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. 2024 Mar 14;24(6):1867.
doi: 10.3390/s24061867.

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy

Gürkan Yilmaz  1 , Andrea Seiler  2 , Olivier Chételat  1 , Kaspar A Schindler  2
Affiliations

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy

Gürkan Yilmaz et al. Sensors (Basel). .

Abstract

Epilepsy is characterized by the occurrence of epileptic events, ranging from brief bursts of interictal epileptiform brain activity to their most dramatic manifestation as clinically overt bilateral tonic-clonic seizures. Epileptic events are often modulated in a patient-specific way, for example by sleep. But they also reveal temporal patterns not only on ultra- and circadian, but also on multidien scales. Thus, to accurately track the dynamics of epilepsy and to thereby enable and improve personalized diagnostics and therapies, user-friendly systems for long-term out-of-hospital recordings of electrical brain signals are needed. Here, we present two wearable devices, namely ULTEEM and ULTEEMNite, to address this unmet need. We demonstrate how the usability concerns of the patients and the signal quality requirements of the clinicians have been incorporated in the design. Upon testbench verification of the devices, ULTEEM was successfully benchmarked against a reference EEG device in a pilot clinical study. ULTEEMNite was shown to record typical macro- and micro-sleep EEG characteristics in a proof-of-concept study. We conclude by discussing how these devices can be further improved and become particularly useful for a better understanding of the relationships between sleep, epilepsy, and neurodegeneration.

Keywords: dementia; electroencephalography; epilepsy; sleep; wearables.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
ULTEEM device consisting of sensors with active dry electrodes clipped onto the metallic frame of the eyeglasses.
Figure 2
Figure 2
ULTEEMNite device with its central unit where processing, storage, and power management units are located, and the two sensor nodes equipped with active-dry electrodes.
Figure 3
Figure 3
Simplified block diagram for biopotential measurement using a single wire between a measurement sensor and a reference sensor using a pass-through circuit. The pass-through circuit is implemented by an operational amplifier.
Figure 4
Figure 4
A close-up of a pair of ULTEEM clip-on sensors. The white part containing the stainless-steel dry electrodes and a spring backing is formed with a tapering to better adjust to the anatomy of an individual’s temple.
Figure 5
Figure 5
Test setup for verifications: (a) patient auxiliary current and input leakage current, (b) input impedance, (c) acquisition bandwidth, and (d) input-referred noise measurements. MD: measurement device, SG: signal generator, and SW: switch. I: low-impedance current-injection electrode, V: high-impedance voltage-measurement electrode.
Figure 6
Figure 6
Input-referred noise of the devices within 0.5 and 50 Hz bandwidth for a 10-s recording. Only the worst result of 10 consecutive measurements is shown.
Figure 7
Figure 7
Placement of ULTEEM and ULTEEMNite devices with respect to the international 10–20 system. Position of ULTEEM device is marked with orange and of ULTEEMNite device with blue.
Figure 8
Figure 8
(Left column) Comparison of time-domain signals acquired under four different settings (eyes open, eyes closed, eyes moving to the left and right, and swallowing action while eyes are open) by the ULTEEM device (blue curve) and the reference EEG device (Natus SleepWorks) which uses gel electrodes (orange curve). (Right column) Comparison of frequency spectra of the corresponding time-domain signals for each action.
Figure 9
Figure 9
The ULTEEMNite system inserted into a headband and placed on the forehead. The central unit is slightly pulled out of its original place in the headbands pocket to increase its visibility. The device on the nightstand serves as a gateway to download recorded data from the ULTEEMNite device and to then upload it to a secure cloud server.
Figure 10
Figure 10
Night-long recording with ULTEEMNite in a healthy 53-year-old proband clearly reveals the typical macro-and microstructure of sleep EEG. In (A), the recorded EEG signal is analysed by computing the turning rate, which is defined as the probability of all non-monotonous ordinal patterns of length 3 [24,25] for moving windows of 10 s duration (in light gray). The blue line represents results from applying a 5 min moving median filter. The turning rate was proposed as a computationally efficient and robust time-domain-based measure to assess sleep depth, i.e., the lower the turning rate, the deeper the sleep. (B) shows a multi-taper spectrogram [26,27], revealing the typical spectral dynamics and motifs of five sleep cycles, i.e., periodically recurring power increases in the 0.5–4 Hz and 10–14 Hz frequency ranges. Representative EEG time segments of 10 s duration are shown for the non-REM sleep stage N1 in (C), stage N2 in (D) with typical sleep spindles, and for stage N3 with larger amplitude and more regular delta activity in (E). The times when these EEG segments were recorded are indicated by the corresponding labels and small arrows in (B).
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