filtering (8-12 Hz) and added this alpha activity to or subtracted it from the signals in a stepwise manner. For each of the original and modified signals, the following pEEG values were calculated: (1) spectral edge frequency (SEF95), (2) beta ratio, (3) spectral entropy (SpEntr), (4) approximate entropy (ApEn), and (5) permutation entropy (PeEn). The pEEG approaches showed different reactions to the alpha-band modification that depended on the data set and the amplification step. The beta ratio and PeEn decreased with increasing alpha activity for all data sets, indicating a deepening of anesthesia. The other pEEG approaches behaved nonuniformly. SEF95, SpEntr, and ApEn decreased with increasing alpha for the simulated anesthesia data (arousal) but decreased for simulated sedation. For the patient EEG, ApEn indicated an arousal, and SEF95 and SpEntr showed a nonuniform change. Changes in the alpha-band activity lead to different reactions for different pEEG approaches. Hence, the presence of strong oscillatory alpha activity that reflects an adequate level of anesthesia may be interpreted differently, by an either increasing (arousal) or decreasing (deepening) pEEG value. This could complicate anesthesia navigation and prevent the adjustment to an adequate, alpha-dominant anesthesia level, when titrating by the pEEG values.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Alpha Rhythm - drug effects</subject><subject>Anesthesia</subject><subject>Anesthesia, General</subject><subject>Bronchoscopy</subject><subject>Computer Simulation</subject><subject>Electroencephalography - drug effects</subject><subject>Female</subject><subject>Humans</subject><subject>Intraoperative Neurophysiological Monitoring - methods</subject><subject>Male</subject><issn>0003-2999</issn><issn>1526-7598</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdUE1v1DAQtRCILoV_gJCPXFL8Ecf2MSpbQCr0QHuO3GS8MTjxYntVVeLHM9WWD2Fp5Hmj9549j5DXnJ1xweW7_sv2jP1zlGbtE7LhSnSNVtY8JRucykZYa0_Ii1K-IeTMdM_JiWxVK4XUG_Lzegb6tWZYd3WmydM-7mdHr8oYYnQ1pLXQsNKKrG2EseYE6whIiWmX3ULfB-8B1TXe0x7bsRa02KUc6rwUelNgoj5l2q9Q0KQERz-nNVQkrLuX5Jl3scCrx_uU3Fxsr88_NpdXHz6d95fNKB-2MUa4VhtrhRHWA580194pKxTvJuM1Qyic7pyXRpuW6UkzBVYz02pg6laekrdH331OPw74kWEJZQRccIV0KINQWrFO29YgtT1Sx5xKyeCHfQ6Ly_cDZ8ND7gPmPvyfO8rePL5wuF1g-iP6HfRf37sUK-TyPR7uIA8zuFjno5-SthFMcC4QNFiik78AVJuNaw</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Weyer, Clara</creator><creator>Prötzl, Eva</creator><creator>Kinateder, Thomas</creator><creator>Nowak, Fabian</creator><creator>Husemann, Cornelius</creator><creator>Hautmann, Hubert</creator><creator>Kratzer, Stephan</creator><creator>Schneider, Gerhard</creator><creator>Kreuzer, Matthias</creator><general>Lippincott Williams &amp; Wilkin</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20211201</creationdate><title>The Strength of Alpha Oscillations in the Electroencephalogram Differently Affects Algorithms Used for Anesthesia Monitoring</title><author>Weyer, Clara ; Prötzl, Eva ; Kinateder, Thomas ; Nowak, Fabian ; Husemann, Cornelius ; Hautmann, Hubert ; Kratzer, Stephan ; Schneider, Gerhard ; Kreuzer, Matthias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3526-882a478992829fe1d717fa592516d8f7017f2a76af3878407d705e970847e05b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Alpha Rhythm - drug effects</topic><topic>Anesthesia</topic><topic>Anesthesia, General</topic><topic>Bronchoscopy</topic><topic>Computer Simulation</topic><topic>Electroencephalography - drug effects</topic><topic>Female</topic><topic>Humans</topic><topic>Intraoperative Neurophysiological Monitoring - methods</topic><topic>Male</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weyer, Clara</creatorcontrib><creatorcontrib>Prötzl, Eva</creatorcontrib><creatorcontrib>Kinateder, Thomas</creatorcontrib><creatorcontrib>Nowak, Fabian</creatorcontrib><creatorcontrib>Husemann, Cornelius</creatorcontrib><creatorcontrib>Hautmann, Hubert</creatorcontrib><creatorcontrib>Kratzer, Stephan</creatorcontrib><creatorcontrib>Schneider, Gerhard</creatorcontrib><creatorcontrib>Kreuzer, Matthias</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Anesthesia and analgesia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Weyer, Clara</au><au>Prötzl, Eva</au><au>Kinateder, Thomas</au><au>Nowak, Fabian</au><au>Husemann, Cornelius</au><au>Hautmann, Hubert</au><au>Kratzer, Stephan</au><au>Schneider, Gerhard</au><au>Kreuzer, Matthias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Strength of Alpha Oscillations in the Electroencephalogram Differently Affects Algorithms Used for Anesthesia Monitoring</atitle><jtitle>Anesthesia and analgesia</jtitle><addtitle>Anesth Analg</addtitle><date>2021-12-01</date><risdate>2021</risdate><volume>133</volume><issue>6</issue><spage>1577</spage><epage>1587</epage><pages>1577-1587</pages><issn>0003-2999</issn><eissn>1526-7598</eissn><abstract>Intraoperative patient monitoring using the electroencephalogram (EEG) can help to adequately adjust the anesthetic level. Therefore, the processed EEG (pEEG) provides the anesthesiologist with the estimated anesthesia level. The commonly used approaches track the changes from a fast- and a low-amplitude EEG during wakefulness to a slow- and a high-amplitude EEG under general anesthesia. However, besides these changes, another EEG feature, a strong oscillatory activity in the alpha band (8-12 Hz), develops in the frontal EEG. Strong alpha-band activity during general anesthesia seems to reflect an appropriate anesthetic level for certain anesthetics, but the way the common pEEG approaches react to changes in the alpha-band activity is not well explained. Hence, we investigated the impact of an artificial alpha-band modulation on pEEG approaches used in anesthesia research. We performed our analyses based on 30 seconds of simulated sedation (n = 25) EEG, simulated anesthesia (n = 25) EEG, and EEG episodes from 20 patients extracted from a steady state that showed a clearly identifiable alpha peak in the density spectral array (DSA) and a state entropy (GE Healthcare) around 50, indicative of adequate anesthesia. From these traces, we isolated the alpha activity by band-pass filtering (8-12 Hz) and added this alpha activity to or subtracted it from the signals in a stepwise manner. For each of the original and modified signals, the following pEEG values were calculated: (1) spectral edge frequency (SEF95), (2) beta ratio, (3) spectral entropy (SpEntr), (4) approximate entropy (ApEn), and (5) permutation entropy (PeEn). The pEEG approaches showed different reactions to the alpha-band modification that depended on the data set and the amplification step. The beta ratio and PeEn decreased with increasing alpha activity for all data sets, indicating a deepening of anesthesia. The other pEEG approaches behaved nonuniformly. SEF95, SpEntr, and ApEn decreased with increasing alpha for the simulated anesthesia data (arousal) but decreased for simulated sedation. For the patient EEG, ApEn indicated an arousal, and SEF95 and SpEntr showed a nonuniform change. Changes in the alpha-band activity lead to different reactions for different pEEG approaches. Hence, the presence of strong oscillatory alpha activity that reflects an adequate level of anesthesia may be interpreted differently, by an either increasing (arousal) or decreasing (deepening) pEEG value. This could complicate anesthesia navigation and prevent the adjustment to an adequate, alpha-dominant anesthesia level, when titrating by the pEEG values.</abstract><cop>United States</cop><pub>Lippincott Williams &amp; Wilkin</pub><pmid>34543237</pmid><doi>10.1213/ANE.0000000000005704</doi><tpages>11</tpages></addata></record>
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source MEDLINE; Journals@Ovid LWW Legacy Archive; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Adult
Algorithms
Alpha Rhythm - drug effects
Anesthesia
Anesthesia, General
Bronchoscopy
Computer Simulation
Electroencephalography - drug effects
Female
Humans
Intraoperative Neurophysiological Monitoring - methods
Male
title The Strength of Alpha Oscillations in the Electroencephalogram Differently Affects Algorithms Used for Anesthesia Monitoring
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