Electrocardiographic changes at the onset of epileptic seizures

Cardiorespiratory disturbances in focal impaired awareness seizures: Insights from wearable ECG monitoring

PURPOSE

Seizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA).

METHODS

We extracted ECG signals across three time points - five minutes baseline and preictal, ten minutes postictal - and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means.

RESULTS

The mean preictal heart rate was 69.95 bpm (95 % CI 65.6 - 74.3), and it increased to 82 bpm, 95 % CI (77.51 - 86.47) and 84.11 bpm, 95 % CI (76.9 - 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 - 17.1), 14.8 (95 % CI 13.4 - 16.2) and 15.1 (95 % CI 14 - 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures.

CONCLUSIONS

We found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.

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.

Epileptic seizure prediction based on features extracted from lagged Poincaré plots

OBJECTIVE

The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR).

METHODS

In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points.

RESULTS

The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor.

CONCLUSION

The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

Differentiation of subclinical and clinical electrographic events in long-term electroencephalographic recordings

OBJECTIVE

With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events.

METHODS

EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts.

RESULTS

Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals.

SIGNIFICANCE

A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Wrist-worn smartwatch and predictive models for seizures

OBJECTIVE

This study was undertaken to describe extracerebral biosignal characteristics of overall and various seizure types as compared with baseline physical activities using multimodal devices (Empatica E4); develop predictive models for overall and each seizure type; and assess diagnostic performance of each model.

METHODS

We prospectively recruited patients with focal epilepsy who were admitted to the epilepsy monitoring unit for presurgical evaluation during January to December 2020. All study participants were simultaneously applied gold standard long-term video-electroencephalographic (EEG) monitoring and an index test, E4. Two certified epileptologists independently determined whether captured events were seizures and then indicated ictal semiology and EEG information. Both were blind to multimodal biosignal findings detected by E4. Biosignals during 5-min epochs of both seizure events and baseline were collected and compared. Predictive models for occurrence overall and of each seizure type were developed using a generalized estimating equation. Diagnostic performance of each model was then assessed.

RESULTS

Thirty patients had events recorded and were recruited for analysis. One hundred eight seizure events and 120 baseline epochs were collected. Heart rate (HR), acceleration (ACC), and electrodermal activity (EDA) but not temperature were significantly elevated during seizures. Cluster analysis showed trends of greatest elevation of HR and ACC in bilateral tonic-clonic seizures (BTCs), as compared with non-BTCs and isolated auras. HR and ACC were independent predictors for overall seizure types, BTCs, and non-BTCs, whereas only HR was a predictor for isolated aura. Diagnostic performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the predictive model for overall seizures were 77.78%, 60%, and .696 (95% confidence interval = .628-.764), respectively.

SIGNIFICANCE

Multimodal extracerebral biosignals (HR, ACC, EDA) detected by a wrist-worn smartwatch can help differentiate between epileptic seizures and normal physical activities. It would be worthwhile to implement our predictive algorithms in commercial seizure detection devices. However, larger studies to externally validate our predictive models are required.

The Heart and Seizures: Friends or Enemies?

The heart and seizures are closely linked by an indissoluble relationship that finds its basis in the cerebral limbic circuit whose mechanisms remain largely obscure. The differential diagnosis between seizures and syncopes has always been a cornerstone of the collaboration between cardiologists and neurologists and is renewed as a field of great interest for multidisciplinary collaboration in the era of the diffusion of prolonged telemonitoring units. The occurrence of ictal or post-ictal arrhythmias is currently a cause of great scientific debate with respect to the role and risks that these complications can generate (including sudden unexpected death in epilepsy). Furthermore, the study of epileptic seizures and the arrhythmological complications they cause (during and after seizures) also allows us to unravel the mechanisms that link them. Finally, intercritical arrhythmias may represent great potential in terms of the prevention of cardiological risk in epileptic patients as well as in the possible prediction of the seizures themselves. In this paper, we review the pertaining literature on this subject and propose a scheme of classification of the cases of arrhythmia temporally connected to seizures.

Sudden unexpected death in epilepsy: A critical view of the literature

Sudden unexpected death in epilepsy (SUDEP) is a sudden, unexpected, witnessed or unwitnessed, non-traumatic and non-drowning death, occurring in benign circumstances, in an individual with epilepsy, with or without evidence for a seizure and excluding documented status epilepticus in which postmortem examination does not reveal other causes of death. Lower diagnostic levels are assigned when cases met most or all of these criteria, but data suggested more than one possible cause of death. The incidence of SUDEP ranged from 0.09 to 2.4 per 1000 person-years. Differences can be attributed to the age of the study populations (with peaks in the 20-40-year age group) and the severity of the disease. Young age, disease severity (in particular, a history of generalized TCS), having symptomatic epilepsy, and the response to antiseizure medications (ASMs) are possible independent predictors of SUDEP. The pathophysiological mechanisms are not fully known due to the limited data available and because SUDEP is not always witnessed and has been electrophysiologically monitored only in a few cases with simultaneous assessment of respiratory, cardiac, and brain activity. The pathophysiological basis of SUDEP may vary according to different circumstances that make that particular seizure, in that specific moment and in that patient, a fatal event. The main hypothesized mechanisms, which could contribute to a cascade of events, are cardiac dysfunction (included potential effects of ASMs, genetically determined channelopathies, acquired heart diseases), respiratory dysfunction (included postictal arousal deficit for the respiratory mechanism, acquired respiratory diseases), neuromodulator dysfunction, postictal EEG depression and genetic factors.

Generalized Epilepsy With Repetitive Sinus Pauses Following Generalized Tonic-Clonic Seizures Due to Reduced Baroreflex Sensitivity

Epilepsy and syncope are sometimes difficult to differentiate, and they often occur together. We report here a unique case of severe neuromodulatory syncope associated with generalized epilepsy. A 24-year-old right-handed female with no remarkable history had her first epileptic seizure when she was 15 years old and was diagnosed with epilepsy. However, she had epileptic seizures or fainting spells every few months and was referred to Nara Medical Center at the age of 23 years. No obvious neurological abnormality was present, and no organic abnormality was found on head magnetic resonance imaging. The seizures were symmetrical generalized tonic-clonic seizures (GTCS) without aura, and the patient was unable to stand up for several hours after the seizure. Long-term video electroencephalogram monitoring revealed two types of seizures: (1) GTCS starting with generalized polyspikes and waves and (2) fainting with sinus arrest for up to 10 seconds when the patient tried to stand up after GTCS. After the addition of valproic acid following the diagnosis of generalized epilepsy, her epileptic seizures improved, but syncope remained. We consulted the cardiology department of our hospital and diagnosed mixed neuromodulatory syncope after performing the tilt test. She underwent catheter ablation for cardioneuromodulation, and her syncope improved. Several reports have described reduced baroreflex sensitivity during the interictal period in epilepsy, and seizure-related autonomic dysfunction has been implicated in sudden unexpected death in epilepsy (SUDEP). In addition to suppression of epileptic seizures, when autonomic nervous system symptoms associated with epilepsy are severe, as in this case, a thorough cardiovascular examination should be performed, and the patient should be treated with the goal of preventing SUDEP.

Electrocardiographic Changes at the Early Stage of Status Epilepticus: First Insights From the ICTAL Registry

OBJECTIVES

To describe early electrocardiogram (ECG) abnormalities after status epilepticus (SE) and evaluate their association with 90-day neurological outcomes.

DESIGN

Retrospective analysis of a multicenter, national prospective registry between February 2018 and June 2020.

SETTING

Sixteen ICUs in France, IctalGroup Research Network.

PATIENTS

Adults with available ECG performed less than or equal to 24 hours after the onset of SE and less than or equal to 12 hours after its resolution.

INTERVENTION

Double-blinded review of all ECGs was performed by two independent cardiologists. ECGs were categorized as normal/abnormal and then with minor/major early ECG abnormalities according to the Novacode ECG Classification system.

MEASUREMENTS AND MAIN RESULTS

Among 155 critically ill patients with SE, early ECG abnormalities were encountered in 145 (93.5%), categorized as major in 91 of 145 (62.8%). In addition to sinus tachycardia, the main abnormalities were in the ST segment (elevation [16.6%] or depression [17.9%]) or negative T waves (42.1%). Major early ECG abnormalities were significantly associated with respiratory distress and sinus tachycardia at the scene and hyperlactatemia at ICU admission. By multivariable analysis, three variables were significantly associated with 90-day poor outcome: age, preexisting ultimately fatal comorbidity, and cerebral insult as the cause of SE. Early major ECG abnormalities were not independently associated with 90-day functional outcome.

CONCLUSIONS

In our study, early ECG abnormalities in the acute phase of SE were frequent, often unrecognized and were associated with clinical and biological stigma of hypoxemia. Although they were not independently associated with 90-day functional outcome, ECG changes at the early stage of SE should be systematically evaluated.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT03457831 .

Central control of cardiac activity as assessed by intra-cerebral recordings and stimulations

Some of the most important integrative control centers for the autonomic nervous system are located in the brainstem and the hypothalamus. However, growing recent neuroimaging evidence support that a set of cortical regions, named the central autonomic network (CAN), is involved in autonomic control and seems to play a major role in continuous autonomic cardiac adjustments to high-level emotional, cognitive or sensorimotor cortical activities. Intracranial explorations during stereo-electroencephalography (SEEG) offer a unique opportunity to address the question of the brain regions involved in heart-brain interaction, by studying: (i) direct cardiac effects produced by the electrical stimulation of specific brain areas; (ii) epileptic seizures inducing cardiac modifications; (iii) cortical regions involved in cardiac interoception and source of cardiac evoked potentials. In this review, we detail the available data assessing cardiac central autonomic regulation using SEEG, address the strengths and also the limitations of this technique in this context, and discuss perspectives. The main cortical regions that emerge from SEEG studies as being involved in cardiac autonomic control are the insula and regions belonging to the limbic system: the amygdala, the hippocampus, and the anterior and mid-cingulate. Although many questions remain, SEEG studies have already demonstrated afferent and efferent interactions between the CAN and the heart. Future studies in SEEG should integrate these afferent and efferent dimensions as well as their interaction with other cortical networks to better understand the functional heart-brain interaction.

Heart Rate Variability in Children with Moderate and Severe Traumatic Brain Injury: A Prospective Observational Study

The aim of this study was to assess the feasibility of continuous monitoring of heart rate variability (HRV) in children with traumatic brain injury (TBI) hospitalized in a pediatric intensive care unit (PICU) and collect preliminary data on the association between HRV, neurological outcome, and complications. This is a prospective observational cohort study in a tertiary academic PICU. Children admitted to the PICU ≤24 hours after moderate or severe TBI were included in the study. Children suspected of being brain dead at PICU entry or with a pacemaker were excluded. Children underwent continuous monitoring of electrocardiographic (ECG) waveforms over 7 days post-TBI. HRV analysis was performed retrospectively, using a standardized, validated HRV analysis software (CIMVA). The occurrence of medical complications (“event”: intracranial hypertension, cerebral hypoperfusion, seizure, and cardiac arrest) was prospectively documented. Outcome of children 6 months post-TBI was assessed using the Glasgow Outcome Scale – Extended Pediatric (GOS-E Peds). Fifteen patients were included over a 20-month period. Thirteen patients had ECG recordings available and 4 had >20% of missing ECG data. When ECG was available, HRV calculation was feasible (average 88%; range 70–97%). Significant decrease in overall HRV coefficient of variation and Poincaré SD2 (p < 0.05) at 6 hours post–PICU admission was associated with an unfavorable outcome (defined as GOS-E Peds ≥ 3, or a deterioration of ≥2 points over baseline score). Several HRV metrics exhibited significant and nonsignificant variation in HRV during event. This study demonstrates that it is feasible to monitor HRV in the PICU provided ECG data are available; however, missing ECG data are not uncommon. These preliminary data suggest that altered HRV is associated with unfavorable neurological outcome and in-hospital medical complications. Larger prospective studies are needed to confirm these findings and to explore if HRV offers reliable and clinically useful prediction data that may help clinical decision making.

Longitudinally tracking personal physiomes for precision management of childhood epilepsy

Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.

Ictal chest discomfort in a patient with temporal lobe seizures and amygdala enlargement

Chest discomfort is the representative symptom of dangerous coronary artery disease (CAD), but rarely occurs in patients with seizures. We treated a 74-year-old man with right mesial temporal lobe epilepsy and amygdala enlargement, who was initially suspected of CAD and underwent repeated cardiac angiography because of recurrent episodes of paroxysmal chest discomfort starting from 68 years old. He visited an epileptologist and underwent long-term video electroencephalography monitoring (LTVEM), which confirmed right temporal seizure onset during a habitual episodes of "chest discomfort," stereotyped movement of chest rubbing with the right hand, followed by impaired conscousness. Brain magnetic resonance imaging revealed right amygdala enlargement. The present case emphasizes the importance of the wide range of symptoms, such as chest discomfort, which may associated with epielpsy and result in a delayed diagnosis. LTVEM is useful for diagnosis of epilepsy with unusual seizure semiology by recording ictal EEG changes during chest discomfort.

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.

Protocols for multimodal polygraphy for cardiorespiratory monitoring in the epilepsy monitoring unit. Part I: Clinical acquisition

Multimodal polygraphy including cardiorespiratory monitoring in the Epilepsy Monitoring is becoming increasingly important. In addition to simultaneous recording of video and EEG, the combination of these techniques not only improves seizure detection, it enhances patient safety and provides information on autonomic clinical symptoms, which may be contributory to localization of seizure foci. However, there are currently no consensus guidelines, nor adequate information on devices available for multimodal polygraphy for cardiorespiratory monitoring in the Epilepsy Monitoring Unit. Our purpose here is to provide protocols and information on devices for such monitoring. Suggested parameters include respiratory inductance plethysmography (thoraco-abdominal belts for respiratory rate), pulse oximetry and four-lead electrocardiography. Detailed knowledge of devices, their operability and acquisition optimization enables accurate interpretation of signal and differentiation of abnormalities from artifacts. Multimodal polygraphy brings new opportunities for identification of peri-ictal cardiorespiratory abnormalities, and may identify high SUDEP risk individuals.

Seizures detection using multimodal signals: a scoping review

INTRODUCTION

Epileptic seizures are common neurological disorders in the world, impacting 50 million people globally. Around 30% of patients with seizures suffer from refractory epilepsy, where seizures are not controlled by medications. The unpredictability of seizures makes it essential to have a continuous seizure monitoring system outside clinical settings for the purpose of minimizing patients' injuries and providing additional pathways for evaluation and treatment follow-up. Autonomic changes related to seizure events have been extensively studied and attempts made to apply them for seizure detection and prediction tasks. This scoping review aims to depict current research activities associated with the implementation of portable, wearable devices for seizure detection or prediction and inform future direction in continuous seizure tracking in ambulatory settings.

METHODS

Overall methodology framework includes 5 essential stages: research questions identification, relevant studies identification, selection of studies, data charting and summarizing the findings. A systematic searching strategy guided by systematic reviews and meta-analysis (PRISMA) was implemented to identify relevant records on two databases (PubMed, IEEE).

RESULTS

A total of 30 articles were included in our final analysis. Most of the studies were conducted off-line and employed consumer-graded wearable device. ACM is the dominant modality to be used in seizure detection, and widely deployed algorithms entail Support Vector Machine, Random Forest and threshold-based approach. The sensitivity ranged from 33.2% to 100% for single modality with a false alarm rate (FAR) ranging from 0.096 /day to 14.8 /day. Multimodality has a sensitivity ranging from 51% to 100% with FAR ranging from 0.12/day - 17.7/day.

CONCLUSION

The overall performance in seizure detection system based on non-cerebral physiological signals is promising, especially for the detection of motor seizures and seizures accompanied with intense ictal autonomic changes.

Seizure semiology: ILAE glossary of terms and their significance

This educational topical review and Task Force report aims to address learning objectives of the International League Against Epilepsy (ILAE) curriculum. We sought to extract detailed features involving semiology from video recordings and interpret semiological signs and symptoms that reflect the likely localization for focal seizures in patients with epilepsy. This glossary was developed by a working group of the ILAE Commission on Diagnostic Methods incorporating the EEG Task Force. This paper identifies commonly used terms to describe seizure semiology, provides definitions, signs and symptoms, and summarizes their clinical value in localizing and lateralizing focal seizures based on consensus in the published literature. Video-EEG examples are included to illustrate important features of semiology in patients with epilepsy.

The role of monoaminergic neurons in modulating respiration during sleep and the connection with SUDEP

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death among epilepsy patients, occurring even more frequently in cases with anti-epileptic drug resistance. Despite some advancements in characterizing SUDEP, the underlying mechanism remains incompletely understood. This review summarizes the latest advances in our understanding of the pathogenic mechanisms of SUDEP, in order to identify possible targets for the development of new strategies to prevent SUDEP. Based on our previous research along with the current literature, we focus on the role of sleep-disordered breathing (SDB) and its related neural mechanisms to consider the possible roles of monoaminergic neurons in the modulation of respiration during sleep and the occurrence of SUDEP. Overall, this review suggests that targeting the monoaminergic neurons is a promising approach to preventing SUDEP. The proposed roles of SDB and related monoaminergic neural mechanisms in SUDEP provide new insights for explaining the pathogenesis of 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.

Serious Cardiac Arrhythmias Detected by Subcutaneous Long-term Cardiac Monitors in Patients With Drug-Resistant Epilepsy

BACKGROUND AND OBJECTIVES

Epilepsy is associated with an increased risk of cardiovascular disease and premature mortality, including sudden unexpected death in epilepsy (SUDEP). Serious cardiac arrythmias might go undetected in routine epilepsy and cardiac investigations.

METHODS

This prospective cohort study aimed to detect cardiac arrhythmias in patients with chronic drug-resistant epilepsy (≥5 years duration) using subcutaneous cardiac monitors for a minimum follow-up duration of 12 months. Participants with known cardiovascular disease or those with abnormal 12-lead ECGs were excluded. The device was programmed to automatically record episodes of tachycardia ≥140 beats per minute (bpm), bradycardia ≤40 bpm for ≥3 seconds, or asystole ≥3 seconds.

FINDINGS

Thirty-one patients underwent subcutaneous cardiac monitoring for a median recording duration of 2.2 years (range 0.5-4.2). During this time, 28 patients (90.3%) had episodes of sustained (≥30 seconds) sinus tachycardia, 8/31 (25.8%) had sinus bradycardia, and 3 (9.7%) had asystole. Three patients (9.7%) had serious cardiac arrhythmias requiring additional cardiac interventions. Among them, 2 patients had prolonged sinus arrest and ventricular asystole (>6 seconds), leading to pacemaker insertion in one, and another patient with epileptic encephalopathy had multiple episodes of recurrent nonsustained polymorphic ventricular tachycardia and bundle branch conduction abnormalities. The time to first detection of a clinically significant cardiac arrhythmia ranged between 1.2 and 26.9 months following cardiac monitor insertion.

DISCUSSION

Implantable cardiac monitors detected a high incidence of clinically significant cardiac arrhythmias in patients with chronic drug-resistant epilepsy, which may contribute to the incidence of premature mortality, including SUDEP.

Vagus nerve stimulation for conservative therapy-refractive epilepsy and depression

Numerous studies confirm that the vagus nerve stimulation (VNS) is an efficient, indirect neuromodulatory therapy with electrically induced current for epilepsy that cannot be treated by epilepsy surgery and is therapy-refractory and for drug therapy-refractory depression. VNS is an established, evidence-based and in the long-term cost-effective therapy in an interdisciplinary overall concept.Long-term data on the safety and tolerance of the method are available despite the heterogeneity of the patient populations. Stimulation-related side effects like hoarseness, paresthesia, cough or dyspnea depend on the stimulation strength and often decrease with continuing therapy duration in the following years. Stimulation-related side effects of VNS can be well influenced by modifying the stimulation parameters. Overall, the invasive vagus nerve stimulation may be considered as a safe and well-tolerated therapy option.For invasive and transcutaneous vagus nerve stimulation, antiepileptic and antidepressant as well as positive cognitive effects could be proven. In contrast to drugs, VNS has no negative effect on cognition. In many cases, an improvement of the quality of life is possible.iVNS therapy has a low probability of complete seizure-freedom in cases of focal and genetically generalized epilepsy. It must be considered as palliative therapy, which means that it does not lead to healing and requires the continuation of specific medication. The functional principle is a general reduction of the neuronal excitability. This effect is achieved by a slow increase of the effectiveness sometimes over several years. Responders are those patients who experience a 50% reduction of the seizure incidence. Some studies even reveal seizure-freedom in 20% of the cases. Currently, it is not possible to differentiate between potential responders and non-responders before therapy/implantation.The current technical developments of the iVNS generators of the new generation like closed-loop system (cardiac-based seizure detection, CBSD) reduce also the risk for SUDEP (sudden unexpected death in epilepsy patients), a very rare, lethal complication of epilepsies, beside the seizure severity.iVNS may deteriorate an existing sleep apnea syndrome and therefore requires possible therapy interruption during nighttime (day-night programming or magnet use) beside the close cooperation with sleep physicians.The evaluation of the numerous iVNS trials of the past two decades showed multiple positive effects on other immunological, cardiological, and gastroenterological diseases so that additional therapy indications may be expected depending on future study results. Currently, the vagus nerve stimulation is in the focus of research in the disciplines of psychology, immunology, cardiology as well as pain and plasticity research with the desired potential of future medical application.Beside invasive vagus nerve stimulation with implantation of an IPG and an electrode, also devices for transdermal and thus non-invasive vagus nerve stimulation have been developed during the last years. According to the data that are currently available, they are less effective with regard to the reduction of the seizure severity and duration in cases of therapy-refractory epilepsy and slightly less effective regarding the improvement of depression symptoms. In this context, studies are missing that confirm high evidence of effectiveness. The same is true for the other indications that have been mentioned like tinnitus, cephalgia, gastrointestinal complaints etc. Another disadvantage of transcutaneous vagus nerve stimulation is that the stimulators have to be applied actively by the patients and are not permanently active, in contrast to implanted iVNS therapy systems. So they are only intermittently active; furthermore, the therapy adherence is uncertain.

Hemisphere-Dependent Ictal Tachycardia Versus Ictal Bradycardia in a Critically Ill Patient

SUMMARY

Tachycardia is a common ictal phenomenon; however, ictal bradycardia is less commonly reported and rarely presents as ictal asystole/syncope. In critically ill patients, seizures are much less likely to manifest with overt clinical signs, i.e., are more likely to be subtle or nonconvulsive. In this setting, changes in heart rate may be the only clue that seizures are occurring. The authors report an exemplary case of a 78-year-old right-handed man who presented with spontaneous left frontal intraparenchymal hemorrhages. During standard clinical monitoring in the Neuro-Intensive Care Unit, the patient had discrete paroxysms of relative sinus tachycardia, independent episodes of sinus bradycardia, and 3 to 4 seconds of sinus pause. The cardiac investigation was unrevealing, but continuous EEG revealed the answer. The episodes of mild tachycardia were associated with seizures from the left temporal region, whereas those with bradycardia were associated with independent seizures from the right temporal region. The case stands as a stark reminder to remain vigilant of seizures in high-risk patients, especially as a cause for paroxysmal autonomic changes.

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

BACKGROUND

Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

METHODS

Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

RESULTS

These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

CONCLUSION

The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

A multimodal AI system for out-of-distribution generalization of seizure identification

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

Alice in Wonderland Syndrome-Like Seizure and Refractory Supraventricular Tachycardia

Alice in Wonderland syndrome (AIWS) is a rarely curious visual perceptual disorder which has been associated with diverse neurologic and psychiatric problems. It may be a manifestation in migraine, epileptic seizures, encephalitis, other brain lesions, medication-related side effects, schizophrenia, and depressive disorders. Principal character of AIWS is the disproportion between the external world and the self-image in which micropsia (objects appear smaller), macropsia (objects appear larger), and teleopsia (objects appear further away) are frequently reported. The cases of temporal lobe epilepsy may present with complex visual auras of visual distortions (e.g., micropsia and macropsia) like AIWS. We report an unusual case of an elderly man who presented with AIWS, focal impaired awareness seizures, ictal tachyarrhythmia, multiple episodes of transient visual disturbances of macropsia and transient loss of consciousness. During those symptoms, telemetry showed self-limited supraventricular tachycardia several times which could not be regulated with heart rate-controlled medication. The electroencephalography was later tested and showed rhythmic theta activity over the right cerebral hemisphere. He was treated with levetiracetam, and all his symptoms and tachyarrhythmias were gradually resolved thereafter. Refractory response to treatment would remind the physicians to reassess for the correct diagnosis.

Interictal autonomic dysfunction in patients with epilepsy

Background

Autonomic nervous system (ANS) symptoms are frequently present in people with epilepsy (PwE). They are generally more prominent when they originate from the temporal lobe. We aim to investigate the alterations of autonomic functions during the interictal period in patient with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) using heart-based tests, blood pressure (BP)-based tests and sympathetic skin response (SSR). Forty-eight PwE with disease duration ranging from 2 to 15 years and 51 healthy individuals were studied. Long-term electroencephalography (EEG) monitoring, the heart rate variability (HRV) during normal breathing, deep breathing, Valsalva maneuver and standing, BP responses during standing, to isometric hand grip and to mental arithmetic, and the SSR was recorded for all participants.

Results

31 patients with TLE and 17 with IGE showed lower RR-IV values during deep breathing, Valsalva maneuver and standing, but not during rest, impaired BP responses during standing, isometric hand grip, and mental arithmetic. Also, prolonged SSR latencies. Within PwE group, no difference was noticed between males and females, nor between the left and right temporal lobes.

Conclusion

Abnormal autonomic (sympathetic and parasympathetic) regulatory functions suggest that epilepsy may alter the autonomic function and this is not only in TLE but rather in IGE too. These autonomic changes are irrespective of the localization of epilepsy between the two hemispheres. The ANS changes in epileptic patients, particularly those with autonomic symptoms, confirm that electrophysiologic measures of autonomic function may be of value in preventing sudden unexpected death in epilepsy.

Reducing Sudden Unexpected Death in Epilepsy: Considering Risk Factors, Pathophysiology and Strategies

Purpose of Review

Sudden Unexpected Death in Epilepsy (SUDEP) is the commonest cause of epilepsy-related premature mortality in people with chronic epilepsy. It is the most devastating epilepsy outcome. We describe and discuss risk factors and possible pathophysiological mechanisms to elucidate possible preventative strategies to avert SUDEP.

Recent Findings

Sudden death accounts for a significant proportion of premature mortality in people with epilepsy compared to the general population. Unmodifiable risk factors include a history of neurologic insult, younger age of seizure-onset, longer epilepsy duration, a history of convulsions, symptomatic epilepsy, intellectual disability, and non-ambulatory status. Modifiable risk factors include the presence of convulsive seizures, increased seizure frequency, timely and appropriate use of antiseizure medications, polytherapy, alcoholism, and supervision while sleeping. Pathophysiology is unclear, but several possible mechanisms such as direct alteration of cardiorespiratory function, pulmonary impairment, electrocerebral shutdown, adenosine dysfunction, and genetic susceptibility suggested.

Summary

Methods to prevent SUDEP include increasing awareness of SUDEP, augmenting knowledge of unmodifiable risk factors, obtaining full seizure remission, addressing lifestyle factors such as supervision and prone positioning, and enacting protocols to increase the detection of and intervention for SUDEP. Further studies are required to characterize precisely and comprehensively SUDEP risk factors and pathophysiological drivers and develop evidence-based algorithms to minimize SUDEP in people with epilepsy.

Novel Seizure Biomarkers in Continuous Electrocardiograms from Pediatric Epilepsy Patients

There is growing evidence that seizures are accompanied by multi-system changes, not only in the brain but also in organs and systems under its control. Non-EEG measurements from these systems could be leveraged to improve seizure prediction, which is difficult but critical to the success of next-generation epilepsy therapies. Clinical electrophysiology studies during presurgical patient evaluations routinely collect continuous EEG but also ECG data that span multiple days. Prior work has reported electrocardiographic changes but has primarily focused on ventricular activity and brief peri-ictal intervals. Using novel data-driven classification and separation of the ECG high-dimensional signal space, this study investigated seizure-related changes in both ventricular and atrial activity. Measures of complexity as well as heart rate and R-R interval length were analyzed over time in continuous ECGs from 22 pediatric patients with pharmacoresistant seizures and no diagnosed cardiovascular anomalies. Fifteen patients (>68%) had significant changes in atrial or ventricular activity (or both) in intervals containing seizures. Thus, for a substantial number of patients, cardiac markers may be specifically modulated by seizures and could be leveraged to improve and personalize seizure prediction.

Severe autonomic nervous system imbalance in Lennox-Gastaut syndrome patients demonstrated by heart rate variability recordings

OBJECTIVE

Patients diagnosed with Lennox Gastaut syndrome (LGS), an epileptic encephalopathy characterized by usually drug resistant generalized and focal seizures, are often considered as candidates for vagus nerve stimulation (VNS). Recent research shows that heart rate variability (HRV) differs in epilepsy patients and is related to VNS treatment response. This study investigated pre-ictal HRV in generalized onset seizures of patients with LGS in correlation with their VNS response.

METHODS

In drug resistant epilepsy (DRE) patients diagnosed with LGS video-electroencephalography recording was performed during their pre-surgical evaluation. Six HRV parameters (time and-, frequency domain, non-linear parameters) were evaluated for every seizure in epochs of 10 min at baseline (60 to 50 min before seizure onset) and pre-ictally (10 min prior to seizure onset). The results were correlated to VNS response after one year of VNS therapy.

RESULTS

Seven patients and 31 seizures were included, two patients were classified as VNS responders (≥ 50 % seizure reduction). No difference in pre-ictal HRV parameters between VNS responders and VNS non-responders could be found, but high frequency (HF) power, reflecting the parasympathetic tone increased significantly in the pre-ictal epoch in both VNS responders and VNS non-responders (p = 0.017, p = 0.004).

SIGNIFICANCE

In this pilot data pre-ictal HRV did not differ in VNS responders compared to VNS non-responders, but showed a significant increase in HF power - a parasympathetic overdrive - in both VNS responders and VNS non-responders. This sudden autonomic imbalance might have an influence on the cardiovascular system in the ictal period. Generalized tonic-clonic seizures are regarded as the main risk factor for SUDEP and severe seizure-induced autonomic imbalance may play a role in the pathophysiological pathway.

Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.

Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions

Objective

To identify non‐EEG‐based signals and algorithms for detection of motor and non‐motor seizures in people lying in bed during video‐EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings.

Methods

Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one‐lead ECG. All seizure types were analyzed. Feature extraction and machine‐learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F₁ score, and false positives per 24 hours.

Results

The algorithms were developed in group 1 (35 PwE, 33 seizures) and achieved best results (F₁ score 56%, sensitivity 67%, precision 45%, false positives 0.7/24 hours) when ECG features alone were used, with no improvement by including accelerometry and electrodermal activity. In group 2 (97 PwE, 255 seizures), this ECG‐based algorithm largely achieved the same performance (F₁ score 51%, sensitivity 39%, precision 73%, false positives 0.4/24 hours). In group 3 (30 PwE, 51 seizures), the same ECG‐based algorithm failed to meet up with the performance in groups 1 and 2 (F₁ score 27%, sensitivity 31%, precision 23%, false positives 1.2/24 hours). ECG‐based algorithms were also separately trained on data of groups 2 and 3 and tested on the data of the other groups, yielding maximal F₁ scores between 8% and 26%.

Significance

Our results suggest that algorithms based on ECG features alone can provide clinically meaningful performance for automatic detection of all seizure types. Our study also underscores that the circumstances under which such algorithms were developed, and the selection of the training and test data sets need to be considered and limit the application of such systems to unseen patient groups behaving in different conditions.

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey-Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.

The potential of closed-loop endovascular neurostimulation as a viable therapeutic approach for drug-resistant epilepsy: A critical review

Over the last few decades, biomedical implants have successfully delivered therapeutic electrical stimulation to reduce the frequency and severity of seizures in people with drug-resistant epilepsy. However, neurostimulation approaches require invasive surgery to implant stimulating electrodes, and surgical, medical, and hardware complications are not uncommon. An endovascular approach provides a potentially safer and less invasive surgical alternative. This article critically evaluates the feasibility of endovascular closed-loop neuromodulation for the treatment of epilepsy. By reviewing literature that reported the impact of direct electrical stimulation to reduce the frequency of epileptic seizures, we identified clinically validated extracranial, cortical, and deep cortical neural targets. We identified veins in close proximity to these targets and evaluated the potential of delivering an endovascular implant to these veins based on their diameter. We then compared the risks and benefits of existing technology to describe a benchmark of clinical safety and efficacy that would need to be achieved for endovascular neuromodulation to provide therapeutic benefit. For the majority of brain regions that have been clinically demonstrated to reduce seizure occurrence in response to delivered electrical stimulation, vessels of appropriate diameter for delivery of an endovascular electrode to these regions could be achieved. This includes delivery to the vagus nerve via the 13.2 ± 0.9 mm diameter internal jugular vein, the motor cortex via the 6.5 ± 1.7 mm diameter superior sagittal sinus, and the cerebellum via the 7.7 ± 1.4 mm diameter sigmoid sinus or 6.2 ± 1.4 mm diameter transverse sinus. Deep cerebral targets can also be accessed with an endovascular approach, with the 1.9 ± 0.5 mm diameter internal cerebral vein and 1.2-mm-diameter thalamostriate vein lying in close proximity to the anterior and centromedian nuclei of the thalamus, respectively. This work identified numerous veins that are in close proximity to conventional stimulation targets that are of a diameter large enough for delivery and deployment of an endovascular electrode array, supporting future work to assess clinical efficacy and chronic safety of an endovascular approach to deliver therapeutic neurostimulation.

Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

Background:

Because about 30% of epileptic patients suffer from refractory epilepsy, an efficient automatic seizure prediction tool is in great demand to improve their life quality.

Methods:

In this work, time-domain discriminating preictal and interictal features were efficiently extracted from the intracranial electroencephalogram of twelve patients, i.e., six with temporal and six with frontal lobe epilepsy. The performance of three types of feature selection methods was compared using Matthews’s correlation coefficient (MCC).

Results:

Kruskal Wallis, a non-parametric approach, was found to perform better than the other approaches due to a simple and less resource consuming strategy as well as maintaining the highest MCC score. The impact of dividing the electroencephalogram signals into various sub-bands was investigated as well. The highest performance of Kruskal Wallis may suggest considering the importance of univariate features like complexity and interquartile ratio (IQR), along with autoregressive (AR) model parameters and the maximum (MAX) cross-correlation to efficiently predict epileptic seizures.

Conclusion:

The proposed approach has the potential to be implemented on a low power device by considering a few simple time domain characteristics for a specific sub-band. It should be noted that, as there is not a great deal of literature on frontal lobe epilepsy, the results of this work can be considered promising.

Ictal and Interictal Cardiac Manifestations in Epilepsy. A Review of Their Relation With an Altered Central Control of Autonomic Functions and With the Risk of SUDEP

There is a complex interrelation between epilepsy and cardiac pathology, with both acute and long-term effects of seizures on the regulation of the cardiac rhythm and on the heart functioning. A specific issue is the potential relation between these cardiac manifestations and the risk of Sudden and Unexpected Death in Epilepsy (SUDEP), with unclear respective role of centrally-control ictal changes, long-term epilepsy-related dysregulation of the neurovegetative control and direct effects on the heart function. In the present review, we detailed available data about ictal cardiac changes, along with interictal cardiac manifestations associated with long-term functional and structural alterations of the heart. Pathophysiological mechanisms of these cardiac changes are discussed, with a specific focus on central mechanisms and the investigation of a possible deregulation of the central control of autonomic functions in addition to the role of catecholamine and hypoxemia on heart.

Wearables bei Epilepsien

Epileptische Anfälle führen zu verschiedensten körperlichen Symptomen, die je nach Art und Ausprägung mit geeigneten Geräten gemessen werden und als Surrogatmarker epileptischer Anfälle dienen können. Dominierende motorische Symptome können mit Beschleunigungssensoren oder elektromyografisch erfasst werden. Bei fokalen Anfällen mit fehlender oder geringer motorischer Beteiligung können autonome Phänomene wie Änderungen der Herzrate, Atmung und des elektrischen Hautwiderstandes per Elektrokardiografie, Photopletysmografie und Hautsensoren gemessen werden. Die in den heutigen Wearables integrierten Sensoren können diese Körpersignale messen und zur automatisierten Anfallserkennung nutzbar machen. In dieser Übersichtsarbeit werden verschiedene Sensortechnologien, Wearables und deren Anwendung zur automatisierten Erkennung epileptischer Anfälle vorgestellt, Gütekriterien zur Einschätzung mobiler Gesundheitstechnologien diskutiert und klinisch geprüfte Systeme zusammengefasst.

Pre-ictal heart rate variability alterations in focal onset seizures and response to vagus nerve stimulation

PURPOSE

Vagus nerve stimulation (VNS) is an effective and well-known treatment for drug resistant epilepsy (DRE) patients since 1997, yet prediction of treatment response before implantation is subject of ongoing research. Neuroimaging and neurophysiological studies investigating the vagal afferent network in resting state documented that differences in between epilepsy patients were related to treatment response. This study investigated whether an event-related parameter, pre-ictal heart rate variability (HRV) is associated with response to VNS therapy.

METHODS

DRE patients underwent video-electroencephalography (EEG) recording before VNS implantation. HRV parameters (time, non-linear and frequency domain) were assessed for every seizure during two 10 min timeframes: baseline (60 min before seizure onset) and pre-ictal (10 min before seizure onset). Pre-ictal HRV parameter alterations were correlated with VNS response after one year of VNS therapy and seizure characteristics (temporal/extratemporal, left/right or bilateral).

RESULTS

104 seizures from 22 patients were evaluated. Eleven patients were VNS responders with a seizure frequency reduction of ≥ 50 % after one year of VNS. In VNS responders no changes in HRV parameters were found while in VNS non-responders the time domain and non-linear HRV variables decreased significantly (p = 0.024, p = 0.005, p = 0.005) during the pre-ictal time frame. 10/11 VNS non-responders had a seizure lateralization to the left compared to 4/11 VNS responders.

CONCLUSION

VNS non-responders were characterized by a significant decrease of pre-ictal HRV (time domain/non-linear variables) suggesting a sudden autonomic imbalance probably due to an impaired central autonomic function that makes it at the same time unlikely to respond to VNS.

Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor data

This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video-electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

Corrected QT interval and QT dispersion in temporal lobe epilepsy

Background

Cardiac arrhythmias are expected among patients with epilepsy due to the effect of anti-epileptic drugs. Temporal lobe epilepsy also causes autonomic seizures that may affect heart rhythm. Prolongation of the corrected QT interval and QT dispersion is a risk factor for cardiac arrhythmia.

Objectives

We aimed to assess corrected QT interval and QT dispersion in patients with epilepsy and if there is a difference between patients with temporal epilepsy versus non-temporal epilepsy.

Methods

This study was conducted on 100 patients (50 patients with temporal epilepsy and 50 patients with non-temporal epilepsy) and 50 age- and sex-matched healthy controls. They underwent a prolonged (6 to 24 h) 22 channel computerized electroencephalogram monitor with a 10–20 system. QT dispersion, QT interval, and corrected QT interval (using Bazett’s formula) were calculated.

Results

This study showed significantly higher QT dispersion and corrected QT interval in patients with epilepsy when compared to the age- and sex-matched control group (P < 0.001, P < 0.001). Also, the corrected QT interval and QT dispersion were significantly higher in temporal epilepsy patients when compared to the non-temporal group (P < 0.001, P < 0.001).

Conclusion

Corrected QT interval and QT dispersion are higher in epileptic patients and more among temporal epilepsy patients in comparison to non-temporal epilepsy patients.

EPO and EPO-Receptor System as Potential Actionable Mechanism for the Protection of Brain and Heart in Refractory Epilepsy and SUDEP

The most important activity of erythropoietin (EPO) is the regulation of erythrocyte production by activation of the erythropoietin receptor (EPO-R), which triggers the activation of anti-apoptotic and proliferative responses of erythroid progenitor cells. Additionally, to erythropoietic EPO activity, an antiapoptotic effect has been described in a wide spectrum of tissues. EPO low levels are found in the central nervous system (CNS), while EPO-R is expressed in most CNS cell types. In spite of EPO-R high levels expressed during the hypoxicischemic brain, insufficient production of endogenous cerebral EPO could be the cause of determined circuit alterations that lead to the loss of specific neuronal populations. In the heart, high EPO-R expression in cardiac progenitor cells appears to contribute to myocardial regeneration under EPO stimulation. Several lines of evidence have linked EPO to an antiapoptotic role in CNS and in heart tissue. In this review, an antiapoptotic role of EPO/EPO-R system in both brain and heart under hypoxic conditions, such as epilepsy and sudden death (SUDEP) has been resumed. Additionally, their protective effects could be a new field of research and a novel therapeutic strategy for the early treatment of these conditions and avoid SUDEP.

Can seizure-related heart rate differentiate epileptic from psychogenic nonepileptic seizures?

OBJECTIVES

We aimed to (1) determine if seizure-related heart rate (HR) differentiates epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES); (2) define the most useful point of the following HR measurements: preictal, ictal-onset, maximal-ictal, or postictal; and (3) delineate the optimal HR cutoff points (absolute HR and relative HR increase) to differentiate ES from PNES.

METHODS

All video-electroencephalography (VEEG) recorded at an Australian tertiary hospital from May 2009 to November 2015 were retrospectively reviewed. Baseline (during rest and wakefulness), 1-min preictal, ictal-onset, maximal-ictal, and 1-min postictal HR were measured for each ES and PNES event. Events lasting <10 s or with uninterpretable electrocardiogram (ECG) due to artifacts were excluded. Receiver operating characteristic curve analysis was performed to assess the diagnostic accuracy of HR reflected by the area under the curve (AUC).

RESULTS

Video-electroencephalography of 341 ES and 265 PNES from 130 patients were analyzed. The AUC for preictal, ictal-onset, maximal-ictal, and postictal HR were found to have poor differentiation between all types of ES and PNES. However, comparing bilateral tonic-clonic ES and PNES, AUC for absolute maximal-ictal HR was 0.84 (95% confidence interval [CI]: 0.73-0.95) and for absolute postictal HR was 0.90 (95% CI: 0.81-1.00) indicating good diagnostic discrimination. Using Youden's index to diagnose tonic-clonic ES, the optimal cutoff point for absolute maximal-ictal HR was 114 bpm (sensitivity: 84%, specificity: 82%) and for absolute postictal HR was 90 bpm (sensitivity: 91%, specificity: 82%).

CONCLUSION

These findings suggest that seizure-related HR is useful in differentiating bilateral tonic-clonic ES from PNES. Based on the AUC, the best diagnostic measurements are maximal-ictal and postictal HR.

Cardiorespiratory and autonomic function in epileptic seizures: A video-EEG monitoring study

PURPOSE

Seizure-induced cardiorespiratory and autonomic dysfunction has long been recognized, and growing evidence points to its implication in sudden unexpected death in epilepsy (SUDEP). However, a comprehensive understanding of cardiorespiratory function in the preictal, ictal, and postictal periods are lacking.

METHODS

We examined continuous cardiorespiratory and autonomic function in 157 seizures (18 convulsive and 139 nonconvulsive) from 70 consecutive patients who had a seizure captured on concurrent video-encephalogram (EEG) monitoring and polysomnography between February 1, 2012 and May 31, 2017. Heart and respiratory rates, heart rate variability (HRV), and oxygen saturation were assessed across four distinct periods: baseline (120 s), preictal (60 s), ictal, and postictal (300 s). Heart and respiratory rates were further followed for up to 60 min after seizure termination to assess return to baseline.

RESULTS

Ictal tachycardia occurred during both convulsive and nonconvulsive seizures, but the maximum rate was higher for convulsive seizures (mean: 138.8 beats/min, 95% confidence interval (CI): 125.3-152.4) compared with nonconvulsive seizures (mean: 105.4 beats/min, 95% CI: 101.2-109.6; p < 0.001). Convulsive seizures were associated with a lower ictal minimum respiratory rate (mean: 0 breaths/min, 95% CI: 0-0) compared with nonconvulsive seizures (mean: 11.0 breaths/min, 95% CI: 9.5-12.6; p < 0.001). Ictal obstructive apnea was associated with convulsive compared with nonconvulsive seizures. The low-frequency (LF) power band of ictal HRV was higher among convulsive seizures than nonconvulsive seizures (ratio of means (ROM): 2.97, 95% CI: 1.34-6.60; p = 0.008). Postictal tachycardia was substantially prolonged, characterized by a longer return to baseline for convulsive seizures (median: 60.0 min, interquartile range (IQR): 46.5-60.0) than nonconvulsive seizures (median: 0.26 min, IQR: 0.008-0.9; p < 0.001). For postictal hyperventilation, the return to baseline was longer in convulsive seizures (median: 25.3 min, IQR: 8.1-60) than nonconvulsive seizures (median: 1.0 min, IQR: 0.07-3.2; p < 0.001). The LF power band of postictal HRV was lower in convulsive seizures than nonconvulsive seizures (ROM: 0.33, 95% CI: 0.11-0.96; p = 0.043). Convulsive seizures with postictal generalized EEG suppression (PGES; n = 12) were associated with lower postictal heart and respiratory rate, and increased HRV, compared with those without (n = 6).

CONCLUSIONS

Profound cardiorespiratory and autonomic dysfunction associated with convulsive seizures may explain why these seizures carry the greatest risk of SUDEP.

Causes and Effects Contributing to Sudden Death in Epilepsy and the Rationale for Prevention and Intervention

Sudden unexpected death in epilepsy (SUDEP) claims the lives of one in every thousand epileptic patients each year. Autonomic, cardiac, and respiratory pieces to a mechanistic puzzle have not yet been completely assembled. We propose a single sequence of causes and effects that unifies disparate and competitive concepts into a single algorithm centered on ictal obstructive apnea. Based on detailed animal studies that are sometimes impossible in humans, and striking parallels with a growing body of clinical examples, this framework (1) accounts for the autonomic, cardiac, and respiratory data to date by showing the causal relationships between specific elements, and (2) highlights specific kinds of data that can be used to precisely classify various patient outcomes. The framework also justifies a “near miss” designation to be applied to any cases with evidence of obstructive apnea even, and perhaps especially, in individuals that do not require resuscitation. Lastly, the rationale for preventative oxygen therapy is demonstrated. With better mechanistic understanding of SUDEP, we suggest changes for detection and classification to increase survival rates and improve risk stratification.

Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Noninvasive detection of focal seizures in ambulatory patients

Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions.

Combining tissue engineering and optical imaging approaches to explore interactions along the neuro-cardiac axis

Interactions along the neuro-cardiac axis are being explored with regard to their involvement in cardiac diseases, including catecholaminergic polymorphic ventricular tachycardia, hypertension, atrial fibrillation, long QT syndrome and sudden death in epilepsy. Interrogation of the pathophysiology and pathogenesis of neuro-cardiac diseases in animal models present challenges resulting from species differences, phenotypic variation, developmental effects and limited availability of data relevant at both the tissue and cellular level. By contrast, tissue-engineered models containing cardiomyocytes and peripheral sympathetic and parasympathetic neurons afford characterization of cellular- and tissue-level behaviours while maintaining precise control over developmental conditions, cellular genotype and phenotype. Such approaches are uniquely suited to long-term, high-throughput characterization using optical recording techniques with the potential for increased translational benefit compared to more established techniques. Furthermore, tissue-engineered constructs provide an intermediary between whole animal/tissue experiments and in silico models. This paper reviews the advantages of tissue engineering methods of multiple cell types and optical imaging techniques for the characterization of neuro-cardiac diseases.

Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures

Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is commonly based on electroencephalogram and concurrent video recordings (vEEG). Here, we demonstrate that these two types of seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors. We used Empatica E4 Wristband sensors worn on both arms in vEEG confirmed seizures, and machine learning methods to train classifiers, specifically, extreme gradient boosting (XGBoost). Classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic, which is 6 standard deviations above the baseline of 68% accuracy. Our dataset contained altogether 35 seizures from 18 patients out of which 8 patients had 13 convulsive seizures. Prediction of seizure type was based on simple features derived from the segments of autonomic activity measurements (electrodermal activity, body temperature, blood volume pulse, and heart rate) and forearm acceleration. Features related to heart rate and electrodermal activity were ranked as the top predictors in XGBoost classifiers. We found that patients with PNES had a higher ictal heart rate and electrodermal activity than patients with ES. In contrast to existing published studies of mainly convulsive seizures, our classifier focuses on autonomic signals to differentiate convulsive or nonconvulsive semiology ES from PNES. Our results show that autonomic activity recorded via wearable sensors provides promising signals for detection and discrimination of psychogenic and epileptic seizures, but more work is necessary to improve the predictive power of the model.