Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG
Summary Objective Ultra long-term subcutaneous EEG (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 fourteen patients with epilepsy and twelve 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 approximately 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, while 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.Key Points <jats:list list-type="bullet">Subcutaneous EEG shows similar spectral characteristics to scalp EEGThe subcutaneous EEG signal is highly stable throughout weeks and months of recordingSubcutaneous EEG systems are well suited for chronic implantation, for seizure detection and seizure forecasting.
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Preprint| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
bioRxiv.org - (2021) vom: 15. Dez. Zur Gesamtaufnahme - year:2021 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Viana, Pedro F. [VerfasserIn] |
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| doi: |
10.1101/2021.04.15.21255388 |
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| PPN (Katalog-ID): |
XBI020375840 |
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| 520 | |a Summary Objective Ultra long-term subcutaneous EEG (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 fourteen patients with epilepsy and twelve 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 approximately 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, while 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.Key Points <jats:list list-type="bullet">Subcutaneous EEG shows similar spectral characteristics to scalp EEGThe subcutaneous EEG signal is highly stable throughout weeks and months of recordingSubcutaneous EEG systems are well suited for chronic implantation, for seizure detection and seizure forecasting | ||
| 700 | 1 | |a Remvig, Line S. |e verfasserin |4 aut | |
| 700 | 1 | |a Duun-Henriksen, Jonas |e verfasserin |4 aut | |
| 700 | 1 | |a Glasstetter, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Dümpelmann, Matthias |e verfasserin |4 aut | |
| 700 | 1 | |a Nurse, Ewan S. |e verfasserin |4 aut | |
| 700 | 1 | |a Martins, Isabel P. |e verfasserin |4 aut | |
| 700 | 1 | |a Schulze-Bonhage, Andreas |e verfasserin |4 aut | |
| 700 | 1 | |a Freestone, Dean R. |e verfasserin |4 aut | |
| 700 | 1 | |a Brinkmann, Benjamin H. |e verfasserin |4 aut | |
| 700 | 1 | |a Kjaer, Troels W. |e verfasserin |4 aut | |
| 700 | 1 | |a Richardson, Mark P. |e verfasserin |4 aut | |
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