Detecting interruption events using EEG

Molcho³

et al. 2022

Preprint

Self Cite

Background: Post-operative cognitive dysfunction/decline is a well-known phenomenon and of crucial importance, especially in the elderly, emphasizing the significance of selecting the proper anesthetics. General anesthesia can be accomplished by inhalation-based (volatile) or total intravenous anesthesia (TIVA). While their effects on post-operative symptoms have been investigated, little is known about their influence on brain functionalities during the surgery itself. Objective: To assess differences in brain activity between volatile and TIVA anesthetics during surgery. Methods: Seventeen patients who were electively scheduled for laparoscopic cholecystectomy in Galilee Medical Center, Nahariya, gave written informed consent to participate in the study and were randomly divided to receive either volatile anesthesia (n = 9), or TIVA (n = 8). The level of anesthesia was kept equal in both groups. A single bipolar EEG electrode (Aurora by Neurosteer®) was placed on each participant’s forehead. The electrode presented real-time activity and collected participants’ data during the surgery. The dependent variables included frequency bands (delta, theta, alpha, and beta), and three features (VC9, ST4, and A0) previously extracted with the Aurora device and provided by Neurosteer®.Results: All surgeries were uneventful, and all patients showed bispectral index (BIS) scores less than 60. Feature activity under volatile anesthesia (in comparison to TIVA) was significantly lower for the delta, theta, and alpha frequency bands and for the three features (VC9, ST4, and A0). Further analysis showed that the largest difference between anesthesia types was for feature A0. Conclusions: Both EEG frequency bands and novel brain activity features provide evidence that volatile anesthesia further reduces components of brain activity in comparison to TIVA anesthesia. Specifically, A0, which previously showed a correlation with cognitive decline severity and cognitive load, exhibited the most prominent difference between anesthesia types. Together, this study suggests that measuring brain activity during anesthesia using sensitive features can reveal that different anesthesia types may affect brain activity differently, which could affect a patient’s recovery from anesthesia, and consequently reduce post-operative cognitive decline.

Section: Introductionmentioning

confidence: 80%

Section: Data Analysis Resultsmentioning

confidence: 99%

Section: Dependent Variablesmentioning

confidence: 90%

Section: Dependent Variablesmentioning

confidence: 99%

Section: Dependent Variablesmentioning

confidence: 99%

See 3 more Smart Citations

Reduced neural activity during volatile anesthesia compared to TIVA: evidence from a novel EEG signal processing analysis

Bickel

Molcho³

et al. 2022

Preprint

Self Cite

Reduced neural activity during volatile anesthesia compared to TIVA: evidence from a novel EEG signal processing analysis

Bickel

Molcho³

et al. 2022

Preprint

Self Cite

Background Post-operative cognitive dysfunction/decline is a well-known phenomenon and of crucial importance, especially in the elderly, emphasizing the significance of selecting the proper anesthetics. General anesthesia can be accomplished by inhalation-based (volatile) or total intravenous anesthesia (TIVA). While their effects on post-operative symptoms have been investigated, little is known about their influence on brain functionalities during the surgery itself. Objective To assess differences in brain activity between volatile and TIVA anesthetics during surgery. Methods Seventeen patients who were electively scheduled for laparoscopic cholecystectomy in Galilee Medical Center, Nahariya, gave written informed consent to participate in the study and were randomly divided to receive either volatile anesthesia (n = 9), or TIVA (n = 8). The level of anesthesia was kept equal in both groups. A single bipolar EEG electrode (Aurora by Neurosteer®) was placed on each participant’s forehead. The electrode presented real-time activity and collected participants’ data during the surgery. The dependent variables included frequency bands (delta, theta, alpha, and beta), and three features (VC9, ST4, and A0) previously extracted with the Aurora device and provided by Neurosteer®. Results All surgeries were uneventful, and all patients showed bispectral index (BIS) scores less than 60. Feature activity under volatile anesthesia (in comparison to TIVA) was significantly lower for the delta, theta, and alpha frequency bands and for the three features (VC9, ST4, and A0). Further analysis showed that the largest difference between anesthesia types was for feature A0. Conclusions Both EEG frequency bands and novel brain activity features provide evidence that volatile anesthesia further reduces components of brain activity in comparison to TIVA anesthesia. Specifically, A0, which previously showed a correlation with cognitive decline severity and cognitive load, exhibited the most prominent difference between anesthesia types. Together, this study suggests that measuring brain activity during anesthesia using sensitive features can reveal that different anesthesia types may affect brain activity differently, which could affect a patient’s recovery from anesthesia, and consequently reduce post-operative cognitive decline.

Single-Channel EEG Features Reveal an Association With Cognitive Decline in Seniors Performing Auditory Cognitive Assessment

Molcho¹,

Regev-Plotnik

et al. 2022

Front. Aging Neurosci.

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BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.MethodsThis study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.ResultsMMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24–27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.DiscussionThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.Trial RegistrationNIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].