Calculation of compact amplitude-integrated EEG tracing and upper and lower margins using raw EEG data

Amplitude-integrated EEG (aEEG) is a popular method for monitoring cerebral function. Although various commercial aEEG recorders have been produced, a detailed aEEG algorithm currently is not available. The upper and lower margins in the aEEG tracing are the discriminating features for data inspection and tracing classification. However, most aEEG devices require that these margins be measured semi-subjectively. This paper proposes a step-by-step signal-processing method to calculate a compact aEEG tracing and the upper/lower margin using raw EEG data. The high accuracy of the algorithm was verified by comparison with a recognized commercial aEEG device based on a representative testing dataset composed of 72 aEEG data. The introduced digital algorithm achieved compact aEEG tracing with a small data size. Moreover, the algorithm precisely represented the upper and lower margins in the tracing for objective data interpretation. The described method should facilitate aEEG signal processing and further establish the clinical and experimental application of aEEG methods.

Amplitude-Integrated Electroencephalography Predicts Outcome in Patients with Coma After Acute Brain Injury

Prognostication of coma patients after brain injury is important, yet challenging. In this study, we evaluated the predictive value of amplitude-integrated electroencephalography （aEEG） for neurological outcomes in coma patients. From January 2013 to January 2016, 128 coma patients after acute brain injury were prospectively enrolled and monitored with aEEG. The 6-month neurological outcome was evaluated using the Cerebral Performance Category Scale. aEEG monitoring commenced at a median of 7.5 days after coma onset. Continuous normal voltage predicted a good 6-month neurological outcome with a sensitivity of 93.6% and specificity of 85.2%. In contrast, continuous extremely low voltage, burst-suppression, or a flat tracing was correlated with poor 6-month neurological outcome with a sensitivity of 76.5% and specificity of 100%. In conclusion, aEEG is a promising predictor of 6-month neurological outcome for coma patients after acute brain injury.

Clinical Manifestations and Amplitude-integrated Encephalogram in Neonates with Early-onset Epileptic Encephalopathy

Background： The patients with early-onset epileptic encephalopathy （EOEE） suffer from neurodevelopmental delay. The aim of this study was to analyze the clinical manifestations and amplitude-integrated encephalogram （aEEG） characteristics of infants with EOEE with onset within the neonatal period, to make early diagnosis to improve the prognosis. Methods： One-hundred and twenty-eight patients with neonatal seizure were enrolled and followed up till 1 year old. Sixty-six neonates evolved into EOEE were as the EOEE group, the other 62 were as the non-EOEE （nEOEE） group. Then we compared the clinical and aEEG characteristics between the two groups to analyze the manifestations in neonates with EOEE. Results： Compared to the nEOEE group, the incidence of daily seizure attacks, more than two types of convulsions, more than two antiepileptic drugs （AEDs） application, severely abnormal aEEG background, absence of cyclicity, and more than two seizures detection were significantly higher in the EOEE group （P 〈 0.05） （97% vs. 54.8%; 30.3% vs. 14.5%; 97.0% vs. 25.4%; 39.4% vs. 3.2%; 57.6% vs. 9.7%; and 56% vs. 3.2%, respectively）. Severely abnormal background pattern （odds ratio [OR] = 0.081, 95% confidence interval [CI]：0.009-0.729, P = 0.025） and more than two seizures detection by aEEG （OR = 0.158, 95% CI： 0.043-0.576, P = 0.005） were the independent risk factors for the evolvement into EOEE. The upper and lower margins of active sleep （AS） and quiet sleep （QS） were significantly higher in EOEE group than those of the control group （P 〈 0.05） （34.3 ± 13.6 vs. 21.3 ± 6.4; 9.9 ± 3.7 vs. 6.7 ± 2.2; 41.2 ± 15.1 vs. 30.4 ± 11.4;and 11.9 ± 4.4 vs. 9.4 ± 4.0; unit： μV, respectively）. AS upper margin was demonstrated a higher diagnostic specificity and sensitivity for EOEE than another three parameters according to the receiver operating characteristic curves; the area under the curve was 0.827. Conclusions： The clinical characteristics of the neonatal seizure which will evolve into EOEE were more than two AEDs application, high seizure frequency （daily attack）, and more than two types of the seizure. Significant high voltage, severely abnormal background, absence of cyclicity, and more than two seizures detected on aEEG were the meaningful indicators to the prediction of EOEE.

Newborns' sleep-wake cycle development on amplitude integrated electroencephalography

Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:Bedside aEEG monitoring was carried out for healthy newborns from 32 to 46 weeks CA between September 1,2011 and August 30,2012.For each aEEG tracing,mean duration of every complete SWC,number of SWC repetition within 12 hours,mean duration of each narrow and broadband of SWC,mean voltage of the upper edge and lower edge of SWC,mean bandwidth of SWC were counted and calculated.Analysis of the correlations between voltages or bandwidth of SWC and CA was performed to assess the developmental changes of central nervous system of newborns with different CA.Results:The SWC of different CA on aEEG showed clearly identifiable trend after 32 weeks of CA.The occurrence of SWC gradually increases from preterm to post-term infants;term infants had longer SWC duration.The voltage of upper edge of the broadband decreased at 39 weeks,while the lower edge voltage increases and the bandwidth of broadband declined along with the growing CA.The upper edge of the narrowband dropped while the lower edge rised gradually,especially in preterm stage.The width of the narrowband narrowed down while CA increased.Conclusions:The SWC on aEEG of 32-46 weeks infants showed a continuous,dynamic and developmental progress.The appearance of SWC and the narrowing bandwidth of narrowband is the main indicator to identify the CA-dependent SWC from the preterm to the late preterm period.The lower edge of the broadband identifi es the term to post-term period.