Subclinical epileptiform discharges in Alzheimer's disease are associated with increased hippocampal blood flow.

BACKGROUND: In epilepsy, the ictal phase leads to cerebral hyperperfusion while hypoperfusion is present in the interictal phases. Patients with Alzheimer's disease (AD) have an increased prevalence of epileptiform discharges and a study using intracranial electrodes have shown that these are very frequent in the hippocampus. However, it is not known whether there is an association between hippocampal hyperexcitability and regional cerebral blood flow (rCBF). The objective of the study was to investigate the association between rCBF in hippocampus and epileptiform discharges as measured with ear-EEG in patients with Alzheimer's disease. Our hypothesis was that increased spike frequency may be associated with increased rCBF in hippocampus. METHODS: A total of 24 patients with AD, and 15 HC were included in the analysis. Using linear regression, we investigated the association between rCBF as measured with arterial spin-labelling MRI (ASL-MRI) in the hippocampus and the number of spikes/sharp waves per 24 h as assessed by ear-EEG. RESULTS: No significant difference in hippocampal rCBF was found between AD and HC (p-value = 0.367). A significant linear association between spike frequency and normalized rCBF in the hippocampus was found for patients with AD (estimate: 0.109, t-value = 4.03, p-value < 0.001). Changes in areas that typically show group differences (temporal-parietal cortex) were found in patients with AD, compared to HC. CONCLUSIONS: Increased spike frequency was accompanied by a hemodynamic response of increased blood flow in the hippocampus in patients with AD. This phenomenon has also been shown in patients with epilepsy and supports the hypothesis of hyperexcitability in patients with AD. The lack of a significant difference in hippocampal rCBF may be due to an increased frequency of epileptiform discharges in patients with AD. TRIAL REGISTRATION: The study is registered at clinicaltrials.gov (NCT04436341).

Assessment of Vestigial Auriculomotor Activity to Acoustic Stimuli Using Electrodes In and Around the Ear.

Recently, it has been demonstrated that electromyographic (EMG) activity of auricular muscles in humans, especially the postauricular muscle (PAM), depends on the spatial location of auditory stimuli. This observation has only been shown using wet electrodes placed directly on auricular muscles. To move towards a more applied, out-of-the-laboratory setting, this study aims to investigate if similar results can be obtained using electrodes placed in custom-fitted earpieces. Furthermore, with the exception of the ground electrode, only dry-contact electrodes were used to record EMG signals, which require little to no skin preparation and can therefore be applied extremely fast. In two experiments, auditory stimuli were presented to ten participants from different spatial directions. In experiment 1, stimuli were rapid onset naturalistic stimuli presented in silence, and in experiment 2, the corresponding participant's first name, presented in a "cocktail party" environment. In both experiments, ipsilateral responses were significantly larger than contralateral responses. Furthermore, machine learning models objectively decoded the direction of stimuli significantly above chance level on a single trial basis (PAM: ≈ 80%, in-ear: ≈ 69%). There were no significant differences when participants repeated the experiments after several weeks. This study provides evidence that auricular muscle responses can be recorded reliably using an almost entirely dry-contact in-ear electrode system. The location of the PAM, and the fact that in-ear electrodes can record comparable signals, would make hearing aids interesting devices to record these auricular EMG signals and potentially utilize them as control signals in the future.

Subclinical Epileptiform Activity in Dementia with Lewy Bodies.

BACKGROUND: Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG). However, no studies have so far investigated the occurrence of interictal epileptiform discharges (IED) in patients with DLB. OBJECTIVES: To investigate if IED as measured with ear-EEG occurs with a higher frequency in patients with DLB compared to healthy controls (HC). METHODS: In this longitudinal observational exploratory study, 10 patients with DLB and 15 HC were included in the analysis. Patients with DLB underwent up to three ear-EEG recordings, each lasting up to 2 days, over a period of 6 months. RESULTS: At baseline, IED were detected in 80% of patients with DLB and in 46.7% of HC. The spike frequency (spikes or sharp waves/24 hours) was significantly higher in patients with DLB as compared to HC with a risk ratio of 2.52 (CI, 1.42-4.61; P-value = 0.001). Most IED occurred at night. CONCLUSIONS: Long-term outpatient ear-EEG monitoring detects IED in most patients with DLB with an increased spike frequency compared to HC. This study extends the spectrum of neurodegenerative disorders in which epileptiform discharges occurs at an elevated frequency. It is possible that epileptiform discharges are, therefore, a consequence of neurodegeneration. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Detection of subclinical epileptiform discharges in Alzheimer's disease using long-term outpatient EEG monitoring.

BACKGROUND: In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC). METHODS: In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months. RESULTS: The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77-5.01, p < 0.001). Most patients with AD (91.7%) showed epileptiform discharges when combining all ear-EEG recordings. CONCLUSIONS: Long-term ear-EEG monitoring detects epileptiform discharges in most patients with AD with a three-fold increased spike frequency compared to HC, which most likely originates from the temporal lobes. Since most patients showed epileptiform discharges with multiple recordings, elevated spike frequency should be considered a marker of hyperexcitability in AD.

Long-term ear-EEG monitoring of sleep - A case study during shift work.

The interest in sleep as a potential clinical biomarker is growing, but the standard method of sleep assessment, polysomnography, is expensive, time consuming, and requires a lot of expert assistance for both set-up and interpretation. To make sleep analysis more available both in research and in the clinic, there is a need for a reliable wearable device for sleep staging. In this case study, we test ear-electroencephalography. A wearable, where electrodes are placed in the outer ear, as a platform for longitudinal at-home recording of sleep. We explore the usability of the ear-electroencephalography in a shift work case with alternating sleep conditions. We find the ear-electroencephalography platform to be reliable both in terms of showing substantial agreement to polysomnography after long-time use (with an overall agreement, using Cohen's kappa, of 0.72) and by being unobtrusive enough to wear during night shift conditions. We find that fractions of non-rapid eye movement sleep and transition probability between sleep stages show great potential as sleep metrics when exploring quantitative differences in sleep architecture between shifting sleep conditions. This study shows that the ear-electroencephalography platform holds great potential as a reliable wearable for quantifying sleep "in the wild", pushing this technology further towards clinical adaptation.

Long-Term EEG Monitoring in Patients with Alzheimer's Disease Using Ear-EEG: A Feasibility Study.

BACKGROUND: Previous studies have reported that epileptiform activity may be detectible in nearly half of patients with Alzheimer's disease (AD) on long-term electroencephalographic (EEG) recordings. However, such recordings can be uncomfortable, expensive, and difficult. Ear-EEG has shown promising results for long-term EEG monitoring, but it has not been used in patients with AD. OBJECTIVE: To investigate if ear-EEG is a feasible method for long-term EEG monitoring in patients with AD. METHODS: In this longitudinal, single-group feasibility study, ten patients with mild to moderate AD were recruited. A total of three ear-EEG recordings of up to 48 hours three months apart for six months were planned. RESULTS: All patients managed to wear the ear-EEG for at least 24 hours and at least one full night. A total of 19 ear-EEG recordings were performed (self-reported recording, mean: 37.15 hours (SD: 8.96 hours)). After automatic pre-processing, a mean of 27.37 hours (SD: 7.19 hours) of data with acceptable quality in at least one electrode in each ear was found. Seven out of ten participants experienced mild adverse events. Six of the patients did not complete the study with three patients not wanting to wear the ear-EEG anymore due to adverse events. CONCLUSION: It is feasible and safe to use ear-EEG for long-term EEG monitoring in patients with AD. Minor adjustments to the equipment may improve the comfort for the participants.

Comparing In-ear EOG for Eye-Movement Estimation With Eye-Tracking: Accuracy, Calibration, and Speech Comprehension.

This presentation details and evaluates a method for estimating the attended speaker during a two-person conversation by means of in-ear electro-oculography (EOG). Twenty-five hearing-impaired participants were fitted with molds equipped with EOG electrodes (in-ear EOG) and wore eye-tracking glasses while watching a video of two life-size people in a dialog solving a Diapix task. The dialogue was directionally presented and together with background noise in the frontal hemisphere at 60 dB SPL. During three conditions of steering (none, in-ear EOG, conventional eye-tracking), participants' comprehension was periodically measured using multiple-choice questions. Based on eye movement detection by in-ear EOG or conventional eye-tracking, the estimated attended speaker was amplified by 6 dB. In the in-ear EOG condition, the estimate was based on one selected channel pair of electrodes out of 36 possible electrodes. A novel calibration procedure introducing three different metrics was used to select the measurement channel. The in-ear EOG attended speaker estimates were compared to those of the eye-tracker. Across participants, the mean accuracy of in-ear EOG estimation of the attended speaker was 68%, ranging from 50 to 89%. Based on offline simulation, it was established that higher scoring metrics obtained for a channel with the calibration procedure were significantly associated with better data quality. Results showed a statistically significant improvement in comprehension of about 10% in both steering conditions relative to the no-steering condition. Comprehension in the two steering conditions was not significantly different. Further, better comprehension obtained under the in-ear EOG condition was significantly correlated with more accurate estimation of the attended speaker. In conclusion, this study shows promising results in the use of in-ear EOG for visual attention estimation with potential for applicability in hearing assistive devices.

Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography.

While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.

Investigation of low dimensional feature spaces for automatic sleep staging.

BACKGROUND: Automatic sleep stage classification depends crucially on the selection of a good set of descriptive features. However, the selection of a feature set with an appropriate low computational cost without compromising classification performance is still a challenge. This study attempts to represent sleep EEG patterns using a minimum number of features, without significant performance loss. METHODS: Three feature selection algorithms were applied to a high dimensional feature space comprising 84 features. These methods were based on a bootstrapping approach guided by Gini ranking and mutual information between the features. The algorithms were tested on three scalp electroencephalography (EEG) and one ear-EEG datasets. The relationship between the information carried by different features was investigated using mutual information and illustrated by a graphical clustering tool. RESULTS: The minimum number of features that can represent the whole feature set without performance loss was found to range between 5 and 11 for different datasets. In ear-EEG, 7 features based on Continuous Wavelet Transform (CWT) resulted in similar performance as the whole set whereas in two scalp EEG datasets, the difference between minimal CWT set and the whole set was statistically significant (0.008 and 0.017 difference in average kappa). Features were divided into groups according to the type of information they carry. The group containing relative power features was identified as the most informative feature group in sleep stage classification, whereas the group containing non-linear features was found to be the least informative. CONCLUSIONS: This study showed that EEG sleep staging can be performed based on a low dimensional feature space without significant decrease in sleep staging performance. This is especially important in the case of wearable devices like ear-EEG where low computational complexity is needed. The division of the feature space into groups of features, and the analysis of the distribution of feature groups for different feature set sizes, is helpful in the selection of an appropriate feature set.

Ear-EEG for sleep assessment: a comparison with actigraphy and PSG.

PURPOSE: To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings. METHODS: Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. The automatic sleep staging was tested on 80 full-night recordings from 20 healthy subjects. The scoring performance and sleep metrics were determined for all ear-EEG setups and the actigraphy device. RESULTS: The single-ear, the single-ear with ipsilateral mastoid setup, and the cross-ear setup performed five class sleep staging with kappa values 0.36, 0.63, and 0.72, respectively. For the single-ear with mastoid electrode and the cross-ear setup, the performance of the sleep metrics, in terms of mean absolute error, was better than the sleep metrics estimated from the actigraphy device in the current study, and also better than current state-of-the-art actigraphy studies. CONCLUSION: A statistically significant improvement in both accuracy and kappa was observed from single-ear to single-ear with ipsilateral mastoid, and from single-ear with ipsilateral mastoid to cross-ear configurations for both two and five-sleep stage classification. In terms of sleep metrics, the results were more heterogeneous, but in general, actigraphy and single-ear with ipsilateral mastoid configuration were better than the single-ear configuration; and the cross-ear configuration was consistently better than both the actigraphy device and the single-ear configuration.

Muscle Activity Detection during Sleep by Ear-EEG.

Muscle activation during sleep is an important biomarker in the diagnosis of several sleep disorders and neurodegenerative diseases. Muscle activity is typically assessed manually based on the EMG channels from polysomnography recordings. Ear-EEG provides a mobile and comfortable alternative for sleep assessment. In this study, ear-EEG was used to automatically detect muscle activities during sleep. The study was based on a dataset comprising four full night recordings from 20 healthy subjects with concurrent polysomnography and ear-EEG. A binary label, active or relax, extracted from the chin EMG was assigned to selected 30 s epoch of the sleep recordings in order to train a classifier to predict muscle activation. We found that the ear-EEG based classifier detected muscle activity with an accuracy of 88% and a Cohen's kappa value of 0.71 relative to the labels derived from the chin EMG channels. The analysis also showed a significant difference in the distribution of muscle activity between REM and non-REM sleep.

Absolute Eye Gaze Estimation With Biosensors in Hearing Aids.

People with hearing impairment typically have difficulties following conversations in multi-talker situations. Previous studies have shown that utilizing eye gaze to steer audio through beamformers could be a solution for those situations. Recent studies have shown that in-ear electrodes that capture electrooculography in the ear (EarEOG) can estimate the eye-gaze relative to the head, when the head was fixed. The head movement can be estimated using motion sensors around the ear to create an estimate of the absolute eye-gaze in the room. In this study, an experiment was designed to mimic a multi-talker situation in order to study and model the EarEOG signal when participants attempted to follow a conversation. Eleven hearing impaired participants were presented speech from the DAT speech corpus (Bo Nielsen et al., 2014), with three targets positioned at -30°, 0° and +30° azimuth. The experiment was run in two setups: one where the participants had their head fixed in a chinrest, and the other where they were free to move their head. The participants' task was to focus their visual attention on an LED-indicated target that changed regularly. A model was developed for the relative eye-gaze estimation, taking saccades, fixations, head movement and drift from the electrode-skin half-cell into account. This model explained 90.5% of the variance of the EarEOG when the head was fixed, and 82.6% when the head was free. The absolute eye-gaze was also estimated utilizing that model. When the head was fixed, the estimation of the absolute eye-gaze was reliable. However, due to hardware issues, the estimation of the absolute eye-gaze when the head was free had a variance that was too large to reliably estimate the attended target. Overall, this study demonstrated the potential of estimating absolute eye-gaze using EarEOG and motion sensors around the ear.

A study of evoked potentials from ear-EEG.

A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over steady-state and transient paradigms, and across a population of subjects. The respective steady-state responses are evaluated in terms of signal-to-noise ratio and statistical significance, while the qualitative analysis of the transient responses is performed by considering grand averaged event-related potential (ERP) waveforms. The outcomes of this study demonstrate conclusively that the ear-EEG signals, in terms of the signal-to-noise ratio, are on par with conventional EEG recorded from electrodes placed over the temporal region.