Identify seizure cycles with ultra long-term EEG monitoring

Subcutaneous EEG uncovers seizure risk cycles in patients

BENEFITS OF OUR SOLUTION

Identify seizure cycles

Subcutaneous EEG technology is suitable for counting seizures over long periods of time.

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Evidence indicates the presence of multidien cycles in as many as 60 percent of patients with focal epilepsy. With current scalp EEG options, these cycles will escape detection due to short recording timeframes. Subcutaneous EEG has made this option a reality.

In a 230-day case study from King’s College London, ultra long-term EEG monitoring revealed two seizure risk cycles: the circadian cycle was visible in both EEG and patient diary data, while the multiday cycle was visible only in the EEG data.

Signal quality

The EEG signal quality provided by the 24/7 EEG SubQ solution compares with that of scalp electrodes and is maintained over months. High signal quality is demonstrated with low impedance and high stability. This reinforces that the subcutaneous EEG technology is suitable for counting seizures over long periods of time, thus uncovering seizure fluctuations and seizure risk cycles in patients.

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Demonstrating high signal quality

Adapted from Viana et al. Epilepsia, 2021

Top: dark blue line, blue-shaded and grey-shaded areas represent respectively, the mean, standard error, and range of daily impedance measurements for each subject.

Bottom: size of the impedance measurements database as it evolves over time, depending on the recording duration of each subject.

Read study on signal quality

Clinical publications

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

Pedro F. Viana, Line S. Remvig, Jonas Duun-Henriksen, Martin Glasstetter, Matthias Dümpelmann, Ewan S. Nurse, Isabel P. Martins, Andreas Schulze-Bonhage, Dean R. Freestone, Benjamin H. Brinkmann, Troels W. Kjaer, Mark P. Richardson. Epilepsia 2021

230 days of ultra long-term subcutaneous EEG: seizure cycle analysis and comparison to patient diary

Pedro F. Viana, Jonas Duun-Henriksen, Martin Glasstëter, Matthias Dümpelmann, Ewan S. Nurse, Isabel P. Martins, Sonya B. Dumanis, Andreas Schulze-Bonhage, Dean R. Freestone, Benjamin H. Brinkmann & Mark P. Richardson. Annals of Clinical and Translational Neurology 2020.

Other benefits

Detect treatment effect with objective seizure counting

Reliable measurement of treatment effect

Rapid, automated analysis

On-demand EEG data access

Manage your patients remotely

Geographical barriers are removed

Proven high adherence rate

Empower patients

New insights with real-life data

Motivate patients

Identify seizure cycles

Ultra long-term EEG monitoring

High data quality over time

Disclaimer

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