Application of audio fingerprinting to neonatal EEG.

A clinical neurophysiologist must recognize patterns in EEG signals to evaluate the health of a patient's brain activity. Rare or unusual patterns may take time to correctly identify. The ability to automatically assist this recall would be beneficial in ensuring that appropriate measures could be taken in a timely fashion. Audio fingerprinting is a method used to identify songs using only a snippet of the song. Fingerprints are extracted from a sub-section of the song and matched against a database of previously computed fingerprints. In this paper, a fingerprint quantization technique is implemented on neonatal EEG data to attempt to identify sections of EEG data when only seeing a sub-section of the data. The impact of signal distortions is investigated and results from a database of one hour recordings from 40 newborns are presented.

Artefact detection in neonatal EEG.

Artefact detection is an important component of any automated EEG analysis. It is of particular importance in analyses such as sleep state detection and EEG grading where there is no null state. We propose a general artefact detection system (GADS) based on the analysis of the neonatal EEG. This system aims to detect both major and minor artefacts (a distinction based primarily on amplitude). As a result, a two-stage system was constructed based on 14 features extracted from EEG epochs at multiple time scales: [2, 4, 16, 32]s. These features were combined in a support vector machine (SVM) in order to determine the presence of absence of artefact. The performance of the GADS was estimated using a leave-one-out cross-validation applied to a database of hour long recordings from 51 neonates. The median AUC was 1.00 (IQR: 0.95-1.00) for the detection of major artefacts and 0.89 (IQR: 0.83-0.95) for the detection of minor artefacts.

An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.

Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.

Quantitative EEG analysis in neonatal hypoxic ischaemic encephalopathy.

OBJECTIVE: To test the hypothesis that quantitative EEG (qEEG) measures are associated with a grading of HIE based on the visual interpretation of neonatal EEG (EEG/HIE). METHODS: Continuous multichannel video-EEG data were recorded for up to 72 h. One-hour EEG segments from each recording were visually analysed and graded by two electroencephalographers (EEGers) blinded to clinical data. Several qEEG measures were calculated for each EEG segment. Kruskal-Wallis testing with post hoc analysis and multiple linear regression were used to assess the hypothesis. RESULTS: Fifty-four full-term infants with HIE were studied. The relative delta power, skewness, kurtosis, amplitude, and discontinuity were significantly different across four EEG/HIE grades (p<0.05). A linear combination of these qEEG measures could predict the EEG/HIE grade assigned by the EEGers with an accuracy of 89%. CONCLUSION: Quantitative analysis of background EEG activity has shown that measures based on the amplitude, frequency content and continuity of the EEG are associated with a visual interpretation of the EEG performed by experienced EEGers. SIGNIFICANCE: Identifying qEEG measures that can separate between EEG/HIE grades is an important first step towards creating a classifier for automated detection of EEG/HIE grades.

Heart rate variability during sleep in healthy term newborns in the early postnatal period.

Normative time- and frequency-domain heart rate variability (HRV) measures were extracted during quiet sleep (QS) and active sleep (AS) periods in 30 healthy babies. All newborn infants studied were less than 12 h old and the sleep state was classified using multi-channel video EEG. Three bands were extracted from the heart rate (HR) spectrum: very low frequency (VLF), 0.01-0.04 Hz; low frequency (LF), 0.04-0.2 Hz, and high frequency (HF), >0.2 Hz. All metrics were averaged across all patients and per sleep state to produce a table of normative values. A noticeable peak corresponding to activity in the RSA band was found in 80% patients during QS and 0% of patients during AS, although some broadband activity was observed. The majority of HRV metrics showed a statistically significant separation between QS and AS. It can be concluded that (i) activity in the RSA band is present during QS in the healthy newborn, in the first 12 h of life, (ii) HRV measures are affected by sleep state and (iii) the averaged HRV metrics reported here could assist the interpretation of HRV data from newborns with neonatal illnesses.

EEG in the healthy term newborn within 12 hours of birth.

OBJECTIVE: To characterise and quantify the EEG during sleep in healthy newborns in the early newborn period. METHODS: Continuous multi-channel video-EEG data was recorded for up to 2 hours in normal newborns within 12 hours of birth. The total amount of active (AS) and quiet sleep (QS) was calculated in the first hour of recording. The EEG signal was quantitatively analysed for symmetry and synchrony. Spectral edge frequency (SEF), spectral entropy (H) and relative delta power (delta(R)) were calculated for a ten-minute segment of AS and QS in each recording. Paired t-test and Wilcoxon rank sum test were used for data analysis. RESULTS: Thirty normal newborn babies were studied, 10 within 6 hours of birth and 20 between 6 and 12 hours. All babies showed continuous symmetrical and synchronous EEG activity and well-developed sleep-wake cycling (SWC) with the median percentage of AS--48.5% and QS--36.6%. Quantitative EEG analysis of sleep epochs showed that SEF and H were significantly higher (p<0.0001) and delta(R) was significantly lower (p<0.0001) in AS than in QS. CONCLUSION: The normal newborn EEG shows symmetrical and synchronous continuous activity and well-developed SWC as early as within the first 6 hours of birth. Quantitative analysis of the EEG in the early postnatal period reveals differences in SEF, H and delta(R) for AS and QS periods. SIGNIFICANCE: These findings may have implications for quantitative analysis of the newborn EEG, including the EEG of babies with hypoxic ischaemic encephalopathy.

A comparison of quantitative EEG features for neonatal seizure detection.

OBJECTIVE: This study was undertaken to identify the best performing quantitative EEG features for neonatal seizures detection from a test set of 21. METHODS: Each feature was evaluated on 1-min, artefact-free segments of seizure and non-seizure neonatal EEG recordings. The potential utility of each feature for neonatal seizure detection was determined using receiver operating characteristic analysis and repeated measures t-tests. A performance estimate of the feature set was obtained using a cross-fold validation and combining all features together into a linear discriminant classifier model. RESULTS: Significant differences between seizure and non-seizure segments were found in 19 features for 17 patients. The best performing features for this application were the RMS amplitude, the line length and the number of local maxima and minima. An estimate of the patient independent classifier performance yielded a sensitivity of 81.08% and specificity of 82.23%. CONCLUSIONS: The individual performances of 21 quantitative EEG features in detecting electrographic seizure in the neonate were compared and numerically quantified. Combining all features together into a classifier model led to superior performance than that provided by any individual feature taken alone. SIGNIFICANCE: The results documented in this study may provide a reference for the optimum quantitative EEG features to use in developing and enhancing neonatal seizure detection algorithms.