Interictal Epileptiform Discharge Detection in EEG in Different Practice Settings.

OBJECTIVE: The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings. METHODS: Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison. RESULTS: Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts. CONCLUSIONS: Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.

Interictal epileptiform discharge characteristics underlying expert interrater agreement.

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.

Characteristics of EEG Interpreters Associated With Higher Interrater Agreement.

PURPOSE: The goal of the project is to determine characteristics of academic neurophysiologist EEG interpreters (EEGers), which predict good interrater agreement (IRA) and to determine the number of EEGers needed to develop an ideal standardized testing and training data set for epileptiform transient (ET) detection algorithms. METHODS: A three-phase scoring method was used. In phase 1, 19 EEGers marked the location of ETs in two hundred 30-second segments of EEG from 200 different patients. In phase 2, EEG events marked by at least 2 EEGers were annotated by 18 EEGers on a 5-point scale to indicate whether they were ETs. In phase 3, a third opinion was obtained from EEGers on any inconsistencies between phase 1 and phase 2 scoring. RESULTS: The IRA for the 18 EEGers was only fair. A select group of the EEGers had good IRA and the other EEGers had low IRA. Board certification by the American Board of Clinical Neurophysiology was associated with better IRA performance but other board certifications, years of fellowship training, and years of practice were not. As the number of EEGers used for scoring is increased, the amount of change in the consensus opinion decreases steadily and is quite low as the group size approaches 10. CONCLUSIONS: The IRA among EEGers varies considerably. The EEGers must be tested before use as scorers for ET annotation research projects. The American Board of Clinical Neurophysiology certification is associated with improved performance. The optimal size for a group of experts scoring ETs in EEG is probably in the 6 to 10 range.

Comparison of a Novel Dry Electrode Headset to Standard Routine EEG in Veterans.

OBJECTIVE: This purpose of this study was to evaluate the usefulness of a prototype battery-powered dry electrode system (DES) EEG recording headset in Veteran patients by comparing it with standard EEG. METHODS: Twenty-one Veterans had both a standard electrode system recording and DES recording in nine different patient states at the same encounter. Setup time, patient comfort, and subject preference were measured. Three experts performed technical quality rating of each EEG recording in a blinded fashion using the web-based EEGnet system. Power spectra were compared between DES and standard electrode system recordings. RESULTS: The average time for DES setup was 5.7 minutes versus 21.1 minutes for standard electrode system. Subjects reported that the DES was more comfortable during setup. Most subjects (15 of 21) preferred the DES. On a five-point scale (1-best quality to 5-worst quality), the technical quality of the standard electrode system recordings was significantly better than for the DES recordings, at 1.25 versus 2.41 (P < 0.0001). But experts found that 87% of the DES EEG segments were of sufficient technical quality to be interpretable. CONCLUSIONS: This DES offers quick and easy setup and is well tolerated by subjects. Although the technical quality of DES recordings was less than standard EEG, most of the DES recordings were rated as interpretable by experts. SIGNIFICANCE: This DES, if improved, could be useful for a telemedicine approach to outpatient routine EEG recording within the Veterans Administration or other health system.

Osteoconductivity and osteoinductivity of NanoFUSE(®) DBM.

Bone graft substitutes have become an essential component in a number of orthopedic applications. Autologous bone has long been the gold standard for bone void fillers. However, the limited supply and morbidity associated with using autologous graft material has led to the development of many different bone graft substitutes. Allogeneic demineralized bone matrix (DBM) has been used extensively to supplement autograft bone because of its inherent osteoconductive and osteoinductive properties. Synthetic and natural bone graft substitutes that do not contain growth factors are considered to be osteoconductive only. Bioactive glass has been shown to facilitate graft containment at the operative site as well as activate cellular osteogenesis. In the present study, we present the results of a comprehensive in vitro and in vivo characterization of a combination of allogeneic human bone and bioactive glass bone void filler, NanoFUSE(®) DBM. NanoFUSE(®) DBM is shown to be biocompatible in a number of different assays and has been cleared by the FDA for use in bone filling indications. Data are presented showing the ability of the material to support cell attachment and proliferation on the material thereby demonstrating the osteoconductive nature of the material. NanoFUSE(®) DBM was also shown to be osteoinductive in the mouse thigh muscle model. These data demonstrate that the DBM and bioactive glass combination, NanoFUSE(®) DBM, could be an effective bone graft substitute.

Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis.

The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification.