Electroencephalography leads placed by nontechnologists using a template system produce signals equal in quality to technologist-applied, collodion disk leads.
نویسندگان
چکیده
The purpose of this study was to compare the quality of the electroencephalographic (EEG) data obtained with a BraiNet template in a practical use setting, to that obtained with standard 10/20 spaced, technologist-applied, collodion-based disk leads. Pairs of 8-hour blocks of EEG data were prospectively collected from 32 patients with a Glasgow coma score of ≤9 and clinical concern for underlying nonconvulsive status epilepticus over a 6-month period in the Neurocritical Care Unit at the Duke University Medical Center. The studies were initiated with the BraiNet template system applied by critical care nurse practitioners or physicians, followed by standard, collodion leads applied by registered technologists using the 10/20 system of placement. Impedances were measured at the beginning and end of each block recorded and variance in impedance, mean impedance, and the largest differences in impedances found within a given lead set were compared. Physicians experienced in reading EEG performed a masked review of the EEG segments obtained to assess the subjective quality of the recordings obtained with the templates. We found no clinically significant differences in the impedance measures. There was a 3-hour reduction in the time required to initiate EEG recording using the templates (P < 0.001). There was no difference in the overall subjective quality distributions for template-applied versus technologist-applied EEG leads. The templates were also found to be well accepted by the primary users in the intensive care unit. The findings suggest that the EEG data obtained with this approach are comparable with that obtained by registered technologist-applied leads and represents a possible solution to the growing clinical need for continuous EEG recording availability in the critical care setting.
منابع مشابه
A Real-Time Electroencephalography Classification in Emotion Assessment Based on Synthetic Statistical-Frequency Feature Extraction and Feature Selection
Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...
متن کاملA novel method based on a combination of deep learning algorithm and fuzzy intelligent functions in order to classification of power quality disturbances in power systems
Automatic classification of power quality disturbances is the foundation to deal with power quality problem. From the traditional point of view, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection and classification. However, there are some inherent defects in signal analysis and the procedure of manual fe...
متن کاملEpileptic Seizure Detection in EEG signals Using TQWT and SVM-GOA Classifier
Background: Epilepsy is a Brain disorder disease that affects people's quality of life. If it is diagnosed at an early stage, it will not be spread. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. However, this screening system cannot diagnose epileptic seizure states precisely. Nevertheless, with the help of computer-aided diagnosis systems (CADS), neurologists ca...
متن کاملMental Fatigue Assessment using recording Brain Signals: Electroencephalography
Background and Objectives: Mental fatigue is a condition triggered by prolonged cognitive activity. Mental fatigue causes brain over-activity. This is a condition where the brain cells become exhausted, hampering person productivity, and overall cognitive function. The aim of this study was to assess students’ mental fatigue using brain indices. Methods: The present descriptive - analytic stud...
متن کاملDesign and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals
A computer-based classification system has been designed capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised 25 patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society
دوره 29 1 شماره
صفحات -
تاریخ انتشار 2012