Parasympathetic alteration during sub-clinical seizures

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.

The Association of Nocturnal Seizures and Interictal Cardiac/Central Autonomic Function in Frontal Lobe Epilepsy: Heart Rate Variability and Central Autonomic Network Analysis

Purpose

Patients with epilepsy frequently experience autonomic dysfunction, closely related to sudden unexplained death in epilepsy (SUDEP). SUDEP occurs most often at night or during sleep, and frequent nocturnal seizures are an established risk factor. This study investigated the influence of nocturnal seizures on autonomic dysfunction in epilepsy.

Patients and Methods

This retrospective study enrolled frontal lobe epilepsy (FLE) patients who performed 24-hour EEG monitoring. All participants were divided into nocturnal FLE (NFLE, > 90% of seizures occurring during sleep) or diurnal FLE (DFLE) groups. EEG and ECG signals were simultaneously obtained during waking and sleep stages. EEG current density source and connectivity analysis of the autonomic network were performed. ECG was analyzed across time and frequency domains heart rate variability (HRV) analysis method was used. The obtained parameters were compared between the NFLE and DFLE groups.

Results

Fifteen NFLE and 16 DFLE patients were enrolled with no significant difference in age, sex, disease duration, seizure frequency, or the number of anti-seizure medications between the two groups. During sleep, a decrease in HRV parameters and an increase of the beta-1 (13-22 Hz) current source density power in the bilateral paracentral lobule (BA4,5,6), precuneus (BA7), and cingulate (BA31) were observed in the NFLE group compared to DFLE group. The NFLE group also showed hyperconnectivity in the central autonomic (12 edges distributed over 10 nodes), sympathetic (2 edges distributed over 3 nodes), and parasympathetic (4 edges distributed over 6 nodes) beta-1 frequency band networks during sleep. During wakefulness, central and cardiac autonomic variables were not significantly different between the NFLE and DFLE groups.

Conclusion

Interictal cardiac and central autonomic dysfunction occurred simultaneously and can be attributed to the brain-heart autonomic axis. Our findings suggest that nocturnal seizures may contribute to interictal autonomic dysfunction during sleep in people with epilepsy.

Vagus Nerve Electroneurogram-Based Detection of Acute Pentylenetetrazol Induced Seizures in Rats

On-demand stimulation improves the efficacy of vagus nerve stimulation (VNS) in refractory epilepsy. The vagus nerve is the main peripheral parasympathetic connection and seizures are known to exhibit autonomic symptoms. Therefore, we hypothesized that seizure detection is possible through vagus nerve electroneurogram (VENG) recording. We developed a metric able to measure abrupt changes in amplitude and frequency of spontaneous vagus nerve action potentials. A classifier was trained using a "leave-one-out" method on a set of 6 seizures and 3 control recordings to utilize the VENG spike feature-based metric for seizure detection. We were able to detect pentylenetetrazol (PTZ) induced acute seizures in 6/6 animals during different stages of the seizure with no false detection. The classifier detected the seizure during an early stage in 3/6 animals and at the onset of tonic clonic stage of the seizure in 3/6 animals. EMG and motion artefacts often accompany epileptic activity. We showed the "epileptic" neural signal to be independent from EMG and motion artefacts. We confirmed the existence of seizure related signals in the VENG recording and proved their applicability for seizure detection. This detection might be a promising tool to improve efficacy of VNS treatment by developing new responsive stimulation systems.

Cardiac Autonomic Dysfunction and Risk of Sudden Unexpected Death in Epilepsy

OBJECTIVE

We aimed to test whether patients who died of sudden unexpected death in epilepsy (SUDEP) had an abnormal cardiac autonomic response to sympathetic stimulation by hyperventilation.

METHODS

We conducted a retrospective, observational, case-control study of a group of patients who died of SUDEP and controls who were matched to the patients for epilepsy type, drug resistance, sex, age at EEG recording, age at onset of epilepsy, and duration of epilepsy. We analyzed the heart rate (HR) and HR variability (HRV) at rest and during and after hyperventilation performed during the patient's last EEG recording before SUDEP. In each group, changes over time in HRV indexes were analyzed with linear mixed models.

RESULTS

Twenty patients were included in each group. In the control group, the HR increased and the root mean square of successive RR-interval differences (RMSSD) decreased during the hyperventilation and then returned to the baseline values. In the SUDEP group, however, the HR and RMSSD did not change significantly during or after hyperventilation. A difference in HR between the end of the hyperventilation and 4 minutes after its end discriminated well between patients with SUDEP and control patients (area under the receiver operating characteristic curve 0.870, sensitivity 85%, specificity 75%).

CONCLUSION

Most of patients with subsequent SUDEP have an abnormal cardiac autonomic response to sympathetic stimulation through hyperventilation. An index reflecting the change in HR on hyperventilation might be predictive of the risk of SUDEP and could be used to select patients at risk of SUDEP for inclusion in trials assessing protective measures.

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.

Autonomic Characteristics of Sudden Unexpected Death in Epilepsy in Children-A Systematic Review of Studies and Their Relevance to the Management of Epilepsy in Rett Syndrome

Aim: To systematically identify and critically appraise studies that investigate the autonomic characteristics of Sudden Unexpected Death in Epilepsy (SUDEP) in the pediatric population. We also wanted to explore how this information would be relevant to the management of epilepsy in patients with Rett Syndrome.

Method: Using PRISMA guidelines, a systematic review of PubMed, Scopus, Cochrane, PsycINFO, Embase, and Web of Science databases was performed to identify eligible studies. After extracting data from the included studies, a thematic analysis was undertaken to identify emerging themes. A quality appraisal was also done to assess the quality of the included studies.

Results: The systematic search revealed 41 records, and 15 full-text articles on the autonomic characteristics of SUDEP in children were included in the final analysis. Following thematic analysis, three themes were identified (I) modulation in sympathovagal tone, (II) pre- and post-ictal autonomic changes, and (III) other markers of autonomic dysregulation in children with epilepsy. Modulation in sympathovagal tone emerged as the theme with the highest frequency followed by pre- and post-ictal autonomic changes. While the themes provide additional insight into the management of epilepsy in the Rett Syndrome population, the quality of evidence concerning the autonomic characteristics of SUDEP in the pediatric population was low and underscores the importance of much needed research in this area.

Conclusion: The mechanism of SUDEP in the pediatric population is complex and involves an interplay between several components of the autonomic nervous system. While direct clinical inferences regarding pediatric SUDEP could not be made, the thematic analysis does suggest that in vulnerable populations such as Rett Syndrome, where there is already a pervasive autonomic dysregulation, pro-active surveillance of the autonomic profile in this patient group would be useful to better manage epilepsy and reduce the SUDEP risk.

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.

Electrocardiographic changes associated with epilepsy beyond heart rate and their utilization in future seizure detection and forecasting methods

The ability to assess seizure risk may help provide timely warnings and more personalized treatment plans for people with epilepsy (PWE). ECG changes are commonly observed in epilepsy which make ECG a promising candidate to monitor seizure risk. Most ECG research in this domain has focused on heart rate-related changes. However, several studies have identified a range of other peri-ictal ECG parameter changes that may potentially prove useful for seizure detection and forecasting. Here, we offer a systematic review of ECG changes in epilepsy outside of heart rate. We performed the systematic literature review according to PRISMA guidelines using key words related to ECG, SUDEP and epilepsy. We identified and screened 502 abstracts, read 110 full papers, and included 24 papers in the final review. Our results suggest that PWE may be more prone to cardiac conduction abnormalities than healthy controls. During interictal periods, PWE were more likely to have abnormal QTc intervals, ST segment abnormalities, elevated T Waves, early repolarization (ER), increased P Wave dispersion and PR intervals when compared to controls. Apart from these baseline abnormalities, changes during the pre-ictal and ictal states have been reported, with arrhythmias, QTc prolongation and ST segment changes being the most common. A better understanding of these state-dependent changes may afford less-cumbersome and less-stigmatizing epilepsy monitoring tools in the future.

The Evolution of Subclinical Seizures in Children With Tuberous Sclerosis Complex

BACKGROUND

Subclinical seizures are electrographic seizures that present without subjective or objective clinical symptoms. In tuberous sclerosis complex, it is not known whether subclinical seizures occur alone, forewarn, or coexist with clinical seizures. To address this knowledge gap, we studied the prevalence and evolution of subclinical seizures in tuberous sclerosis complex.

METHODS

We retrospectively reviewed electroencephalography (EEG) data from our tuberous sclerosis complex clinic with subclinical seizures and clinical seizures in a blinded fashion. Based on EEG location and ictal pattern, subclinical seizures were classified as having a clinical counterpart from the same epileptogenic region (match) or not (no match).

RESULTS

Of 208 children with tuberous sclerosis complex, 138 had epilepsy and available EEG data. Subclinical seizures were detected in 26 of 138 (19%) children. Twenty-four children had both subclinical seizures and clinical seizures captured on EEG. In 13 of 24, subclinical seizures were detected as a novel, not previously recorded seizure type. In these children, subclinical seizures preceded matching clinical seizures in 4 (31%) within a median time of 4.5 months (range 2-14), whereas 9 (69%) never had any matching clinical seizure. In 11 of 24 children, subclinical seizures were not novel and could be matched to a previously recorded clinical seizure. Matching seizure types were focal (n = 10, 67%), tonic (n = 2), epileptic spasms (n = 2), and status epilepticus (n = 1).

CONCLUSIONS

Subclinical seizures occur in one-fifth of children with tuberous sclerosis complex and epilepsy, and match with clinical seizures in a small majority. In a third of patients presenting with a novel subclinical seizure, matching clinical seizures follow.

Seizure detection based on heart rate variability using a wearable electrocardiography device

OBJECTIVE

To assess the feasibility and accuracy of seizure detection based on heart rate variability (HRV) using a wearable electrocardiography (ECG) device. Noninvasive devices for detection of convulsive seizures (generalized tonic-clonic and focal to bilateral tonic-clonic seizures) have been validated in phase 2 and 3 studies. However, detection of nonconvulsive seizures still needs further research, since currently available methods have either low sensitivity or an extremely high false alarm rate (FAR).

METHODS

In this phase 2 study, we prospectively recruited patients admitted to long-term video-EEG monitoring (LTM). ECG was recorded using a dedicated wearable device. Seizures were automatically detected using HRV parameters computed off-line, blinded to all other data. We compared the performance of 26 automated algorithms with the seizure time-points marked by experts who reviewed the LTM recording. Patients were classified as responders if >66% of their seizures were detected.

RESULTS

We recruited 100 consecutive patients and analyzed 126 seizures (108 nonconvulsive and 18 convulsive) from 43 patients who had seizures during monitoring. The best-performing HRV algorithm combined a measure of sympathetic activity with a measure of how quickly HR changes occurred. The algorithm identified 53.5% of the patients with seizures as responders. Among responders, detection sensitivity was 93.1% (95% CI: 86.6%-99.6%) for all seizures and 90.5% (95% CI: 77.4%-97.3%) for nonconvulsive seizures. FAR was 1.0/24 h (0.11/night). Median seizure detection latency was 30 s. Typically, patients with prominent autonomic nervous system changes were responders: An ictal change of >50 heartbeats per minute predicted who would be responder with a positive predictive value of 87% and a negative predictive value of 90%.

SIGNIFICANCE

The automated HRV algorithm, using ECG recorded with a wearable device, has high sensitivity for detecting seizures, including the nonconvulsive ones. FAR was low during the night. This approach is feasible in patients with prominent ictal autonomic changes.

Heart rate variability measurement in epilepsy: How can we move from research to clinical practice?

Our objective was to critically evaluate the literature surrounding heart rate variability (HRV) in people with epilepsy and to make recommendations as to how future research could be directed to facilitate and accelerate integration into clinical practice. We reviewed relevant HRV publications including those involving human subjects with seizures. HRV has been studied in patients with epilepsy for more than 30 years and, overall, patients with epilepsy display altered interictal HRV, suggesting a shift in autonomic balance toward sympathetic dominance. This derangement appears more severe in those with temporal lobe epilepsy and drug-resistant epilepsy. Normal diurnal variation in HRV is also disturbed in at least some people with epilepsy, but this aspect has received less study. Some therapeutic interventions, including vagus nerve stimulation and antiepileptic medications, may partially normalize altered HRV, but studies in this area are sometimes contradictory. During seizures, the changes in HRV may be complex, but the general trend is toward a further increase in sympathetic overactivity. Research in HRV in people with epilepsy has been limited by inconsistent experimental protocols and studies that are often underpowered. HRV measurement has the potential to aid clinical epilepsy management in several possible ways. HRV may be useful in predicting which patients are likely to benefit from surgical interventions such as vagus nerve stimulation and focal cerebral resection. As well, HRV could eventually have utility as a biomarker of risk for sudden unexpected death in epilepsy (SUDEP). However, at present, the inconsistent measurement protocols used in research are hindering translation into clinical practice. A minimum protocol for HRV evaluation, to be used in all studies involving epilepsy patients, is necessary to eventually allow HRV to become a useful tool for clinicians. We propose a straightforward protocol, involving 5-minute measurements of root mean square of successive differences in wakefulness and light sleep.

Diurnal patterns and relationships between physiological and self-reported stress in patients with epilepsy and psychogenic non-epileptic seizures

PURPOSE

Patients with epilepsy and those with psychogenic non-epileptic seizures (PNES) experience high levels of stress and stress is one of the most frequently self-identified seizure precipitants. Although stress is a multifaceted phenomenon, few studies have systematically examined its different components in patients with seizures. The aim of this study was therefore to describe diurnal patterns of psychological and physiological measures of stress in patients with epilepsy and patients with PNES, and explore their relationships to each other in order to improve our understanding of the mechanisms underlying stress and seizure occurrence in these patients.

METHOD

A range of stress markers including self-reported stress, salivary cortisol, and heart rate variability (HRV) were explored in adult patients with refractory epilepsy (N=22) and those with PNES (N=23) undergoing three- to five-day video-telemetry.

RESULTS

A diurnal pattern was observed in the physiological measures, characterized by higher levels of physiological arousal in the mornings and lower levels at night in both patients with epilepsy and PNES. The physiological measures (cortisol and HRV) were associated with each other in patients with epilepsy; no close relationship was found with self-reported stress in either of the two patient groups.

CONCLUSION

The findings contribute to and expand on previous studies of the patterns of stress in patients with seizures. The results also indicate a discrepancy between patients' physiological responses and their subjective stress perceptions, suggesting that simple self-reports cannot be used as a proxy of physiological arousal in patients with seizures and stress. Stress in these patient groups should be studied using a combination of complementary measures.

Cardiac and autonomic mechanisms contributing to SUDEP

Sudden unexpected death in epilepsy is likely caused by a cascade of events affecting the vegetative nervous system leading to cardiorespiratory failure and death. Multiple genetic, electrophysiological, neurochemical, and pharmacological cardiac alterations have been associated with epilepsy, which can affect autonomic regulation of the heart and predispose patients to sudden unexpected death in epilepsy. These cardiac and autonomic changes are more frequently seen in patients with longstanding and medication refractory epilepsy and may be a prerequisite for sudden unexpected death in epilepsy. Cardiac changes are also observed within the immediate periictal period in patients with and without preexisting cardiac pathology and could be the tipping point in the cascade of events compromising autonomic, respiratory, and cardiac function during an epileptic convulsion. Better understanding if and how these cardiac alterations can make a particular individual with epilepsy more susceptible to sudden unexpected death in epilepsy will hopefully lead us to more effective preventative strategies.

Cardiac phenomena during kainic-acid induced epilepsy and lamotrigine antiepileptic therapy

RATIONALE

Pathologic ECG events are known to accompany seizures and to persist in several chronic epilepsy syndromes. The contribution of antiepileptic drugs (AEDs) to these events and the implications in the etiology of sudden-unexpected death in epilepsy (SUDEP) continue to be a matter of debate. We therefore investigated cardiac parameters during kainic-acid (KA) induced experimental epilepsy and antiepileptic treatment with lamotrigine (LTG).

METHODS

Epilepsy was induced in seven C57Bl/6 mice by injections of KA (20 mg/kg) on days 1 and 5, which produced severe acute seizures and spontaneous seizures 10 days later. Treatment with LTG (30 mg/kg) was initiated on day 11 and repeated on day 12. Continuous ECGs and ECoGs were collected telemetrically from freely moving mice.

RESULTS

Mice displayed pre-ictal but not ictal tachycardia. The squared coefficient of variation (SCV) of R-R intervals was significantly elevated 30s before and during seizures compared to control conditions. LTG produced a significant reversible increase in SCV and LF/HF ratio during slow-wave sleep (SWS), potentially indicative of sympatho-vagal imbalance during this state of vigilance, in which epileptic patients are known to be particularly vulnerable to SUDEP.

SIGNIFICANCE

The KA model used in this study permits the investigation of cardiac phenomena during epilepsy, as it features many effects found in human epileptic patients. Increased LF/HF, a known risk factor for cardiac disease, which is often found in epileptic patients, was observed as a side-effect of LTG treatment during SWS, suggesting that LTG may promote imbalance of the autonomous nervous system in epileptic mice.

Heart rate variability in normal and pathological sleep

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. The application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). In summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. The use of these tools could provide important information of clinical and prognostic relevance.

Sudden unexpected death in epilepsy: from the lab to the clinic setting

Sudden unexpected death in epilepsy (SUDEP) is defined as sudden, unexpected, witnessed or unwitnessed, non-traumatic, and non-drowning death in a patient with epilepsy. Sudden unexpected death in epilepsy is probably the most common cause of epilepsy-related deaths. Many predisposing and initiating factors may coexist and contribute to SUDEP, but the mechanisms are poorly understood. Cardiac and respiratory deregulation seems to have a major role in SUDEP. Here, we review several advances in understanding the mechanisms involved in SUDEP.

Autonomic epileptic seizures, autonomic effects of seizures, and SUDEP

Many generalized tonic-clonic seizures are accompanied by profound autonomic changes. However, autonomic seizures and autonomic status epilepticus can also be seen with specific electroclinical syndromes (Panayiotopoulos syndrome), etiologies, and localizations. Such autonomic symptoms may impact cardiorespiratory function. While it is likely that several factors contribute to SUDEP, further study of both ictal respiratory and cardiac changes and underlying neuroanatomical mechanisms involved in autonomic seizure semiology are likely to provide important data to improve our understanding of the pathophysiology of this devastating condition. This paper will review the association between autonomic symptoms and epileptic seizures and will highlight the work of three young investigators. Drs. Lisa Bateman and Brian Moseley will review their work on cardiorespiratory effects of recorded seizures and how this assists in our understanding of SUDEP. Dr. John Millichap will review autonomic seizures and autonomic dysfunctions related to childhood epilepsy and will discuss the importance of expanded research efforts in this field.