Dataset Information

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring.

ABSTRACT: PURPOSE:Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns. METHODS:Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients. RESULTS:Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68). CONCLUSIONS:This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

SUBMITTER: Massey SL

PROVIDER: S-EPMC6322680 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring.

Massey Shavonne L SL Shou Haochang H Clancy Robert R DiGiovine Marissa M Fitzgerald Mark P MP Fung France W FW Farrar John J Abend Nicholas S NS

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society 20190101 1

<h4>Purpose</h4>Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers in ...[more]

PMID: 30383719

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Project description:Clinical evaluation of scapular dyskinesis (SD) aims to identify abnormal scapulothoracic movement, underlying causal factors, and the potential relationship with shoulder symptoms. The literature proposes different methods of dynamic clinical evaluation of SD, but improved reliability and agreement values are needed. The present study aimed to evaluate the intrarater and interrater agreement and reliability of three SD classifications: 1) 4-type classification, 2) Yes/No classification, and 3) scapular dyskinesis test (SDT). Seventy-five young athletes, including 45 men and 30 women, were evaluated. Raters evaluated the SD based on the three methods during one series of 8-10 cycles (at least eight and maximum of ten) of forward flexion and abduction with an external load under the observation of two raters trained to diagnose SD. The evaluation protocol was repeated after 3 h for intrarater analysis. The agreement percentage was calculated by dividing the observed agreement by the total number of observations. Reliability was calculated using Cohen Kappa coefficient, with a 95% confidence interval (CI), defined by Kappa coefficient ±1.96 multiplied by the measurement standard error. The interrater analyses showed an agreement percentage between 80% and 95.9% and an almost perfect reliability (k>0.81) for the three classification methods in all the test conditions, except the 4-type and SDT classification methods, which had substantial reliability (k<0.80) in shoulder abduction. Intrarater analyses showed agreement percentages between 80.7% and 89.3% and substantial reliability (0.67 to 0.81) for both raters in the three classifications. CIs ranged from moderate to almost perfect categories. This indicates that the three SD classification methods investigated in this study showed high reliability values for both intrarater and interrater evaluation throughout a protocol that provided SD evaluation training of raters and included several repetitions of arm movements with external load during a live assessment.

Project description:OBJECTIVE:The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED. METHODS:IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization. RESULTS:Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11. CONCLUSIONS:The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10). SIGNIFICANCE:By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

Dataset Information

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring.

Publications

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring.

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