Quantitative Measurement of Brain Electrical Activity in Delirium

1991 ◽  
Vol 3 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Andrew F. Leuchter ◽  
Sandra A. Jacobson
Keyword(s):  
Visual Inspection ◽  
Quantitative Methods ◽  
Quantitative Measurement ◽  
Quantitative Eeg ◽  
Eeg Analysis ◽  
Eeg Data ◽  
Eeg Power ◽  
Logistical Problem ◽  
Work Done ◽  
Electroencephalogram Eeg

It has long been known that the conventional electroencephalogram (EEG) is a useful tool in the evaluation of delirium. There are moderate correlations between the amount of slowing seen on EEG and the degree of confusion or level of arousal observed among delirious patients. The usefulness of the EEG for assessment and diagnosis in this area has been limited, however, by: (a) difficulties in assessing the significance of slow-wave activity, (b) problems in detecting changes in relative EEG power, and (c) the logistical problem of lengthy recording sessions with agitated patients. This article selectively reviews the development of quantitative EEG methods and presents preliminary data from an ongoing longitudinal study of the use of these methods among elderly delirious patients. There has been little work done in applying quantitative methods to the study of delirium, and no work done that has systematically compared conventional EEG analysis to quantitative analysis. Delirium shares electrophysiological characteristics with other organic mental syndromes, however, where quantitative EEG has been shown to be useful. Furthermore, analysis of digital EEG data is inherently superior to visual inspection in assessing the distribution of EEG power among different frequency bands. Previous studies, as well as data presented here, suggest that quantitative EEG is a clinically useful supplement to the conventional EEG for the assessment of elderly patients with delirium.

Electroencephalogram monitoring in the critically ill

2016 ◽  
Author(s):  
Paul M. Vespa
Keyword(s):  
Brain Function ◽  
Visual Inspection ◽  
Perfusion Pressure ◽  
Quantitative Eeg ◽  
Rapid Identification ◽  
Brain Ischaemia ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Processed Eeg ◽  
Electroencephalogram Eeg

Electroencephalography monitoring provides a method for monitoring brain function, which can complement other forms of monitoring, such as monitoring of intracranial pressure and derived parameters, such as cerebral perfusion pressure. Continuous electroencephalogram (EEG) monitoring can be helpful in seizure detection after brain injury and coma. Seizures can be detected by visual inspection of the raw EEG and/or processed EEG data. Treatment of status epilepticus can be improved by rapid identification and abolition of seizures using continuous EEG. Quantitative EEG can also be used to detect brain ischaemia and seizures, to monitor sedation and aid prognosis.

scholarly journals EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai
Keyword(s):  
Visual Inspection ◽  
Sleep Stages ◽  
Subjective Time ◽  
Sleep Staging ◽  
Large Bulk ◽  
State Classification ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

EEG Analysis of Imagined Speech

2020 ◽  
pp. 679-692
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

EEG Analysis of Imagined Speech

2016 ◽  
Vol 3 (2) ◽  
pp. 32-44
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data

2014 ◽  
Vol 125 (1) ◽  
pp. 32-46 ◽  
Author(s):  
Ptolemaios G. Sarrigiannis ◽  
Yifan Zhao ◽  
Hua-Liang Wei ◽  
Stephen A. Billings ◽  
Jayne Fotheringham ◽  
...  
Keyword(s):  
Quantitative Eeg ◽  
Error Reduction ◽  
Reduction Ratio ◽  
Test Validation ◽  
Eeg Analysis ◽  
Causality Test ◽  
Eeg Data ◽  
Quantitative Eeg Analysis

scholarly journals The Different Colors of mAbs in Solution

Antibodies ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 21
Author(s):  
Alexandre Ambrogelly
Keyword(s):  
Visual Inspection ◽  
Quantitative Methods ◽  
Geographical Area ◽  
Therapeutic Protein ◽  
Protein Solutions ◽  
Protein Solution ◽  
Critical Quality Attribute ◽  
Recombinant Therapeutic Protein ◽  
Protein Variants ◽  
Antibody Solution

The color of a therapeutic monoclonal antibody solution is a critical quality attribute. Consistency of color is typically assessed at time of release and during stability studies against preset criteria for late stage clinical and commercial products. A therapeutic protein solution’s color may be determined by visual inspection or by more quantitative methods as per the different geographical area compendia. The nature and intensity of the color of a therapeutic protein solution is typically determined relative to calibrated standards. This review covers the analytical methodologies used for determining the color of a protein solution and presents an overview of protein variants and impurities known to contribute to colored recombinant therapeutic protein solutions.

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