Detection of EEG transients in neonates

   EEG is characterized by ongoing background rhythms and transient events with varaiable amplitude and frequency. An EEG transient can be defined as "a nonperiodic activity of short duration". EEG transients are usually characterized by specific morphologies distinct from background activity. Physiological EEG transients in adults and children can be classified based on their duration (Niedermeyer and Lopes da Silva, 1999). In adults, EEG transient waveforms are usually manifestations of some sort of brain dysfunction. Their contextual information such as polarity, amplitude, shape and spatial distribution often provides information on the location and severity of the brain abnormality. In adults, epileptiform discharges such as interictal spikes and sharp waves (Fig. 1) are the most frequent events, which play an important role in the diagnosis of epilepsy (Smith, 2005).

Figure 1. Epileptiform discharges in adult EEG.

   In neonates, EEG background activity consists of continuous and discontinuous patterns. The discontinuous EEG background activity is characterized by long periods of very low amplitudes interrupted by high-amplitude bursts of variable frequencies. The cerebral activity of preterm and full-term newborns is characterized by a great diversity of age-dependent electrophysiological properties. The maturation process is concomitant with age-dependent changes in temporal and spatial characteristics of electroencephalographic events. In newborns, the appearance or disappearance of specific EEG physiological events may indicate temporal involvement of cerebral neural networks. In preterm and fullterm infants, there are several easily recognizable physiological EEG transients with short durations (<1 sec), such as delta brush and temporal theta activities (Fig. 2a and b). Pathological EEG transients are electrographic signs of local disturbances in brain network functions, such as positive rolandic sharp waves (Fig. 2c).

Figure 2. (a) delta brush patterns, (b) premature temporal theta, (c) positive rolandic sharp waves.

   In general, the detection of EEG transients is more challenging in neonates due to their morphological variability and time-varying temporal and spatial characteristics of EEG background activities. By visual inspection, EEG experts can detect EEG transients in EEG recordings based on their morphology and spatial distribution. This procedure is complex and time-consuming, requiring highly trained neurophysiologists. In our study published in 2009, we developed a semi-automatic EEG transient detection system to detect physiological and abnormal transients in neonatal EEG (Fig. 3). In this method, a template (an EEG transient of interest, Fig. 4) is first selected to capture the spatio-temporal characteristics of the events to be detected by the detection method. The template is then fed into a coarse-to-fine temporal event detection procedure for detecting single-channel EEG event candidates. Next, a coarse-to-fine spatial event selection algorithm selects and groups EEG event candidates based on  their spatial characteristics. The main goal is to identify event goups with similar temporal characteristics and spatial distribution.

Figure 3. Block diagram of the spatiotemporal EEG event detection system.

Figure 4. Temporal features calculated for each event: half-wave amplitudes (Ahw1, Ahw2), event amplitude (Aw), half-wave durations (T1, T2), half-wave slopes (S1, S2).

   In the preprocessing step, multichannel EEG signals are first band-pass filtered between 0.5 and 70 Hz to eliminate low-frequency artifacts and high-frequency noise. A notch filter is also applied to suppress 50 Hz power-line interference. To reduce false positives due to artifacts, three most common artifacts, movement, electrooculogram (EOG) and electromyogram (EMG) are then identified using an artifact detection algorithm. The hierarchical time-domain procedure involves the application of coarse-tuning (feature thresholding) and fine-tuning subroutines (shape matching). On a single channel basis, peaks and troughs are determined using a detection algorithm based on zero-crossings of the first derivative of the filtered EEG signal on the template channel. All waves exhibiting the same polarity as the template are then selected. Next, the morphological features (Fig. 4) of the preselceted waves are determined. These parameters present a set of relevant features with maximum discrimination power for temporal event detection. All events exhibiting similar features to those extracted from the template within specific threshold ranges are selected and saved for further analysis using the fine-tuning procedure. A dynamic time warping (DTW)-based template matching method (Fig. 5) is then used to achieve maximum correlations between the template and selected event candidates. Transient events exhibiting maximum similarity in shape with the templae are  selected for spatial event detection.

Figure 5. (a) Summed squared error between the template (T) and a segment of signal (S), (b) typical template matching, one-to-one alignment on the time axis versus dynamic time warping non-linear alignment with a warped time axis.

  To obtain high sensitivity and selectivity, we used a hierarchical spatial coarse-to-fine selection procedure, in which inter-channel correlations are used to further eliminate single-channel isolated events. In this step, the candidates selected at the fine temporal event matching phase are evaluated one by one. The template-like event candidates are kept if the interchannel correlations between the template channel and other channels within each event's time window exceed a predefined threshold. A fine spatial event selection procedure is finally used to identify and classify events based on their spatial features computed using a dipole fitting method (Fig. 6).

Figure 6. Definition of the parameters used for the spatial clustering. R: right, L: left.

   To evaluate the spatiotemporal event detection system, high density (64 electrodes) EEGs recorded from four neonates (aged 36–43 weeks) and six older children (aged 1.5–13 years) were used to identify focal spikes or sharp waves. The system was evaluated at two levels. First, the performance of the temporal and spatial event detection subsystem cascaded with the coarse spatial event selection stage was computed in terms of sensitivity and selectivity. The events detected by the system were then compared with those marked by the experts. Mean sensitivities of 84.9% and 91.9% and mean selectivities of 86.3% and 90.6% were obtained on neonates and older children, respectively. The results of the spatial clustering for Patient I and all patients are shown in Figs 7 and 8. More details can be found in Aarabi et al. (2009).

Figure 7. (a) Results of the source reconstruction for the events detected by the experts and by the automatic method for Patient I. The dipole cluster for the expert-marked events is represented by a large dotted circle. The dipole clusters for the system-detected events are represented by dashed circles. The average event for the expert-marked and the systemdetected events are shown in (b) and (c), respectively. The dotted ellipses show the channels, on which the spatial clustering of the dipoles could clearly increase the SNR of the average time series, compared to the expert-detected events. The dot–dashed ellipses show the differences between the average time series resulting from spatial clustering of the dipoles. R: right, L: left, A: anterior, P: posterior.

Figure 8. Spatial clustering of the dipoles reconstructed for the events detected by the system. For each patient, the ROI is represented by a dotted circle and the dipole clusters are shown by dashed circles. R: right, L: left.


Aarabi, A., Kazemi, K., Grebe, R., Moghaddam, H.A., Wallois, F., 2009. Detection of EEG transients in neonates and older children using a system based on dynamic time-warping template matching and spatial dipole clustering. Neuroimage 48, 50-62. ​