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

Aborted sudden cardiac death in a young patient with epilepsy and the Gorlin Goltz syndrome

Epilepsy is one of the most common chronical neurological conditions affecting over 50 million people worldwide. In addition to the stigma and discrimination, individuals with epilepsy suffer from a nearly three-fold increased risk of premature death compared to the general population. Although these premature deaths occur due to multiple causes, sudden unexpected death in epilepsy (SUDEP) still challenges neurologists and clinicians dealing with individuals with epilepsy. Recently, an increased interest in cardiac outcomes related to acute seizures and chronic epilepsy resulted in the groundbreaking development of the "epileptic heart" concept, and sudden cardiac death in individuals with epilepsy, which is 4.5 times as frequent as SUDEP according to some observational data, has gained more attention. As we gather information and learn about possible comorbidities and consequences of seizures and/or chronic epilepsy, we present a clinical case of a young patient with an unusual association of epilepsy, the Gorlin Goltz syndrome, and a cardiac fibroma with Wolf-Parkinson-White (WPW), who had multiple aborted cardiac arrests. Diagnostic challenges and multiple possible causes of sudden cardiac death in this single patient report are discussed.

Impaired post-sleep apnea autonomic arousals in patients with drug-resistant epilepsy

OBJECTIVE

Sudden and unexpected deaths in epilepsy (SUDEP) pathophysiology may involve an interaction between respiratory dysfunction and sleep/wake state regulation. We investigated whether patients with epilepsy exhibit impaired sleep apnea-related arousals.

METHODS

Patients with drug-resistant (N = 20) or drug-sensitive (N = 20) epilepsy and obstructive sleep apnea, as well as patients with sleep apnea but without epilepsy (controls, N = 20) were included. We explored (1) the respiratory arousal threshold based on nadir oxygen saturation, apnea-hypopnea index, and fraction of hypopnea among respiratory events; (2) the cardiac autonomic response to apnea/hypopnea quantified as percentages of changes from the baseline in RR intervals (RRI), high (HF) and low (LF) frequency powers, and LF/HF.

RESULTS

The respiratory arousal threshold did not differ between groups. At arousal onset, RRI decreased (-9.42%) and LF power (179%) and LF/HF ratio (190%) increased. This was followed by an increase in HF power (118%), p < 0.05. The RRI decrease was lower in drug-resistant (-7.40%) than in drug-sensitive patients (-9.94%) and controls (-10.91%), p < 0.05. LF and HF power increases were higher in drug-resistant (188%/126%) than in drug-sensitive patients (172%/126%) and controls (177%/115%), p < 0.05.

CONCLUSIONS

Cardiac reactivity following sleep apnea is impaired in drug-resistant epilepsy.

SIGNIFICANCE

This autonomic dysfunction might contribute to SUDEP pathophysiology.

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.

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.

Exploring Autonomic Alterations during Seizures in Temporal Lobe Epilepsy: Insights from a Heart-Rate Variability Analysis

Epilepsy's impact on cardiovascular function and autonomic regulation, including heart-rate variability, is complex and may contribute to sudden unexpected death in epilepsy (SUDEP). Lateralization of autonomic control in the brain remains the subject of debate; nevertheless, ultra-short-term heart-rate variability (HRV) analysis is a useful tool for understanding the pathophysiology of autonomic dysfunction in epilepsy patients. A retrospective study reviewed medical records of patients with temporal lobe epilepsy who underwent presurgical evaluations. Data from 75 patients were analyzed and HRV indices were extracted from electrocardiogram recordings of preictal, ictal, and postictal intervals. Various HRV indices were calculated, including time domain, frequency domain, and nonlinear indices, to assess autonomic function during different seizure intervals. The study found significant differences in HRV indices based on hemispheric laterality, language dominancy, hippocampal atrophy, amygdala enlargement, sustained theta activity, and seizure frequency. HRV indices such as the root mean square of successive differences between heartbeats, pNN50, normalized low-frequency, normalized high-frequency, and the low-frequency/high-frequency ratio exhibited significant differences during the ictal period. Language dominancy, hippocampal atrophy, amygdala enlargement, and sustained theta activity were also found to affect HRV. Seizure frequency was correlated with HRV indices, suggesting a potential relationship with the risk of SUDEP.

A unified hypothesis of SUDEP: Seizure-induced respiratory depression induced by adenosine may lead to SUDEP but can be prevented by autoresuscitation and other restorative respiratory response mechanisms mediated by the action of serotonin on the periaqueductal gray

Sudden unexpected death in epilepsy (SUDEP) is a major cause of death in people with epilepsy (PWE). Postictal apnea leading to cardiac arrest is the most common sequence of terminal events in witnessed cases of SUDEP, and postconvulsive central apnea has been proposed as a potential biomarker of SUDEP susceptibility. Research in SUDEP animal models has led to the serotonin and adenosine hypotheses of SUDEP. These neurotransmitters influence respiration, seizures, and lethality in animal models of SUDEP, and are implicated in human SUDEP cases. Adenosine released during seizures is proposed to be an important seizure termination mechanism. However, adenosine also depresses respiration, and this effect is mediated, in part, by inhibition of neuronal activity in subcortical structures that modulate respiration, including the periaqueductal gray (PAG). Drugs that enhance the action of adenosine increase postictal death in SUDEP models. Serotonin is also released during seizures, but enhances respiration in response to an elevated carbon dioxide level, which often occurs postictally. This effect of serotonin can potentially compensate, in part, for the adenosine-mediated respiratory depression, acting to facilitate autoresuscitation and other restorative respiratory response mechanisms. A number of drugs that enhance the action of serotonin prevent postictal death in several SUDEP models and reduce postictal respiratory depression in PWE. This effect of serotonergic drugs may be mediated, in part, by actions on brainstem sites that modulate respiration, including the PAG. Enhanced activity in the PAG increases respiration in response to hypoxia and other exigent conditions and can be activated by electrical stimulation. Thus, we propose the unifying hypothesis that seizure-induced adenosine release leads to respiratory depression. This can be reversed by serotonergic action on autoresuscitation and other restorative respiratory responses acting, in part, via the PAG. Therefore, we hypothesize that serotonergic or direct activation of this brainstem site may be a useful approach for SUDEP prevention.

ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures

Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure prediction approach. The recently proposed ECG-based seizure warning systems utilize machine learning models to classify a patient's condition. Such approaches require the incorporation of large, diverse, and thoroughly annotated ECG datasets, limiting their application potential. In this work, we investigate anomaly detection models in a patient-specific context with low supervision requirements. Specifically, we consider One-Class SVM (OCSVM), Minimum Covariance Determinant (MCD) Estimator, and Local Outlier Factor (LOF) models to quantify the novelty or abnormality of pre-ictal short-term (2-3 min) Heart Rate Variability (HRV) features of patients, trained on a reference interval considered to contain stable heart rate as the only form of supervision. Our models are evaluated against labels that were either hand-picked or automatically generated (weak labels) by a two-phase clustering procedure for samples of the "Post-Ictal Heart Rate Oscillations in Partial Epilepsy" (PIHROPE) dataset recorded by the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, achieving detection in 9 out of 10 cases, with average AUCs of over 93% across all models and warning times ranging from 6 to 30 min prior to seizure. The proposed anomaly detection and monitoring approach can potentially pave the way for early detection and warning of seizure incidents based on body sensor inputs.

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

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

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.

Evaluation of autonomic nervous system functions by using tilt table test and heart rate variability in epileptic children

Objective: The value of head-up tilt test (HUTT) for differential diagnosis of epilepsy and the autonomic nervous system functions in epileptic children using heart rate variability (HRV) are studied. Patients and Methods: The study group consisted of 16 children with idiopatic/criptogenic epilepsy and 12 controls. Heart rate, PR interval, corrected QT (QTc) interval, QT and QTc dispersion were calculated using 12-lead electrocardiogram (ECG), HRV analysis was performed using the Holter recordings obtained both during HUTT and throughout the day. Time domain parameters, standard deviation of all RR intervals (SDNN), the standard deviation of mean NN intervals in five-minutes recording (SDANN), mean standard deviation of NN intervals in five-minutes recordings (SDNNi), root mean square of successive differences (RMSSD), count divided by the total number of all NN intervals (pNN50) and frequency domain parameters low frequency (LF), high frequency (HF), lowfrequency/ high-frequency ratio (LF/HF) were calculated in both and compared between the two groups. Results: Head-up tilt test was positive in 4 epileptic children (25%), none of controls were positive. The heart rate of the patients were higher than the controls (p=0.015). LF/HF ratio in 24-hour Holter recordings, were significantly lower (1.13±0.6, 1.83±0.7 respectively, p=0.002); the SDANN during HUTT (28.7±20.2, 18.2 ± 19.9 respectively, p=0.024) were significantly higher in the patients than the controls. Conclusion: Head-up tilt test positivity is frequent in epileptic children, and cannot be used in differential diagnosis. HRV calculated both from 24 hour Holter recordings and Holter recordings under orthostatic stress were impaired in favour of parasympathetic system in epileptic children.

Reevaluation of risk factors for aneurysmal subarachnoid hemorrhage associated epilepsy

BACKGROUND

Symptomatic epilepsy is a common complication of aneurysmal subarachnoid hemorrhage (aSAH) associated with poor outcome. We sought to analyze the risk factors leading to post-SAH epilepsy.

METHODS

All consecutive aSAH cases treated between 01/2003 and 06/2016 were retrospectively included. Post-aSAH period was followed up to 03/2020 for the occurrence of epilepsy. Demographic characteristics and previous medical history of the patients, parameters of initial severity, performed treatments, certain early and late complications of aSAH, as well as routine laboratory and vital parameter measurements were collected. Functional outcome was assessed at discharge and 6 months after aSAH using the modified Rankin scale (mRS).

RESULTS

During the post-aSAH follow-up (median: 8.93 months/patient), 85 of 948 individuals (9%) in the final analysis developed symptomatic epilepsy (median: 3.43 months). In the majority of cases, epilepsy was diagnosed >3 weeks after aSAH (n = 67, 78.8%) and in survivors with poor outcome at discharge (mRS = 4-5, 15.8% vs. 5.3%, p < 0.0001). Of over 150 analyzed potential risk factors, the following parameters were independently associated with the risk of symptomatic epilepsy after aSAH: thyroid dysfunction (aHR = 1.81, p = 0.029), need for decompressive craniectomy (aHR = 2.32, p = 0.011) and shunt placement (aHR = 1.94, p = 0.022), prolonged tachycardia (≥4 days, aHR = 2.06, p = 0.025), as well as anemia signs (mean red blood cell count <3.6 × 10¹² /L [aHR = 2.4, p = 0.015] and mean hematocrit <31% [aHR = 2.13, p = 0.044]) during first 2 weeks after aSAH.

CONCLUSION

Symptomatic epilepsy occurs predominantly in individuals with poor outcome at discharge and after the acute phase of aSAH. Knowledge of risk factors associated with aSAH-related epilepsy might help in early identification and treatment of compromised individuals, and therefore, help to improve their outcome.

Drug-resistant epilepsy and mortality-Why and when do neuromodulation and epilepsy surgery reduce overall mortality

Patients with drug-resistant epilepsy have an increased mortality rate, with the majority of deaths being epilepsy related and 40% due to sudden unexpected death in epilepsy (SUDEP). The impact of epilepsy surgery on mortality has been investigated since the 1970s, with increased interest in this field during the past 15 years. We systematically reviewed studies investigating mortality rate in patients undergoing epilepsy surgery or neuromodulation therapies. The quality of available evidence proved heterogenous and often limited by significant methodological issues. Perioperative mortality following epilepsy surgery was found to be <1%. Meta-analysis of studies that directly compared patients who underwent surgery to those not operated following presurgical evaluation showed that the former have a two-fold lower risk of death and a three-fold lower risk of SUDEP compared to the latter (odds ratio [OR] 0.40, 95% confidence interval [CI]: 0.29-0.56; p < .0001 for overall mortality and OR 0.32, 95% CI: 0.18-0.57; p < .001 for SUDEP). Limited data are available regarding the risk of death and SUDEP in patients undergoing neuromodulation therapies, although some evidence indicates that vagus nerve stimulation might be associated with a lower risk of SUDEP. Several key questions remain to be addressed in future studies, considering the need to better inform patients about the long-term benefit-risk ratio of epilepsy surgery. Dedicated long-term prospective studies will thus be required to provide more personalized information on the impact of surgery and/or neuromodulation on the risk of death and SUDEP.

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

Nonspecific ventricular repolarization abnormalities: A wolf in sheep's clothing

The term nonspecific ventricular repolarization abnormalities refers to a set of minor alterations of the ST segment and/or the T wave. For a long time, they have been of little clinical interest as they do not translate into specific diagnoses. It has even been asserted that they constitute benign electrocardiographic findings. Their presence has been reported in various cardiovascular and non-cardiovascular diseases. However, it is frequently identified in apparently healthy asymptomatic people. A growing number of studies demonstrate their importance as predictors of cardiovascular morbidity and mortality, expanding their spectrum towards cardiovascular prevention. In light of the body of scientific evidence, it is imperative that the traditional view of nonspecific ventricular repolarization abnormalities changes.

Simultaneous Determination of Lamotrigine, Oxcarbazepine, Lacosamide, and Topiramate in Rat Plasma by Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry

This study established an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method to study the pharmacokinetics of four antiepileptic drugs, lamotrigine, oxcarbazepine, lacosamide, and topiramate, in rats after oral administration. The gradient elution was performed on a UPLC HSS T3 (2.1 mm × 100 mm, 1.8 μm) column with acetonitrile-0.1% formic acid as the mobile phase at a flow rate of 0.4 mL/min. Protein precipitation by acetonitrile was adopted for plasma sample pretreatment. Electrospray- (ESI-) positive/negative ion switching and multiple reaction monitoring (MRM) modes were adopted for ion quantitative determination of antiepileptic drugs. UPLC-MS/MS detection and Drug and Statistics (DAS) software fitting were performed to blood samples collected from rats after oral administration of lamotrigine, oxcarbazepine, lacosamide, and topiramate (5 mg/kg). All drugs examined showed linearity within 5–5000 ng/ml (R2 > 0.9987), the intraday accuracy was within 92%–108%, and the interday accuracy was within 93%–109%. The relative standard deviations (RSD) of intraday and interday were less than 15%. The matrix effect was within 91%–105%, and the recovery was better than 88%. The established UPLC-MS/MS method was successfully applied to the pharmacokinetic study of lamotrigine, oxcarbazepine, lacosamide, and topiramate in rats.

COVID-19 prevalence and mortality in people with epilepsy: A nation-wide multicenter study

BACKGROUND

To assess the prevalence, severity, and mortality of COVID-19 in people with epilepsy (PWE) and evaluate seizure control in PWE during and after COVID-19.

METHODS

Retrospective, observational, multicenter study conducted in 14 hospitals. Medical records of randomly selected PWE followed at neurology outpatient clinics were reviewed. Proportion of PWE with a positive test for SARS-CoV-2 during 2020 was calculated. Risk factors associated with COVID-19 and its morbimortality were evaluated.

RESULTS

2751 PWE were included, mean age 48.8 years (18-99), 72.4% had focal epilepsy, and 35% were drug-refractory. COVID-19 prevalence in PWE was 5.53%, while in the Spanish population was 4.26%. Proportion of admissions to hospital, ICU, and deaths in PWE were 17.1%, 2%, and 4.61% of COVID-19 cases, while in Spanish population were 10.81%, 0.95%, and 2.57%, respectively. A severe form of COVID-19 occurred in 11.8%; dyslipidemia, institutionalization at long-term care facilities, intellectual disability, and older age were associated risk factors. Older age, hypertension, dyslipidemia, cardiac disease, and institutionalization were associated with mortality from COVID-19. Seizure control was stable in 90.1% of PWE during acute COVID-19, while 8.6% reported an increase in seizure frequency. During post-COVID-19 follow-up, 4.6% reported seizure control worsening.

CONCLUSIONS

COVID-19 was moderately prevalent in PWE. One out of 5 patients required medical attention and 4.6% died due to COVID-19. Older age, dyslipidemia, institutionalization, and intellectual disability were significant risk factors associated with severe COVID-19. Seizure control remained stable during COVID-19 and throughout long-term follow-up in most PWE who contracted the infection.