Working With Environmental Noise and Noise-Cancelation: A Workload Assessment With EEG and Subjective Measures
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ABSTRACT:
SUBMITTER: Pieper K
PROVIDER: S-EPMC8591241 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.
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Project description:Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.
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Project description:Highlights? Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.? Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.? Approximate Entropy and Sample Entropy performed best in detecting burst suppression.ObjectiveEntropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents.MethodsTwelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared.ResultsAll the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R (2)) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA.ConclusionEach entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.
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Project description:BackgroundRight ventricle (RV) size and function assessment by echocardiography (echo) is a standard tool in the ICU. Frequently subjective assessment is performed, and guidelines suggest its utility in adequately trained clinicians. We aimed to compare subjective (visual) assessment of RV size and function by ICU physicians, with advanced qualifications in echocardiography, vs objective measurements.MethodsICU specialists with a qualification in advanced echocardiography reviewed 2D echo clips from critically ill patients on mechanical ventilation with PaO2:FiO2 < 300. Subjective assessments of RV size and function were made independently using a three-class categorical scale. Agreement (B-score) and bias (p value) were analysed using objective echo measurements. RV size assessment included RV end-diastolic area (EDA) and diameters. RV function assessment included fractional area change, S', TAPSE and RV free wall strain. Binary and ordinal analysis was performed.ResultsFifty-two clinicians reviewed 2D images from 80 patients. Fair agreement was seen with objective measures vs binary assessment of RV size (RV EDA 0.26 [p < 0.001], RV dimensions 0.29 [p = 0.06]) and function (RV free wall strain 0.27 [p < 0.001], TAPSE 0.27 [p < 0.001], S' 0.29 [p < 0.001], FAC 0.31 [p = 0.16]). However, ordinal data analysis showed poor agreement with RV dimensions (0.11 [p = 0.06]) and RV free wall strain (0.14 [p = 0.16]). If one-step disagreement was allowed, agreement was good (RV dimensions 0.6 [p = 0.06], RV free wall strain 0.6 [p = 0.16]). Significant overestimation of severity of abnormalities was seen with subjective assessment vs RV EDA, TAPSE, S' and fractional area change.ConclusionSubjective (visual) assessment of RV size and function, by ICU specialists trained in advanced echo, can be fairly reliable for the initial exclusion of significant RV pathology. It seems prudent to avoid subjective RV assessment in isolation.
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