Latencies from intracranial seizure onset to ictal tachycardia: A comparison to surface EEG patterns and other clinical signs

Autonomic biosignals, seizure detection, and forecasting

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

Single-interface bioelectronic medicines - concept, clinical applications and preclinical data

Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.

Vagus nerve stimulation for conservative therapy-refractive epilepsy and depression

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

Comparison of traditional and closed loop vagus nerve stimulation for treatment of pediatric drug-resistant epilepsy: A propensity-matched retrospective cohort study

OBJECTIVE

For epilepsy patients with drug-resistant, unresectable epilepsy, vagus nerve stimulation (VNS) is an option for seizure control. Approximately 40-70% of patients will achieve ≥50% seizure reduction with VNS. New closed loop VNS models detect ictal tachycardia and responsively stimulate the vagus nerve. The effectiveness of closed loop VNS compared to traditional VNS for pediatric epilepsy is unknown.

METHODS

An 11-year retrospective electronic medical record review at Children's Hospital of Pittsburgh was performed. Patients with drug-resistant epilepsy who underwent VNS implantation were included. Patients were divided into groups based on VNS model: traditional versus closed loop. Those who transitioned from traditional to closed loop VNS were excluded. Given potential for selection bias, propensity scores matching was utilized to compare traditional to closed loop VNS patients. Patients with focal versus generalized epilepsy were also separately analyzed. The primary outcome was "VNS response", defined as at least 50% seizure frequency reduction from baseline.

RESULTS

A total of 320 patients were included in this sample. The percentage of matched patients (total n = 220: n = 179 traditional VNS, n = 41 closed loop VNS) who responded to VNS after one year of therapy was 43% for traditional VNS and 39% for closed loop VNS (p = 0.64). After two years of therapy, a higher proportion of closed loop VNS patients than traditional VNS patients responded to VNS among all subgroups, though no differences were statistically significant (p>0.05). Notably, for those with generalized epilepsy, 73% of closed loop patients responded to VNS compared to only 46% of traditional patients (p = 0.10). After two years of VNS therapy, patients were taking approximately the same quantity of antiseizure medications as baseline (change of +0.074 +/- 0.90 ) with no difference between VNS models (p = 0.87).

SIGNIFICANCE

Among pediatric patients with drug-resistant epilepsy, closed loop VNS trends towards a higher rate of VNS response after two years of treatment, especially among generalized epilepsy patients. Neither model of VNS allows patients to reduce antiseizure medication quantity after two years.

Autonomic manifestations of epilepsy: emerging pathways to sudden death?

Epileptic networks are intimately connected with the autonomic nervous system, as exemplified by a plethora of ictal (during a seizure) autonomic manifestations, including epigastric sensations, palpitations, goosebumps and syncope (fainting). Ictal autonomic changes might serve as diagnostic clues, provide targets for seizure detection and help us to understand the mechanisms that underlie sudden unexpected death in epilepsy (SUDEP). Autonomic alterations are generally more prominent in focal seizures originating from the temporal lobe, demonstrating the importance of limbic structures to the autonomic nervous system, and are particularly pronounced in focal-to-bilateral and generalized tonic-clonic seizures. The presence, type and severity of autonomic features are determined by the seizure onset zone, propagation pathways, lateralization and timing of the seizures, and the presence of interictal autonomic dysfunction. Evidence is mounting that not all autonomic manifestations are linked to SUDEP. In addition, experimental and clinical data emphasize the heterogeneity of SUDEP and its infrequent overlap with sudden cardiac death. Here, we review the spectrum and diagnostic value of the mostly benign and self-limiting autonomic manifestations of epilepsy. In particular, we focus on presentations that are likely to contribute to SUDEP and discuss how wearable devices might help to prevent SUDEP.

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

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

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection

We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states are constructed. These vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes. This flexibility allows our algorithm to identify the electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k-fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 11.57 s) in seizure onset detection, and higher specificity (97.31% vs. 94.84%) and accuracy (96.85% vs. 95.42%). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2% specificity loss). Using only the top 10% of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Learnings from 30 years of reported efficacy and safety of vagus nerve stimulation (VNS) for epilepsy treatment: A critical review

Three decades after its introduction as an adjuvant therapeutic option in the management of selective drug-resistant epilepsy cases (DRE), vagus nerve stimulation (VNS) retains growing interest. An implantable device was first approved for epilepsy in Europe in 1994 and in the United States (US) in 1997. Subsequent modifications improved the safety and the efficacy of the system. The most recent application of vagal neurostimulation is represented by transcutaneous devices that are claimed to have strong therapeutic potential. In this review, we sought to analyze the most meaningful available data describing the indications, safety and efficacy of the different approaches of VNS in clinical practice. Therefore, we identified studies reporting VNS efficacy and/or safety in epilepsy and its comorbidities from January 1990 to February 2020 from various databases including PubMed, Scopus, Cochrane, US government databases and VNS manufacturer published resources. In general, VNS efficacy becomes optimal around the sixth month of treatment and a 50-100 % seizure frequency reduction is achieved in approximately 45-65 % of the patients. However, some clinically relevant differences have been reported with specific factors such as epilepsy etiology or type, patient age as well as the delay of VNS therapy onset. VNS efficacy on seizure frequency has been demonstrated in both children and adults, in lesional and non-lesional cases, in focal and generalized epilepsies, on both seizures and epilepsy comorbidities. Regarding the latter, VNS can lead to an improvement of about 25-35 % in depression scores, 35 % in anxiety scores and 25 % in mood assessment scores. If non-invasive devices are undeniably safer, their efficacy is limited due to the scarcity of large cohort studies and the disparity of methodological approaches (study design and stimulation parameters). Overall, we believe that there is a progress margin for improving the safety of implantable devices and, above all, the effectiveness of the various VNS approaches.

[Mobile seizure monitoring in epilepsy patients]

Wearables are receiving much attention from both epilepsy patients and treating physicians, for monitoring of seizure frequency and warning of seizures. They are also of interest for the detection of seizure-associated risks of patients, for differential diagnosis of rare seizure types and prediction of seizure-prone periods. Accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture clinical seizure manifestations. Currently, a clinical use to document nocturnal motor seizures is feasible. In this review the available devices, data on the performance in the documentation of seizures, current options for clinical use and developments in data analysis are presented and critically discussed.

Semi-automatic quantification of seizure-related effects on heart activity

OBJECTIVE

Seizure-related modulation of heart rate (HR) was examined extensively in previous studies. However, the overall effect on HR attributable to epileptic seizures is difficult to determine, given the considerable fluctuations of HR before and during seizures. Here, we developed a semi-automatic procedure allowing quantification of the total impact of seizures on HR and determination of temporal relationships between seizure onset assessed by intracranial EEG (iEEG) and ECG.

METHODS

ECG and iEEG data of epilepsy patients undergoing video-EEG telemetry for epilepsy surgery with bilateral hippocampal depth electrodes were analysed retrospectively. Consecutive RR intervals and HR profiles were determined using R detection algorithms. Novel features including the normalized ictal area under the curve (niAUC), as well as the time point of ECG onset (HR breakpoint) were calculated. Selected HR features were compared to widely-used manually acquired measures. Data are given as median ± SD.

RESULTS

Fifteen patients had a total of 34 seizures with left-hippocampal and 37 seizures with right-hippocampal onset. HR increased by 9 ± 19% during seizures. Latency between iEEG seizure-onset to the HR breakpoint was 23 ± 22 s. No significant difference between left- and right-hippocampal seizures was observed with respect to HR increases, latencies and niAUC. A comparison between results of the semi-automatic and manual approach revealed that ictal HR changes showed a higher correlation (r = 0.6) than niAUC (r = 0.4).

CONCLUSIONS

The proposed semi-automatic approach to analyze continuous HR data provides useful tools for estimating the overall effect of seizures on HR in greater detail. Our results suggest that the side of hippocampal seizure onset has no significant effect on the latency and extent of ictal HR changes. The algorithms may be of further use in clinical research and the development of seizure detection devices.

Vagus nerve stimulation (VNS) therapy update

Epilepsy affects millions of people worldwide. Approximately one-third have pharmacoresistant epilepsy, and of these, the majority are not candidates for epilepsy surgery. Vagus nerve stimulation (VNS) therapy has been an option to treat pharmacoresistant seizures for 30 years. In this update, we will review the clinical data that support the device's efficacy in children, adolescents, and adults. We will also review its side-effect profile, quality of life and cost benefits, and the impact the device has on sudden unexpected death in epilepsy (SUDEP). We will then discuss candidate selection and provide guidance on dosing and future models. Vagus nerve stimulation therapy is an effective treatment for many seizure types and epilepsy syndromes with a predictable and benign side-effect profile that supports its role as the most commonly prescribed device to treat pharmacoresistant epilepsy. "This article is part of the Supplement issue Neurostimulation for Epilepsy."

Multimodal nocturnal seizure detection in a residential care setting: A long-term prospective trial

Objective

To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement.

Methods

In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers’ user experience.

Results

Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65–0.89). Median sensitivity per participant amounted to 86% (95% CI 77%–93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01–0.05); and the positive predictive value was 49% (95% CI 33%–64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%–80%, p < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively.

Conclusion

Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.

Automatic Computer-Based Detection of Epileptic Seizures

Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way.

Pre-ictal heart rate changes: A systematic review and meta-analysis

PURPOSE

To estimate the incidence of pre-ictal heart rate (HR) manifestations and to identify clinical and study-related factors modulating the estimate.

METHODS

We searched articles recording concurrent pre-ictal EEG and HR in adults and children with epilepsy. Pre-ictal HR changes were classified as HR reduction (HRR) or increase (HRI). Studies reporting the total number of seizures and the number of seizures with pre-ictal HR changes were included in a random-effects meta-analysis. A random-effects meta-regression was used to identify variables affecting study heterogeneity.

RESULTS

Thirty studies, including 1110 participants and 2957 seizures, were included. The meta-analysis showed a pooled incidence of pre-ictal HRI of 36/100 seizures (95% CI 22-50). The pre-ictal HRI incidence was 44/100 seizures (95% CI 33-55) in studies including temporal lobe epilepsy, 55/100 seizures (95% CI 41-68) in studies enrolling adults and 35/100 seizures (95% CI 16-58) when patients on antiepileptic drugs were included. The meta-regression showed that the age group, the length of the pre-ictal period, the incidence of ictal tachycardia and the time of onset of the pre-ictal HRI had a significant impact on estimates variability. The pooled incidence of pre-ictal HRR was 0/100 seizures (95% CI 0-1).

CONCLUSION

Review of bias evaluation and methods assessment disclosed several major limitations in the evidence-base. HR monitoring could be valuable to identify seizures prior to their apparent onset, opening the possibility to early interventions. Additional effort is necessary to delineate the target population who might benefit from its use and the mechanisms sustaining the pre-ictal cardiac changes.

Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG

Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording. The algorithm was tested on a dataset of more than 918[Formula: see text]h of data coming from 17 patients containing 127 complex partial and generalized partial seizures. The algorithm was able to detect 81.89% of the seizures, with on average 1.97 false alarms per hour. These results show a strong drop in the number of false alarms of more than 50% compared to other heart rate-based patient-independent algorithms from the literature, at the expense of a slightly higher detection delay of 17.8s on average.

[Invasive stimulation procedures and EEG diagnostics in epilepsy]

Stimulation has been performed experimentally and in small case series to treat epilepsy since the 1970s. Since the introduction of vagus nerve stimulation in 1997 and intracranial stimulation methods in 2011 into patient care, invasive stimulation has become a rapidly developing but infrequently used therapeutic option in Europe. Whereas vagus nerve stimulation is frequently used, particularly in the USA, intracranial stimulation differs in its regional availability. In order to improve the efficacy of stimulation, develop criteria for its use and assure low complication rates, a concentration on experienced centers and multicenter data acquisition and sharing are needed.Invasive electroencephalographic (EEG) monitoring with subdural electrodes and especially with stereotactically implanted depth electrodes have been used increasingly more often for presurgical evaluation in recent years. They are applied when non-invasive diagnostics show insufficient results to exactly identify the location and extent of the epileptogenic zone or cannot be adequately distinguished from eloquent cortex areas. Complications include intracranial hemorrhage, infections and increased intracranial pressure but lasting deficits or even death are rare (≤2 %). The outcome of invasive monitoring is inferior to non-invasive monitoring because of the higher degree of complexity of the cases; however, it is far superior to the seizure-free rates achieved by anticonvulsant drug treatment alone.

Ictal heart rate changes and the effects of vagus nerve stimulation for patients with refractory epilepsy

Vagus nerve stimulation (VNS) shows long-term efficiency worldwide in most pharmacoresistant patients with epilepsy; however, there are still a small number of patients who are non-responders to VNS therapy. It has been shown that VNS treatment outcomes for drug-resistant epilepsy may be predicted by preoperative heart-rate variability measurements and that patients with epilepsy with ictal tachycardia (IT) during seizures have good responses to VNS. However, few studies have reported the efficacy of VNS in patients with epilepsy with ictal bradycardia (IB) or normal heart rate (HR), and none have explored the possible mechanisms of VNS efficacy based on different HR types. HR during seizures varies, and we presume that different HRs during seizures may impact the effects of VNS. It has been shown that blood pressure in the human body needs to be maintained through the arterial baroreflex (ABR). VNS efficacy in patients with epilepsy with IT, IB, and normal HR during seizures may be related to ABR. Mechanical signals generated by VNS are similar to the autonomic nerve pathways and, thus, we propose the hypothesis that different HRs during seizures can predict VNS efficacy in patients. If VNS is highly efficient in patients with IT during seizures, VNS in patients with a normal HR during seizures may be less efficient, and may even be inefficient in patients with IB during seizures.

Patient-centered design criteria for wearable seizure detection devices

INTRODUCTION

Epilepsy is a common neurological condition. Seizure diary reports and patient- or caregiver-reported seizure counts are often inaccurate and underestimated. Many caregivers express stress and anxiety about the patient with epilepsy having seizures when they are not present. Therefore, a need exists for the ability to recognize and/or detect a seizure in the home setting. However, few studies have inquired on detection device features that are important to patients and their caregivers.

METHODS

A survey instrument utilizing a population of patients and caregivers was created to obtain information on the design criteria most desired for patients with epilepsy in regard to wearable devices.

RESULTS

One thousand one hundred sixty-eight responses were collected. Respondents thought that sensors for muscle signal (61.4%) and heart rate (58.0%) would be most helpful followed by the O₂ sensor (41.4%). There was more interest in these three sensor types than for an accelerometer (25.5%). There was very little interest in a microphone (8.9%), galvanic skin response sensor (8.0%), or a barometer (4.9%). Based on a rating scale of 1-5 with 5 being the most important, respondents felt that "detecting all seizures" (4.73) is the most important device feature followed by "text/email alerts" (4.53), "comfort" (4.46), and "battery life" (4.43) as an equally important group of features. Respondents felt that "not knowing device is for seizures" (2.60) and "multiple uses" (2.57) were equally the least important device features. Average ratings differed significantly across age groups for the following features: button, multiuse, not knowing device is for seizures, alarm, style, and text ability. The p-values were all<0.002. Eighty-two point five percent of respondents [95% confidence interval: 80.0%, 84.7%] were willing to pay more than $100 for a wearable seizure detection device, and 42.8% of respondents [95% confidence interval: 39.8%, 45.9%] were willing to pay more than $200.

CONCLUSIONS

Our survey results demonstrated that patients and caregivers have design features that are important to them in regard to a wearable seizure detection device. Overall, the ability to detect all seizures rated highest among respondents which continues to be an unmet need in the community with epilepsy in regard to seizure detection. Additional uses for a wearable were not as important. Based on our results, it is important that an alert (via test and/or email) for events be a portion of the system. A reasonable price point appears to be around $200 to $300. An accelerometer was less important to those surveyed when compared with the use of heart rate, oxygen saturation, or muscle twitches/signals. As further products become developed for use in other health arenas, it will be important to consider patient and caregiver desires in order to meet the need and address the gap in devices that currently exist.

Epileptic seizure onset detection based on EEG and ECG data fusion

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case.

Postictal immobility and generalized EEG suppression are associated with the severity of respiratory dysfunction

OBJECTIVE

The pathophysiology of sudden unexpected death in epilepsy (SUDEP) remains undetermined. Seizures are accompanied by respiratory dysfunction (RD). Postictal generalized electroencephalography (EEG) suppression (PGES) may follow generalized tonic-clonic seizures (GTCS). Following GTCS patients have impaired arousal and may be motionless. Patients with SUDEP are usually prone. Postictal immobility (PI) may contribute to SUDEP by not permitting repositioning of the head to allow unimpeded ventilation. To determine whether RD and/or ictal characteristics are associated with PI, we analyzed patients with GTCS in the epilepsy monitoring unit.

METHOD

We investigated for associations between PI duration and PGES, ictal/postictal oxygen saturation (SpO2 ), end-tidal CO2 (ETCO2 ), seizure localization, duration, and tonic and total convulsive phase duration. We investigated for linkage between PGES and these measures.

RESULTS

Seventy patients with 181 GTCS and available SpO2 and/or ETCO2 data were studied. Simple linear regression analysis by seizures showed that PI duration was associated with peak periictal ETCO2 (p = 0.03), duration of oxygen desaturation (p = 0.005) and with SpO2 nadir (p = 0.02). PI duration was not associated with tonic, convulsive phase or total seizure duration. Analysis by patients also showed significant association of PI with RD. Duration of PI was longer following seizures with PGES (p < 0.001). PGES was not associated with the tonic, convulsive phase or total seizure duration. SpO2 nadir was lower in seizures with PGES (p = 0.046), ETCO2 peak change (p = 0.003) was higher, and duration of ETCO2 elevation (p = 0.03) was longer. Multivariable regression analysis showed that PGES and severe RD were associated with PI duration.

SIGNIFICANCE

The duration of PI and presence of PGES are associated with periictal RD. The duration of PI is also associated with the presence of PGES. Seizure duration or duration of the convulsive phase is not associated with PI or PGES. Interventions aimed at reversing impaired arousal and PI may reduce SUDEP risk.