RESUMEN
AIMS OF THE STUDY: Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. MATERIALS AND METHODS: First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. RESULTS: In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. CONCLUSION: The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies.
Asunto(s)
Cuidados Críticos/normas , Electroencefalografía/normas , Terminología como Asunto , Algoritmos , Automatización , Absceso Encefálico/diagnóstico , Gráficos por Computador , Femenino , Humanos , Persona de Mediana Edad , Accidente Cerebrovascular/diagnóstico , Interfaz Usuario-ComputadorRESUMEN
AIM OF THE STUDY: A novel method for removal of artifacts from long-term EEGs was developed and evaluated. The method targets most types of artifacts and works without user interaction. MATERIALS AND METHODS: The method is based on a neurophysiological model and utilizes an iterative Bayesian estimation scheme. The performance was evaluated by two independent reviewers. From 48 consecutive epilepsy patients, 102 twenty-second seizure onset EEGs were used to evaluate artifacts before and after artifact removal and regarding the erroneous attenuation of true EEG patterns. RESULTS: The two reviewers found "major improvements" in 59% and 49% of the EEG epochs respectively, and "minor improvements" in 38% and 47% of the epochs, respectively. The answer "similar or worse" was chosen only in 0% and 4%, respectively. Neither of the reviewers found "major attenuations", i.e., a significant attenuation of significant EEG patterns. Most EEG epochs were found to be either "mostly preserved" or "all preserved". A "minor attenuation" was found only in 0% and 17%, respectively. CONCLUSIONS: The proposed artifact removal algorithm effectively removes artifacts from EEGs and improves the readability of EEGs impaired by artifacts. Only in rare cases did the algorithm slightly attenuate EEG patterns, but the clear visibility of significant patterns was preserved in all cases of this study. Current artifact removal methods work either semi-automatically or with insufficient reliability for clinical use, whereas the "PureEEG" method works fully automatically and leaves true EEG patterns unchanged with a high reliability.
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Algoritmos , Electroencefalografía/métodos , Epilepsia/fisiopatología , Monitoreo Fisiológico/métodos , Procesamiento de Señales Asistido por Computador , Artefactos , Teorema de Bayes , Electroencefalografía/instrumentación , Procesamiento Automatizado de Datos , Femenino , Humanos , Masculino , Modelos Neurológicos , Monitoreo Fisiológico/instrumentación , Reproducibilidad de los ResultadosRESUMEN
A parameter optimization method for an automatic seizure detection algorithm using the Nelder Mead algorithm is presented. A suitable cost function for joint optimization of sensitivity and false alarm rate is proposed. The optimization is done using EEG datasets from 23 patients and validated on datasets from another 23 patients. The resulting sensitivity was 82.3% with a false alarm rate of 0.24 FA/h. This is a reduction of the false alarm rate by 1.58 FA/h with an acceptable loss of sensitivity of 4.3%.
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Electroencefalografía/métodos , Convulsiones/diagnóstico , Algoritmos , Procesamiento Automatizado de Datos , Reacciones Falso Positivas , Humanos , Curva ROC , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Procesamiento de Señales Asistido por ComputadorRESUMEN
In this paper we show a proof of concept for novel automatic seizure onset zone detector. The proposed approach utilizes the Austrian Institute of Technology (AIT) seizure detection system EpiScan extended by a frequency domain source localization module. EpiScan was proven to detect rhythmic epileptoform seizure activity often seen during the early phase of epileptic seizures with reasonable high sensitivity and specificity. Additionally, the core module of EpiScan provides complex coefficients and fundamental frequencies representing the rhythmic activity of the ictal EEG signal. These parameters serve as input to a frequency domain version of the Minimum Variance Beamformer to estimate the most dominant source. The position of this source is the detected seizure onset zone. The results are compared to a state of the art wavelet transformation approach based on a manually chosen frequency band. Our first results are encouraging since they coincide with those obtained with the wavelet approach and furthermore show excellent accordance with the medical report for the majority of analyzed seizures. In contrast to the wavelet approach our method has the advantage that it does not rely on a manual selection of the frequency band.