Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG

Epilepsia. 2021 Aug;62(8):1820-1828. doi: 10.1111/epi.16969. Epub 2021 Jul 12.


Objective: Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study.

Methods: The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models.

Results: sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods.

Significance: The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.

Keywords: long-term EEG monitoring; power spectrum; seizure detection; seizure forecasting; subcutaneous EEG.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Electroencephalography*
  • Epilepsy* / diagnosis
  • Humans
  • Seizures / diagnosis
  • Spectrum Analysis
  • Subcutaneous Tissue