Detection of generalized tonic-clonic seizures from ear-EEG based on EMG analysis.

PURPOSE: Sudden unexpected death in epilepsy (SUDEP) is associated with generalized tonic-clonic seizures (GTCS) with most deaths occurring during sleep. Seizure detection devices have been suggested as a SUDEP prevention strategy. EMG-based GTCS detection can take advantage of the GTCS characteristic of sustained high-amplitude, high-frequency activity in the time-domain. METHOD: We present a GTCS-detection method based on median-filtered variance estimates on surface EMG measurements and describe its performance in a small exploratory proof-of-concept setting involving a group of 15 patients with 3 GTCS recorded with ear-EEG and another group of 6 patients with 11 GTCS recorded with scalp-EEG. RESULTS: GTCS intervals were detected within 4.2-12.9 s of onset with 100% sensitivity (CI 29.2-100%) without false positives in 820.7 h of ear-EEG. The same detection method worked for the 11 GTCS from scalp EEG data with 100% sensitivity (CI 71.5-100%) and no false positives. CONCLUSIONS: Ear-EEG contains enough GTCS-specific EMG activity for GTCS detection to be feasible. Ear-EEG could be considered for nocturnal GTCS monitoring as a supplement to SUDEP preventive interventions.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

OBJECTIVE: Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. METHODS: We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. RESULTS: There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. CONCLUSIONS: Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. SIGNIFICANCE: Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis.