Idiopathic Generalized Epilepsy: Misunderstandings, Challenges, and Opportunities

The idiopathic generalized epilepsies (IGE) make up a fifth of all epilepsies, but <1% of epilepsy research. This skew reflects misperceptions: diagnosis is straightforward, pathophysiology is understood, seizures are easily controlled, epilepsy is outgrown, morbidity and mortality are low, and surgical interventions are impossible. Emerging evidence reveals that patients with IGE may go undiagnosed or misdiagnosed with focal epilepsy if EEG or semiology have asymmetric or focal features. Genetic, electrophysiologic, and neuroimaging studies provide insights into pathophysiology, including overlaps and differences from focal epilepsies. IGE can begin in adulthood and patients have chronic and drug-resistant seizures. Neuromodulatory interventions for drug-resistant IGE are emerging. Rates of psychiatric and other comorbidities, including sudden unexpected death in epilepsy, parallel those in focal epilepsy. IGE is an understudied spectrum for which our diagnostic sensitivity and specificity, scientific understanding, and therapies remain inadequate.

Applying the ILAE diagnostic criteria for Lennox-Gastaut syndrome in the real-world setting: A multicenter retrospective cohort study.

OBJECTIVE: Lennox-Gastaut syndrome (LGS) is an archetypal developmental and epileptic encephalopathy, for which novel treatments are emerging. Diagnostic criteria for LGS have recently been defined by the International League Against Epilepsy (ILAE). We aimed to apply these criteria in a real-world setting. METHODS: We applied ILAE diagnostic criteria to a cohort of patients diagnosed with LGS by epileptologists following inpatient video-EEG monitoring (VEM) at tertiary comprehensive epilepsy centers between 1995 and 2015. We also assessed mortality in this cohort. RESULTS: Sixty patients diagnosed with LGS and had complete records available for review were identified. Among them, 29 (48%) patients met ILAE diagnostic criteria for LGS (ILAE-DC group). Thirty-one did not meet criteria (non-ILAE-DC) due to the absence of documented tonic seizures (n = 7), EEG features (n = 12), or both tonic seizures and EEG features (n = 10), intellectual disability (n = 1), or drug resistance (n = 1). The ILAE-DC group had a shorter duration of epilepsy at VEM than the non-ILAE-DC group (median = 12.0 years vs. 23.7 years, respectively; p = 0.015). The proportions of patients with multiple seizure types (100% vs. 96.7%), ≤2.5 Hz slow spike-and-wave EEG activity (100% vs. 90%), seizure-related injuries (27.6% vs. 25.8%), and mortality (standardized mortality ratio 4.60 vs. 5.12) were similar between the groups. SIGNIFICANCE: Up to 52% of patients diagnosed with LGS following VEM may not meet recently accepted ILAE criteria for LGS diagnosis. This may reflect both the limitations of retrospective medical record review and a historical tendency of applying the LGS diagnosis to a broad spectrum of severe, early-onset drug-resistant epilepsies with drop attacks. The ILAE criteria allow the delineation of LGS based on distinct electroclinical features, potentiating accurate diagnosis, prognostication, and management formulation. Nonetheless, mortality outcomes between those who did and did not meet ILAE diagnostic criteria for LGS were similarly poor, and both groups suffered high rates of seizure-related injury. PLAIN LANGUAGE SUMMARY: More than half of patients diagnosed with Lennox-Gastaut Syndrome (LGS) at three Australian epilepsy monitoring units between 1995 and 2015 did not meet the recently devised International League Against Epilepsy (ILAE) diagnostic criteria for LGS. Mortality was equally high in those who did and did not meet the ILAE diagnostic criteria, and seizure-related injury was common. The ILAE diagnostic criteria will guide accurate diagnosis, management, prognostication, and research in patients with LGS, however may be limited in their practical application to patients with a longer duration of epilepsy, or to those for whom detailed assessment is difficult.

EEG based automated seizure detection - A survey of medical professionals.

Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools. Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.

Sleep and respiratory abnormalities in adults with developmental and epileptic encephalopathies using polysomnography and video-EEG monitoring.

This study evaluated sleep and respiratory abnormalities, and their relationship with seizures, in adults with developmental and epileptic encephalopathies (DEEs). We studied consecutive adults with DEEs undergoing inpatient video-EEG monitoring and concurrent polysomnography between December 2011 and July 2022. Thirteen patients with DEEs were included (median age: 31 years, range: 20-50; 69.2% female): Lennox-Gastaut syndrome (n = 6), Lennox-Gastaut syndrome-like phenotype (n = 2), Landau-Kleffner syndrome (n = 1), epilepsy with myoclonic-atonic seizures (n = 1), and unclassified DEEs (n = 3). Sleep architecture was often fragmented by epileptiform discharges and seizures resulting in arousals (median arousal index: 29.0 per h, range: 5.1-65.3). Moderate-to-severe obstructive sleep apnea (OSA) was observed in seven patients (53.8%). Three patients (23.1%) had tonic seizures that frequently occurred with central apnea; one met criteria for mild central sleep apnea. Of the patients with tonic seizures, two had other identifiable seizure manifestations, but in one patient, central apnea was commonly the only discernable seizure manifestation. Polysomnography during video-EEG is an effective diagnostic tool in detecting sleep and seizure-related respiratory abnormalities. Clinically significant OSA may increase the risk of comorbid cardiovascular disease and premature mortality. Treatment of epilepsy may improve sleep quality, and conversely, improved sleep, may decrease seizure burden.

EEG datasets for seizure detection and prediction- A review.

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.

Cardiac structural and functional abnormalities in epilepsy: A systematic review and meta-analysis.

OBJECTIVE: Epilepsy is associated with an increased risk of cardiovascular disease and mortality. Whether cardiac structure and function are altered in epilepsy remains unclear. To address this, we conducted a systematic review and meta-analysis of studies evaluating cardiac structure and function in patients with epilepsy. METHODS: We searched the electronic databases MEDLINE, PubMed, COCHRANE, and Web of Science from inception to 31 December 2021. Primary outcomes of interest included left ventricular ejection fraction (LVEF) for studies reporting echocardiogram findings and cardiac weight and fibrosis for postmortem investigations. Study quality was assessed using the National Heart, Lung, and Blood Institute (NHLBI) assessment tools. RESULTS: Among the 10 case-control studies with epilepsy patients (n = 515) and healthy controls (n = 445), LVEF was significantly decreased in epilepsy group compared with controls (MD: -1.80; 95% confidence interval [CI]: -3.56 to -0.04; P = 0.045), whereas A-wave velocity (MD: 4.73; 95% CI: 1.87-7.60; P = 0.001), E/e' ratio (MD: 0.39; 95% CI: 0.06-0.71; P = 0.019), and isovolumic relaxation time (MD: 10.18; 95% CI: 2.05-18.32; P = 0.014) were increased in epilepsy, compared with controls. A pooled analysis was performed in sudden unexpected death in epilepsy (SUDEP) cases with autopsy data (n = 714). Among SUDEP cases, the prevalence of cardiac hypertrophy was 16% (95% CI: 9%-23%); cardiac fibrosis was 20% (95% CI: 15%-26%). We found no marked differences in cardiac hypertrophy, heart weight, or cardiac fibrosis between SUDEP cases and epilepsy controls. SIGNIFICANCE: Our findings suggest that epilepsy is associated with altered diastolic and systolic echocardiogram parameters compared with healthy controls. Notably, SUDEP does not appear to be associated with a higher incidence of structural cardiac abnormalities, compared with non-SUDEP epilepsy controls. Longitudinal studies are needed to understand the prognostic significance of such changes. Echocardiography may be a useful noninvasive diagnostic test in epilepsy population.

Risk of sudden unexpected death in epilepsy (SUDEP) with lamotrigine and other sodium channel-modulating antiseizure medications.

OBJECTIVE: In vitro data prompted U.S Food and Drug Administration warnings that lamotrigine, a common sodium channel modulating anti-seizure medication (NaM-ASM), could increase the risk of sudden death in patients with structural or ischaemic cardiac disease, however, its implications for Sudden Unexpected Death in Epilepsy (SUDEP) are unclear. METHODS: This retrospective, nested case-control study identified 101 sudden unexpected death in epilepsy (SUDEP) cases and 199 living epilepsy controls from Epilepsy Monitoring Units (EMUs) in Australia and the USA. Differences in proportions of lamotrigine and NaM-ASM use were compared between cases and controls at the time of admission, and survival analyses from the time of admission up to 16 years were conducted. Multivariable logistic regression and survival analyses compared each ASM subgroup adjusting for SUDEP risk factors. RESULTS: Proportions of cases and controls prescribed lamotrigine (P = 0.166), one NaM-ASM (P = 0.80), or ≥2NaM-ASMs (P = 0.447) at EMU admission were not significantly different. Patients taking lamotrigine (adjusted hazard ratio [aHR] = 0.56; P = 0.054), one NaM-ASM (aHR = 0.8; P = 0.588) or ≥2 NaM-ASMs (aHR = 0.49; P = 0.139) at EMU admission were not at increased SUDEP risk up to 16 years following admission. Active tonic-clonic seizures at EMU admission associated with >2-fold SUDEP risk, irrespective of lamotrigine (aHR = 2.24; P = 0.031) or NaM-ASM use (aHR = 2.25; P = 0.029). Sensitivity analyses accounting for incomplete ASM data at follow-up suggest undetected changes to ASM use are unlikely to alter our results. SIGNIFICANCE: This study provides additional evidence that lamotrigine and other NaM-ASMs are unlikely to be associated with an increased long-term risk of SUDEP, up to 16 years post-EMU admission.

Serotonin receptor expression in hippocampus and temporal cortex of temporal lobe epilepsy patients by postictal generalized electroencephalographic suppression duration.

OBJECTIVE: Prolonged postictal generalized electroencephalographic suppression (PGES) is a potential biomarker for sudden unexpected death in epilepsy (SUDEP), which may be associated with dysfunctional autonomic responses and serotonin signaling. To better understand molecular mechanisms, PGES duration was correlated to 5HT1A and 5HT2A receptor protein expression and RNAseq from resected hippocampus and temporal cortex of temporal lobe epilepsy patients with seizures recorded in preoperative evaluation. METHODS: Analyses included 36 cases (age = 14-64 years, age at epilepsy onset = 0-51 years, epilepsy duration = 2-53 years, PGES duration = 0-93 s), with 13 cases in all hippocampal analyses. 5HT1A and 5HT2A protein was evaluated by Western blot and histologically in hippocampus (n = 16) and temporal cortex (n = 9). We correlated PGES duration to our previous RNAseq dataset for serotonin receptor expression and signaling pathways, as well as weighted gene correlation network analysis (WGCNA) to identify correlated gene clusters. RESULTS: In hippocampus, 5HT2A protein by Western blot positively correlated with PGES duration (p = .0024, R2  = .52), but 5HT1A did not (p = .87, R2  = .0020). In temporal cortex, 5HT1A and 5HT2A had lower expression and did not correlate with PGES duration. Histologically, PGES duration did not correlate with 5HT1A or 5HT2A expression in hippocampal CA4, dentate gyrus, or temporal cortex. RNAseq identified two serotonin receptors with expression that correlated with PGES duration in an exploratory analysis: HTR3B negatively correlated (p = .043, R2  = .26) and HTR4 positively correlated (p = .049, R2  = .25). WGCNA identified four modules correlated with PGES duration, including positive correlation with synaptic transcripts (p = .040, Pearson correlation r = .52), particularly potassium channels (KCNA4, KCNC4, KCNH1, KCNIP4, KCNJ3, KCNJ6, KCNK1). No modules were associated with serotonin receptor signaling. SIGNIFICANCE: Higher hippocampal 5HT2A receptor protein and potassium channel transcripts may reflect underlying mechanisms contributing to or resulting from prolonged PGES. Future studies with larger cohorts should assess functional analyses and additional brain regions to elucidate mechanisms underlying PGES and SUDEP risk.

Preferences and User Experiences of Wearable Devices in Epilepsy: A Systematic Review and Mixed-Methods Synthesis.

BACKGROUND AND OBJECTIVES: To examine the preferences and user experiences of people with epilepsy and caregivers regarding automated wearable seizure detection devices. METHODS: We performed a mixed-methods systematic review. We searched electronic databases for original peer-reviewed publications between January 1, 2000, and May 26, 2021. Key search terms included "epilepsy," "seizure," "wearable," and "non-invasive." We performed a descriptive and qualitative thematic analysis of the studies included according to the technology acceptance model. Full texts of the discussion sections were further analyzed to identify word frequency and word mapping. RESULTS: Twenty-two observational studies were identified. Collectively, they comprised responses from 3,299 participants including patients with epilepsy, caregivers, and healthcare workers. Sixteen studies examined user preferences, 5 examined user experiences, and 1 examined both experiences and preferences. Important preferences for wearables included improving care, cost, accuracy, and design. Patients desired real-time detection with a latency of ≤15 minutes from seizure occurrence, along with high sensitivity (≥90%) and low false alarm rates. Device-related costs were a major factor for device acceptance, where device costs of <$300 USD and a monthly subscription fee of <$20 USD were preferred. Despite being a major driver of wearable-based technologies, sudden unexpected death in epilepsy was rarely discussed. Among studies evaluating user experiences, there was a greater acceptance toward wristwatches. Thematic coding analysis showed that attitudes toward device use and perceived usefulness were reported consistently. Word mapping identified "specificity," "cost," and "battery" as key single terms and "battery life," "insurance coverage," "prediction/detection quality," and the effect of devices on "daily life" as key bigrams. DISCUSSION: User acceptance of wearable technology for seizure detection was strongly influenced by accuracy, design, comfort, and cost. Our findings emphasize the need for standardized and validated tools to comprehensively examine preferences and user experiences of wearable devices in this population using the themes identified in this study. Greater efforts to incorporate perspectives and user experiences in developing wearables for seizure detection, particularly in community-based settings, are needed. TRIAL REGISTRATION INFORMATION: PROSPERO Registration CRD42020193565.

Automated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis.

OBJECTIVE: This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES). METHODS: We conducted a systematic review and meta-analysis of studies reported up to November 15, 2021. We included studies that used video-electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection. RESULTS: Twenty-eight studies met the criteria for the systematic review, of which 23 were eligible for meta-analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic-clonic seizures using wrist-worn and/or ankle-worn devices to measure three-dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic-clonic seizures was .91 (95% confidence interval [CI] = .85-.96, I2  = 83.8%); sensitivity was similar between the wrist-worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7-2.6, I2  = 99.7%); FAR was higher in wrist-worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic-clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies. SIGNIFICANCE: Reported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic-clonic seizures during limited recording time in a video-EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.

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.

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study.

Objective: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods: This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions: Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

Association of Short-term Heart Rate Variability and Sudden Unexpected Death in Epilepsy.

BACKGROUND AND OBJECTIVES: We compared heart rate variability (HRV) in sudden unexpected death in epilepsy (SUDEP) cases and living epilepsy controls. METHODS: This international, multicenter, retrospective, nested case-control study examined patients admitted for video-EEG monitoring (VEM) between January 1, 2003, and December 31, 2014, and subsequently died of SUDEP. Time domain and frequency domain components were extracted from 5-minute interictal ECG recordings during sleep and wakefulness from SUDEP cases and controls. RESULTS: We identified 31 SUDEP cases and 56 controls. Normalized low-frequency power (LFP) during wakefulness was lower in SUDEP cases (median 42.5, interquartile range [IQR] 32.6-52.6) than epilepsy controls (55.5, IQR 40.7-68.9; p = 0.015, critical value = 0.025). In the multivariable model, normalized LFP was lower in SUDEP cases compared to controls (contrast -11.01, 95% confidence interval [CI] -20.29 to 1.73; p = 0.020, critical value = 0.025). There was a negative correlation between LFP and the latency to SUDEP, where each 1% incremental reduction in normalized LFP conferred a 2.7% decrease in the latency to SUDEP (95% CI 0.95-0.995; p = 0.017, critical value = 0.025). Increased survival duration from VEM to SUDEP was associated with higher normalized high-frequency power (HFP; p = 0.002, critical value = 0.025). The survival model with normalized LFP was associated with SUDEP (c statistic 0.66, 95% CI 0.55-0.77), which nonsignificantly increased with the addition of normalized HFP (c statistic 0.70, 95% CI 0.59-0.81; p = 0.209). CONCLUSIONS: Reduced short-term LFP, which is a validated biomarker for sudden death, was associated with SUDEP. Increased HFP was associated with longer survival and may be cardioprotective in SUDEP. HRV quantification may help stratify individual SUDEP risk. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in patients with epilepsy, some measures of HRV are associated with SUDEP.

Altered cardiac structure and function is related to seizure frequency in a rat model of chronic acquired temporal lobe epilepsy.

OBJECTIVE: This study aimed to prospectively examine cardiac structure and function in the kainic acid-induced post-status epilepticus (post-KA SE) model of chronic acquired temporal lobe epilepsy (TLE), specifically to examine for changes between the pre-epileptic, early epileptogenesis and the chronic epilepsy stages. We also aimed to examine whether any changes related to the seizure frequency in individual animals. METHODS: Four hours of SE was induced in 9 male Wistar rats at 10 weeks of age, with 8 saline treated matched control rats. Echocardiography was performed prior to the induction of SE, two- and 10-weeks post-SE. Two weeks of continuous video-EEG and simultaneous ECG recordings were acquired for two weeks from 11 weeks post-KA SE. The video-EEG recordings were analyzed blindly to quantify the number and severity of spontaneous seizures, and the ECG recordings analyzed for measures of heart rate variability (HRV). PicroSirius red histology was performed to assess cardiac fibrosis, and intracellular Ca2+ levels and cell contractility were measured by microfluorimetry. RESULTS: All 9 post-KA SE rats were demonstrated to have spontaneous recurrent seizures on the two-week video-EEG recording acquired from 11 weeks SE (seizure frequency ranging from 0.3 to 10.6 seizures/day with the seizure durations from 11 to 62 s), and none of the 8 control rats. Left ventricular wall thickness was thinner, left ventricular internal dimension was shorter, and ejection fraction was significantly decreased in chronically epileptic rats, and was negatively correlated to seizure frequency in individual rats. Diastolic dysfunction was evident in chronically epileptic rats by a decrease in mitral valve deceleration time and an increase in E/E` ratio. Measures of HRV were reduced in the chronically epileptic rats, indicating abnormalities of cardiac autonomic function. Cardiac fibrosis was significantly increased in epileptic rats, positively correlated to seizure frequency, and negatively correlated to ejection fraction. The cardiac fibrosis was not a consequence of direct effect of KA toxicity, as it was not seen in the 6/10 rats from separate cohort that received similar doses of KA but did not go into SE. Cardiomyocyte length, width, volume, and rate of cell lengthening and shortening were significantly reduced in epileptic rats. SIGNIFICANCE: The results from this study demonstrate that chronic epilepsy in the post-KA SE rat model of TLE is associated with a progressive deterioration in cardiac structure and function, with a restrictive cardiomyopathy associated with myocardial fibrosis. Positive correlations between seizure frequency and the severity of the cardiac changes were identified. These results provide new insights into the pathophysiology of cardiac disease in chronic epilepsy, and may have relevance for the heterogeneous mechanisms that place these people at risk of sudden unexplained death.

Interictal autonomic dysfunction.

PURPOSE OF REVIEW: Epilepsy is associated with autonomic dysfunction. Here, we provide an up-to-date review on measures of interictal autonomic function, focusing on heart rate variability (HRV), baroreflex sensitivity (BRS) and electrodermal activity (EDA). RECENT FINDINGS: Resting HRV, BRS and EDA are altered in patients with epilepsy compared with healthy controls. A larger body of work is available for HRV compared with BRS and EDA, and points to interictal HRV derangements across a wide range of epilepsies, including focal, generalized, and combined generalized and focal epilepsies. HRV alterations are most pronounced in temporal lobe epilepsy, Dravet syndrome and drug-resistant and chronic epilepsies. There are conflicting data on the effect of antiseizure medications on measures of interictal autonomic function. However, carbamazepine has been associated with decreased HRV. Epilepsy surgery and vagus nerve stimulation do not appear to have substantial impact on measures of interictal autonomic function but well designed studies are lacking. SUMMARY: Patients with epilepsy, particularly those with longstanding uncontrolled seizures, have measurable alterations of resting autonomic function. These alterations may be relevant to the increased risk of premature mortality in epilepsy, including sudden unexpected death in epilepsy, which warrants investigation in future research.

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review.

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.

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.

Mortality in patients with psychogenic nonepileptic seizures.

OBJECTIVE: To investigate the hypothesis that patients diagnosed with psychogenic nonepileptic seizures (PNES) on video-EEG monitoring (VEM) have increased mortality by comparison to the general population. METHODS: This retrospective cohort study included patients evaluated in VEM units of 3 tertiary hospitals in Melbourne, Australia, between January 1, 1995, and December 31, 2015. Diagnosis was based on consensus opinion of experienced epileptologists and neuropsychiatrists at each hospital. Mortality was determined in patients diagnosed with PNES, epilepsy, or both conditions by linkage to the Australian National Death Index. Lifetime history of psychiatric disorders in PNES was determined from formal neuropsychiatric reports. RESULTS: A total of 5,508 patients underwent VEM. A total of 674 (12.2%) were diagnosed with PNES, 3064 (55.6%) with epilepsy, 175 (3.2%) with both conditions, and 1,595 (29.0%) received other diagnoses or had no diagnosis made. The standardized mortality ratio (SMR) of patients diagnosed with PNES was 2.5 (95% confidence interval [CI] 2.0-3.3). Those younger than 30 had an 8-fold higher risk of death (95% CI 3.4-19.8). Direct comparison revealed no significant difference in mortality rate between diagnostic groups. Among deaths in patients diagnosed with PNES (n = 55), external causes contributed 18%, with 20% of deaths in those younger than 50 years attributed to suicide, and "epilepsy" was recorded as the cause of death in 24%. CONCLUSIONS: Patients diagnosed with PNES have a SMR 2.5 times above the general population, dying at a rate comparable to those with drug-resistant epilepsy. This emphasizes the importance of prompt diagnosis, identification of risk factors, and implementation of appropriate strategies to prevent potential avoidable deaths.