Seizure Detection Devices: Five New Things

Users´ perspectives and preferences on using wearables in epilepsy: A critical review

Seizure detection devices (SDDs) offer promising technological advancements in epilepsy management, providing real-time seizure monitoring and alerts for patients and caregivers. This critical review explores user perspectives and experiences with SDDs to better understand factors influencing their adoption and sustained use. An electronic literature search identified 34 relevant studies addressing common themes such as usability, motivation, comfort, accuracy, barriers, and the financial burden of these devices. Usability emerged as the most frequently discussed factor, with patients and caregivers also emphasizing the importance of ease of use, long battery life, and waterproof design. Although validated devices showed high user satisfaction, technical challenges, false negatives, and false positives need much improvement. Motivation to use SDDs was driven by enhanced safety, symptom tracking, and health care professional recommendations. Comfort and wearability were also critical aspects, with users favoring lightweight, breathable, and discreet designs for long-term wear. Users reported the devices as "comfortable" and preferring wrist or arm-worn devices for the long term. Accuracy-particularly minimizing false positives and false negatives-was a priority for users. Barriers to adoption included device cost, limited insurance reimbursement, discomfort, and concerns about data privacy. Despite these challenges, many users were willing to use SDDs. Recommendations from health care professionals significantly increased user motivation. This review highlights the need for SDD designs that address user concerns regarding usability, comfort, looks, and accuracy, while also reducing financial and technical barriers. Enhancing clinical involvement and tailoring devices to specific patient needs may be crucial to promoting wider SDD adoption. Further research is needed to evaluate the impact of SDDs on quality of life and to explore ways to mitigate challenges in long-term use.

Seizure Detection Devices

Goals of automated detection of epileptic seizures using wearable devices include objective documentation of seizures, prevention of sudden unexpected death in epilepsy (SUDEP) and seizure-related injuries, obviating both the unpredictability of seizures and potential social embarrassment, and finally to develop seizure-triggered on-demand therapies. Automated seizure detection devices are based on the analysis of EEG signals (scalp-EEG, subcutaneous EEG and intracranial EEG), of motor manifestations of seizures (surface EMG, accelerometry), and of physiologic autonomic changes caused by seizures (heart and respiration rate, oxygen saturation, sweat secretion, body temperature). While the detection of generalized tonic-clonic and of focal to bilateral tonic-clonic seizures can be achieved with high sensitivity and low false alarm rates, the detection of focal seizures is still suboptimal, especially in the everyday ambulatory setting. Multimodal seizure detection devices in general provide better performance than devices based on single measurement parameters. Long-term use of seizure detection devices in home environments helps to improve the accuracy of seizure diaries and to reduce seizure-related injuries, while evidence for prevention of SUDEP is still lacking. Automated seizure detection devices are generally well accepted by patients and caregivers.

Seizure quantification in sunflower syndrome by a wrist-worn device

OBJECTIVE

Sunflower syndrome is a rare photosensitive childhood-onset epilepsy, featuring repetitive handwaving events (HWE) triggered by light. Objective documentation of these HWE can be difficult due to the numerous events occurring daily and/or caregivers who document the seizures but are not always present. Hence, seizure diaries can be underreporting.

METHODS

We performed a feasibility study in three Belgian Sunflower syndrome individuals to assess the possibility to quantify the HWE by a wrist-worn wearable device (Axivity AX6). We conducted a structured exercise aiming to capture patterns of possible confounders in a controlled environment. Subsequently, patients wore the device for three to six consecutive days and nights at home. Spectral power analyses were performed to characterize the frequency signature of the different movements.

RESULTS

The HWE of patient A and B showed homogeneity and narrow-band frequencies. Patient C did not experience any HWE at the start of the study due to proper seizure control. Regarding HWE, there was a higher spectral power for Gyroscope Z (Gz) compared to Gy. The inter-subject variability for HWE frequency peaks was in the 3-6 Hz range. Computer analysis by visual annotation, without checking the seizure diary, detected 71% of the HWE if the HWE lasted for longer than 5 s (sensitivity 64%). For shorter HWE duration, the detection rate was 50% but seemed to be higher if there was a concordant change of eye movement (63%) (sensitivity 36%). The most obvious confounder was toothbrushing (TB). However, TB showed a different pattern: that is, higher or comparable spectral power for Gy compared to Gz. There was also a higher or comparable spectral power for Gy compared to Gz for "waving hello".

SIGNIFICANCE

We show that the wearable movement sensor Axivity AX6 can detect HWE in Sunflower syndrome individuals and distinguish them from confounders in a real-world setting.

Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies

A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non-invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS-2 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty-four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist-worn or chest-worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n = 11) or a combination of HR and heart rate variability (HRV) (n = 6). HR-based seizure detection studies collectively reported a sensitivity range of 56%-100% and a false alarm rate (FAR) of 0.02-8/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof-of-principle stage, and evidence for real-time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large-scale longitudinal data. PLAIN LANGUAGE SUMMARY: This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real-time. Therefore, more real-time studies are needed to verify the usage of heart activity-related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.

Ictal vocalizations in the Scn1a+/- mouse model of Dravet syndrome

OBJECTIVE

Ictal vocalizations have shown diagnostic utility in epilepsy patients. Audio recordings of seizures have also been used for seizure detection. The present study aimed to determine whether generalized tonic-clonic seizures in the Scn1a+/- mouse model of Dravet syndrome are associated with either audible mouse squeaks or ultrasonic vocalizations.

METHODS

Acoustic recordings were captured from group-housed Scn1a+/- mice undergoing video-monitoring to quantify spontaneous seizure frequency. We generated audio clips (n = 129) during a generalized tonic-clonic seizure (GTCS) that included 30 seconds immediately prior to the GTCS (preictal) and 30 seconds following the conclusion of the seizure (postictal). Nonseizure clips (n = 129) were also exported from the acoustic recordings. A blinded reviewer manually reviewed the audio clips, and vocalizations were identified as either an audible (<20 kHz) mouse squeak or ultrasonic (>20 kHz).

RESULTS

Spontaneous GTCS in Scn1a+/- mice were associated with a significantly higher number of total vocalizations. The number of audible mouse squeaks was significantly greater with GTCS activity. Nearly all (98%) the seizure clips contained ultrasonic vocalizations, whereas ultrasonic vocalizations were present in only 57% of nonseizure clips. The ultrasonic vocalizations emitted in the seizure clips were at a significantly higher frequency and were nearly twice as long in duration as those emitted in the nonseizure clips. Audible mouse squeaks were primarily emitted during the preictal phase. The greatest number of ultrasonic vocalizations was detected during the ictal phase.

SIGNIFICANCE

Our study shows that ictal vocalizations are exhibited by Scn1a+/- mice. Quantitative audio analysis could be developed as a seizure detection tool for the Scn1a+/- mouse model of Dravet syndrome.

Assessing epilepsy-related autonomic manifestations: Beyond cardiac and respiratory investigations

The Autonomic Nervous System (ANS) regulates many critical physiological functions. Its control relies on cortical input, especially limbic areas, which are often involved in epilepsy. Peri-ictal autonomic dysfunction is now well documented, but inter-ictal dysregulation is less studied. In this review, we discuss the available data on epilepsy-related autonomic dysfunction and the objective tests available. Epilepsy is associated with sympathetic-parasympathetic imbalance and a shift towards sympathetic dominance. Objective tests report alterations in heart rate, baroreflex function, cerebral autoregulation, sweat glands activity, thermoregulation, gastrointestinal and urinary function. However, some tests have found contradictory results and many tests suffer from a lack of sensitivity and reproducibility. Further study on interictal ANS function is required to further understand autonomic dysregulation and the potential association with clinically-relevant complications, including risk of Sudden Unexpected Death In Epilepsy (SUDEP).

The Prospects of Non-EEG Seizure Detection Devices in Dogs

The unpredictable nature of seizures is challenging for caregivers of epileptic dogs, which calls the need for other management strategies such as seizure detection devices. Seizure detection devices are systems that rely on non-electroencephalographic (non-EEG) ictal changes, designed to detect seizures. The aim for its use in dogs would be to provide owners with a more complete history of their dog's seizures and to help install prompt (and potentially life-saving) intervention. Although seizure detection via wearable intracranial EEG recordings is associated with a higher sensitivity in humans, there is robust evidence for reliable detection of generalized tonic-clonic seizures (GTCS) using non-EEG devices. Promising non-EEG changes described in epileptic humans, include heart rate variability (HRV), accelerometry (ACM), electrodermal activity (EDA), and electromyography (EMG). Their sensitivity and false detection rate to detect seizures vary, however direct comparison of studies is nearly impossible, as there are many differences in study design and standards for testing. A way to improve sensitivity and decrease false-positive alarms is to combine the different parameters thereby profiting from the strengths of each one. Given the challenges of using EEG in veterinary clinical practice, non-EEG ictal changes could be a promising alternative to monitor seizures more objectively. This review summarizes various seizure detection devices described in the human literature, discusses their potential use and limitations in veterinary medicine and describes what is currently known in the veterinary literature.

Epilepsy and COVID 2021

Coronavirus 19 (COVID-19) has infected over 400 million people worldwide. Although COVID-19 causes predominantly respiratory symptoms, it can affect other organs including the brain, producing neurological symptoms. People with epilepsy (PWE) have been particularly impacted during the pandemic with decreased access to care, increased stress, and worsening seizures in up to 22% of them probably due to multiple factors. COVID-19 vaccines were produced in a record short time and have yielded outstanding protection with very rare serious side effects. Studies have found that COVID-19 vaccination does not increase seizures in the majority of PWE. COVID-19 does not produce a pathognomonic EEG or seizure phenotype, but rather 1 that can be seen in other types of encephalopathy. COVID-19 infection and its complications can lead to seizures, status epilepticus and post-COVID inflammatory syndrome with potential multi-organ damage in people without pre-existing epilepsy. The lack of access to care during the pandemic has forced patients and doctors to rapidly implement telemedicine. The use of phone videos and smart telemedicine are helping to treat patients during this pandemic and are becoming standard of care. Investment in infrastructure is important to make sure patients can have access to care even during a pandemic.