Evaluation of Dry Sensors for Neonatal EEG Recordings

Abstract

Purpose: Neonatal seizures are a common neurologic diagnosis in neonatal intensive care units, occurring in approximately 14,000 newborns annually in the United States. Although the only reliable means of detecting and treating neonatal seizures is with an electroencephalography (EEG) recording, many neonates do not receive an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include (1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, (2) poor signal quality because of improper skin preparation and artifact, and (3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not previously been evaluated on neonates.

Methods: Sequential and simultaneous recordings with wet and dry sensors were performed for 1 hour on 27 neonates from 35 to 42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined.

Results: Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8), given the nonsuperimposed sensor positions and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically relevant EEG background and seizure patterns. There was no skin injury after 1 hour of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix.

Conclusions: Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury.

Figure 1

(a) Prototype QUASAR dry EEG system with 2 adult-size sensors (S1, S2), common-mode follower (CMF) reference sensor, flat disc dry ground and wireless data acquisition module. (b) Close-up of the dry sensor with two sets of spring-loaded electrode pins and comfort foam. (c) Experimental setup showing wet electrode (T3) applied using conventional techniques, and dry sensor (T5) which was held against the scalp by a bandage. (d) Wet (blue and white) and dry (red) sensor positions during simultaneous recordings. A subset of wet electrodes (blue) was used in quantitative analysis due to their proximity to the dry sensors.

Figure 2

Representative EEG signals recorded with dry and wet systems on subjects 20 (a)-(b) and 26 (c)-(d). Data with dry sensors shows EEG signal in the time-domain that is correlated with wet channels (r = 0.81 between T5-T6 and O1-O2 in (a), r = 0.77 between T5-T6 and P3-P4 in (c)). Corresponding power spectral density (PSD) curves show minor differences between the channels.

Figure 3

Comparison of signals recorded during a seizure on subject 23, showing simultaneously-recorded EEG with adjacent dry (T5-T6) and wet (T3-T4) sensors. (a) Time-domain overlay of both signals shows high correlation (r = 0.84). (b) Power spectral density (PSD) curves are similar for both systems, with dominant peaks at 3.5 Hz. The power at 60 Hz is 6 times smaller for the dry system compared to the wet.

Figure 4

(a) Highest correlation within the quietest 60 second segment between any dry/wet channel and wet/wet channels. (b) Correlations averaged across subjects and shown for different segment lengths.

Figure 5

(a) Average amplitude during the quietest 10 second segment measured by each system. (b) Percent of the data throughout the entire dataset (approx. 1 hr) with amplitude at least 3 times the level in (a). For the wet system, values in (a) and (b) are averaged across the wet channels.