Seizure occurrence is linked to multiday cycles in diverse physiological signals

Seizure prediction and forecasting: a scoping review

PURPOSE OF REVIEW

This scoping review summarizes key developments in the field of seizure forecasting.

RECENT FINDINGS

Developments have been made along several modalities of seizure forecasting, including long term intracranial and subcutaneous encephalogram, wearable physiologic monitoring, and seizure diaries. However, clinical translation of these tools is limited by various factors. One is the lack of validation of these tools on an external dataset. Moreover, the widespread practice of comparing models to a chance forecaster may be inadequate. Instead, the model should be able to at least surpass a moving average forecaster, which serves as a 'napkin test' (i.e., can be computed on the back of a napkin). The impact of seizure frequency on model performance should also be accounted for when comparing performance across studies. Surprisingly, despite the potential for poor quality forecasts, some individuals with epilepsy still want access to imprecise forecasts and some even alter their behavior based upon them.

SUMMARY

Promising advances have been made in the development of tools for seizure forecasting, but current tools have not yet overcome clinical translation hurdles. Future studies will need to address potentially dangerous patient behaviors as well as account for external validation, the napkin test, seizure frequency dependent metrics.

Movement Disorders and Smart Wrist Devices: A Comprehensive Study

In the medical field, there are several very different movement disorders, such as tremors, Parkinson's disease, or Huntington's disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson's disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington's Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel.

Epilepsy and Cardiac Arrhythmias: A State-of-the-Art Review

Epilepsy is an important cause of disability and mortality worldwide. It can be frequently misdiagnosed, and detailed history and relevant investigations are needed to differentiate epilepsy from syncope. Electroencephalogram is a key noninvasive assessment of neurological function, and the diagnostic yield is increased when performed for an extended period in the ambulatory setting with concurrent electrocardiogram and video monitoring. People living with epilepsy may be affected by a diverse range of ictal (seizure-associated) arrhythmias. The prevalence of detected arrhythmias in people living with epilepsy is anticipated to rise in the context of increasingly available wearable technology and improved survival. Ictal bradycardia and asystole sometimes observed in temporal lobe epilepsy may be associated with falls and injury that can be prevented by cardiac pacing; however, seizure control with medical therapy is still crucial. Sudden unexpected death in epilepsy is likely explained by a different mechanism, in particular central cardiorespiratory autonomic dysfunction sometimes associated with generalized tonic-clonic seizures. Channelopathies encompassing the heart-brain axis are increasingly recognized and may explain the overlap between certain epileptic and arrhythmogenic syndromes, with potential for targeted therapy in the future. The QT interval is prognostically significant in epilepsy, with various seizure types affecting the QT interval differently. Certain antiseizure medications may cause electrocardiogram abnormalities and arrhythmias although there remains limited clinical data. This State-of-the-Art Review describes our current understanding regarding the relationship between epilepsy and cardiac arrhythmias, as well as delineating areas of unmet need.

Diminished circadian and ultradian rhythms of human brain activity in pathological tissue in vivo

Chronobiological rhythms, such as the circadian rhythm, have long been linked to neurological disorders, but it is currently unknown how pathological processes affect the expression of biological rhythms in the brain. Here, we use the unique opportunity of long-term, continuous intracranially recorded EEG from 38 patients (totalling 6338 hours) to delineate circadian (daily) and ultradian (minute to hourly) rhythms in different brain regions. We show that functional circadian and ultradian rhythms are diminished in pathological tissue, independent of regional variations. We further demonstrate that these diminished rhythms are persistent in time, regardless of load or occurrence of pathological events. These findings provide evidence that brain pathology is functionally associated with persistently diminished chronobiological rhythms in vivo in humans, independent of regional variations or pathological events. Future work interacting with, and restoring, these modulatory chronobiological rhythms may allow for novel therapies.

Hippocampal network activity forecasts epileptic seizures

Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.

Rest-Activity Rhythm Phenotypes in Adults with Epilepsy and Intellectual Disability

Objective

Sleep and rest-activity rhythms (RARs) are perturbed in many forms of neuropsychiatric illness. In this study, we applied wrist actigraphy to describe the extent of RAR perturbations in adults with epilepsy and intellectual disability ("E+ID"), using a cross-sectional case-control design. We examined whether RAR phenotypes correlated with epilepsy severity, deficits in adaptive function and/or comorbid psychopathology.

Methods

Primary caregivers of E+ID adults provided informed consent during routine ambulatory clinic visits and were asked to complete standardized surveys of overall epilepsy severity (GASE, Global Assessment of Severity of Epilepsy), adaptive function (ABAS-3, Adaptive Behavior Assessment System-3) and psychopathology (ABCL, Adult Behavior Checklist). Caregivers were also asked to ensure that subjects wore an Actiwatch-2 device continuously on their nondominant wrist for at least ten days. From recorded actograms, we calculated RAR amplitude, acrophase, robustness, intradaily variability (IV), interdaily stability (IS) and estimates of sleep quantity and timing. We compared these RAR metrics against those from (i) a previously published cohort of adults with epilepsy without ID (E-ID), and (ii) a cohort of age- and sex-matched intellectually able subjects measured within the Study of Latinos (SOL) Ancillary actigraphy study (SOL). Within E+ID subjects, we applied k-means analysis to divide subjects into three actigraphically distinct clusters.

Results

46 E+ID subjects (median age 26 [20-68], 47% female) provided a median recording duration of 11 days [range 6-27]. Surveys reflected low to extremely low levels of adaptive function (ABAS3 General Adaptive Composite score: median 50 [49-75]), and low/subclinical levels of psychopathology (ABCL total score: median 54.5 [25-67]). Compared with E-ID (n=57) and SOL (n=156) cohorts, E+ID subjects displayed significantly lower RAR amplitude, robustness and IS, with significantly higher IV and total daily sleep. K-means clustering of E+ID subjects recognized an intermediate cluster "B", with RAR values indistinguishable to E-ID. Cluster "A" subjects displayed pronounced hypoactivity and hypersomnia with high rates of rhythm fragmentation, while cluster "C" subjects featured hyper-robust and high amplitude RARs. All three clusters were similar in age, body mass index, antiseizure medication (ASM) polytherapy, ABAS3 and ABCL scores. We qualitatively describe RAR examples from all three clusters.

Interpretation

We show that adults with epilepsy and intellectual disability display a wide spectrum of RAR phenotypes that do not neatly correlate with measures of adaptive function or epilepsy severity. Prospective studies are necessary to determine whether continuous actigraphic monitoring can sensitively capture changes in chronobiological health that may arise with disease progression, iatrogenesis (e.g., ASM toxicity) or acute health deteriorations (e.g., seizure exacerbation, pneumonia). Similar long-term data is necessary to recognize whether behavioral interventions targeted to 'normalize' RARs may promote improvements in adaptive function and therapy engagement.

The influence of temperature and genomic variation on intracranial EEG measures in people with epilepsy

Heatwaves have serious impacts on human health and constitute a key health concern from anthropogenic climate change. People have different individual tolerance for heatwaves or unaccustomed temperatures. Those with epilepsy may be particularly affected by temperature as the electroclinical hallmarks of brain excitability in epilepsy (inter-ictal epileptiform discharges and seizures) are influenced by a range of physiological and non-physiological conditions. Heatwaves are becoming more common and may affect brain excitability. Leveraging spontaneous heatwaves during periods of intracranial EEG recording in participants with epilepsy in a non-air-conditioned telemetry unit at the National Hospital for Neurology and Neurosurgery in London from May to August 2015-22, we examined the impact of heatwaves on brain excitability. In London, a heatwave is defined as three or more consecutive days with daily maximum temperatures ≥28°C. For each participant, we counted inter-ictal epileptiform discharges using four 10-min segments within, and outside of, heatwaves during periods of intracranial EEG recording. Additionally, we counted all clinical and subclinical seizures within, and outside of, heatwaves. We searched for causal rare genetic variants and calculated the epilepsy PRS. Nine participants were included in the study (six men, three women), median age 30 years (range 24-39). During heatwaves, there was a significant increase in the number of inter-ictal epileptiform discharges in three participants. Five participants had more seizures during the heatwave period, and as a group, there were significantly more seizures during the heatwaves. Genetic data, available for eight participants, showed none had known rare, genetically-determined epilepsies, whilst all had high polygenic risk scores for epilepsy. For some people with epilepsy, and not just those with known, rare, temperature-sensitive epilepsies, there is an association between heatwaves and increased brain excitability. These preliminary data require further validation and exploration, as they raise concerns about the impact of heatwaves directly on brain health.

Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing methods, especially the multi-classification problem. We proposed a multi-modal feature fusion (MMFF) method for epileptic EEG signals. First, the time domain features were extracted by kernel principal component analysis, the frequency domain features were extracted by short-time Fourier extracted transform, and the nonlinear dynamic features were extracted by calculating sample entropy. On this basis, the features of these three modalities were interactively learned through the multi-head self-attention mechanism, and the attention weights were trained simultaneously. The fused features were obtained by combining the value vectors of feature representations, while the time, frequency, and nonlinear dynamics information were retained to screen out more representative epileptic features and improve the accuracy of feature extraction. Finally, the feature fusion method was applied to epileptic EEG signal classifications. The experimental results demonstrated that the proposed method achieves a classification accuracy of 92.76 ± 1.64% across the five-category classification task for epileptic EEG signals. The multi-head self-attention mechanism promotes the fusion of multi-modal features and offers an efficient and novel approach for diagnosing and treating epilepsy.

Autonomic biosignals, seizure detection, and forecasting

Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments.

Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation

OBJECTIVES

Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS.

METHODS

We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up.

RESULTS

Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures).

SIGNIFICANCE

These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.

Ultradian rhythms in accelerometric and autonomic data vary based on seizure occurrence in paediatric epilepsy patients

Ultradian rhythms are physiological oscillations that resonate with period lengths shorter than 24 hours. This study examined the expression of ultradian rhythms in patients with epilepsy, a disease defined by an enduring seizure risk that may vary cyclically. Using a wearable device, we recorded heart rate, body temperature, electrodermal activity and limb accelerometry in patients admitted to the paediatric epilepsy monitoring unit. In our case-control design, we included recordings from 29 patients with tonic-clonic seizures and 29 non-seizing controls. We spectrally decomposed each signal to identify cycle lengths of interest and compared average spectral power- and period-related markers between groups. Additionally, we related seizure occurrence to the phase of ultradian rhythm in patients with recorded seizures. We observed prominent 2- and 4-hour-long ultradian rhythms of accelerometry, as well as 4-hour-long oscillations in heart rate. Patients with seizures displayed a higher peak power in the 2-hour accelerometry rhythm (U = 287, P = 0.038) and a period-lengthened 4-hour heart rate rhythm (U = 291.5, P = 0.037). Those that seized also displayed greater mean rhythmic electrodermal activity (U = 261; P = 0.013). Most seizures occurred during the falling-to-trough quarter phase of accelerometric rhythms (13 out of 27, χ2 = 8.41, P = 0.038). Fluctuations in seizure risk or the occurrence of seizures may interrelate with ultradian rhythms of movement and autonomic function. Longitudinal assessments of ultradian patterns in larger patient samples may enable us to understand how such rhythms may improve the temporal precision of seizure forecasting models.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

Neurostimulation for Generalized Epilepsy: Should Therapy be Syndrome-specific?

Current applications of neurostimulation for generalized epilepsy use a one-target-fits-all approach that is agnostic to the specific epilepsy syndrome and seizure type being treated. The authors describe similarities and differences between the 2 "archetypes" of generalized epilepsy-Lennox-Gastaut syndrome and Idiopathic Generalized Epilepsy-and review recent neuroimaging evidence for syndrome-specific brain networks underlying seizures. Implications for stimulation targeting and programming are discussed using 5 clinical questions: What epilepsy syndrome does the patient have? What brain networks are involved? What is the optimal stimulation target? What is the optimal stimulation paradigm? What is the plan for adjusting stimulation over time?

Value of ultralong-term subcutaneous EEG monitoring for treatment decisions in temporal lobe epilepsy: A case report

Treatment decisions in epilepsy critically depend on information on the course of the disease, its severity and options for specific local interventions. We here report a patient with pharmaco-resistant non-lesional temporal lobe epilepsy with evidence for predominant right temporal epileptogenesis. While seizure frequency had been grossly underestimated for many years, ultralong-term monitoring with a subcutaneous EEG device revealed actual seizure frequency (66 over 11 months vs four patient-documented seizures), providing objective data on treatment efficacy and additional supportive lateralizing information that played a decisive role for the choice of surgical treatment, which had been rejected by the patient prior to this information.

Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics

This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p< 0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p> 0.05). There was a significant difference between ovulating and non-ovulating cycles (p< 0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.

Cyclical underreporting of seizures in patient-based seizure documentation

OBJECTIVE

Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings and in the context of seizure forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence.

METHODS

We retrospectively studied if circadian cycles derived from patient-based reporting reflect the objective seizure documentation in 2003 patients undergoing in-patient video-EEG monitoring.

RESULTS

Only 24.1% of more than 29000 seizures documented were accompanied by patient notifications. There was cyclical underreporting of seizures with a maximum during nighttime, leading to significant deviations in the circadian distribution of seizures. Significant cyclical deviations were found for focal epilepsies originating from both, frontal and temporal lobes, and for different seizure types (in particular, focal unaware and focal to bilateral tonic-clonic seizures).

INTERPRETATION

Patient seizure diaries may reflect a cyclical reporting bias rather than the true circadian seizure distributions. Cyclical underreporting of seizures derived from patient-based reports alone may lead to suboptimal treatment schemes, to an underestimation of seizure-associated risks, and may pose problems for valid seizure forecasting. This finding strongly supports the use of objective measures to monitor cyclical distributions of seizures and for studies and treatment decisions based thereon.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study

BACKGROUND

Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices.

METHOD

Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed.

FINDINGS

The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance.

INTERPRETATION

The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications.

FUNDING

This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.