Ictal quantitative surface electromyography correlates with postictal EEG suppression.

To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression

Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES. Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES. Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1–169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6–26.39, p = 0.0003), 7.17 (CI 1.29–39.76, p = 0.02), and 4.77 (CI 1.25–18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations. Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies

Age-specific periictal electroclinical features of generalized tonic-clonic seizures and potential risk of sudden unexpected death in epilepsy (SUDEP)

Generalized tonic–clonic seizure (GTCS) is the commonest seizure type associated with sudden unexpected death in epilepsy (SUDEP). This study examined the semiological and electroencephalographic differences (EEG) in the GTCSs of adults as compared with those of children. The rationale lies on epidemiological observations that have noted a tenfold higher incidence of SUDEP in adults.Weanalyzed the video-EEG data of 105 GTCS events in 61 consecutive patients (12 children, 23 seizure events and 49 adults, 82 seizure events) recruited from the Epilepsy Monitoring Unit. Semiological, EEG, and 3-channel EKG features were studied. Periictal seizure phase durations were analyzed including tonic, clonic, total seizure, postictal EEG suppression (PGES), and recovery phases. Heart rate variability (HRV)measures includingRMSSD (root mean square successive difference of RR intervals), SDNN (standard deviation of NN intervals), and SDSD (standard deviation of differences) were analyzed (including low frequency/high frequency power ratios) during preictal baseline and ictal and postictal phases. Generalized estimating equations (GEEs)were used to find associations between electroclinical features. Separate subgroup analyses were carried out on adult and pediatric age groups as well as medication groups (no antiepileptic medication cessation versus unchanged or reduced medication) during admission.Major differences were seen in adult and pediatric seizures with total seizure duration, tonic phase, PGES, and recovery phases being significantly shorter in children (p b 0.01). Generalized estimating equation analysis, using tonic phase duration as the dependent variable, found age to correlate significantly (p b 0.001), and this remained significant during subgroup analysis (adults and children) such that each 0.12-second increase in tonic phase duration correlated with a 1-second increase in PGES duration. Postictal EEG suppression durations were on average 28 s shorter in children. With cessation of medication, total seizure duration was significantly increased by a mean value of 8 s in children and 11 s in adults (p b 0.05). Tonic phase duration also significantly increased with medication cessation, and although PGES durations increased, this was not significant. Root mean square successive difference was negatively correlated with PGES duration (longer PGES durations were associated with decreased vagally mediated heart rate variability; p b 0.05) but not with tonic phase duration. This study clearly points out identifiable electroclinical differences between adult and pediatric GTCSs that may be relevant in explaining lower SUDEP risk in children. The findings suggest that some prolonged seizure phases and prolonged PGES duration may be electroclinical markers of SUDEP risk and merit further study

The yield of long-term electrocardiographic recordings in refractory focal epilepsy

OBJECTIVE: To determine the incidence of clinically relevant arrhythmias in refractory focal epilepsy and to assess the potential of postictal arrhythmias as risk markers for sudden unexpected death in epilepsy (SUDEP). METHODS: We recruited people with refractory focal epilepsy without signs of ictal asystole and who had at least one focal seizure per month and implanted a loop recorder with 2-year follow-up. The devices automatically record arrhythmias. Subjects and caregivers were instructed to make additional peri-ictal recordings. Clinically relevant arrhythmias were defined as asystole ≥ 6 seconds; atrial fibrillation 200 bpm and duration > 30 seconds; persistent sinus bradycardia < 40 bpm while awake; and second- or third-degree atrioventricular block and ventricular tachycardia/fibrillation. We performed 12-lead electrocardiography (ECG) and tilt table testing to identify non-seizure-related causes of asystole. RESULTS: We included 49 people and accumulated 1060 months of monitoring. A total of 16 474 seizures were reported, of which 4679 were captured on ECG. No clinically relevant arrhythmias were identified. Three people had a total of 18 short-lasting (<6 seconds) periods of asystole, resulting in an incidence of 2.91 events per 1000 patient-months. None of these coincided with a reported seizure; one was explained by micturition syncope. Other non-clinically relevant arrhythmias included paroxysmal atrial fibrillation (n = 2), supraventricular tachycardia (n = 1), and sinus tachycardia with a right bundle branch block configuration (n = 1). SIGNIFICANCE: We found no clinically relevant arrhythmias in people with refractory focal epilepsy during long-term follow-up. The absence of postictal arrhythmias does not support the use of loop recorders in people at high SUDEP risk

Association of Peri-ictal Brainstem Posturing With Seizure Severity and Breathing Compromise in Patients With Generalized Convulsive Seizures

OBJECTIVE: To analyze the association between peri-ictal brainstem posturing semiologies with postictal generalized electroencephalographic suppression (PGES) and breathing dysfunction in generalized convulsive seizures (GCS). METHODS: In this prospective, multicenter analysis of GCS, ictal brainstem semiology was classified as (1) decerebration (bilateral symmetric tonic arm extension), (2) decortication (bilateral symmetric tonic arm flexion only), (3) hemi-decerebration (unilateral tonic arm extension with contralateral flexion) and (4) absence of ictal tonic phase. Postictal posturing was also assessed. Respiration was monitored with thoracoabdominal belts, video, and pulse oximetry. RESULTS: Two hundred ninety-five seizures (180 patients) were analyzed. Ictal decerebration was observed in 122 of 295 (41.4%), decortication in 47 of 295 (15.9%), and hemi-decerebration in 28 of 295 (9.5%) seizures. Tonic phase was absent in 98 of 295 (33.2%) seizures. Postictal posturing occurred in 18 of 295 (6.1%) seizures. PGES risk increased with ictal decerebration (odds ratio [OR] 14.79, 95% confidence interval [CI] 6.18-35.39, p < 0.001), decortication (OR 11.26, 95% CI 2.96-42.93, p < 0.001), or hemi-decerebration (OR 48.56, 95% CI 6.07-388.78, p < 0.001). Ictal decerebration was associated with longer PGES (p = 0.011). Postictal posturing was associated with postconvulsive central apnea (PCCA) (p = 0.004), longer hypoxemia (p < 0.001), and Spo2 recovery (p = 0.035). CONCLUSIONS: Ictal brainstem semiology is associated with increased PGES risk. Ictal decerebration is associated with longer PGES. Postictal posturing is associated with a 6-fold increased risk of PCCA, longer hypoxemia, and Spo2 recovery. Peri-ictal brainstem posturing may be a surrogate biomarker for GCS severity identifiable without in-hospital monitoring. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that peri-ictal brainstem posturing is associated with the GCS with more prolonged PGES and more severe breathing dysfunction

A library of quantitative markers of seizure severity

OBJECTIVE: Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well as assessing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches, for electroencephalographic (EEG) monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures. METHODS: We analysed intracranial EEG (iEEG) recordings of 1009 seizures from 63 patients. For each seizure we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and post-ictal suppression of seizures. RESULTS: Quantitative EEG markers of seizure severity distinguished focal vs. subclinical seizures across patients. In individual patients 53% had a moderate to large difference (ranksum r>0.3, p<0.05) between focal and subclinical seizures in three or more markers. Circadian and longer-term changes in severity were found for the majority of patients. SIGNIFICANCE: We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities. © 2023 International League Against Epilepsy

Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

The heart of epilepsy: Cardiac comorbidity and sudden death

The research described in this thesis aims to increase understanding of cardiac comorbidities and sudden unexpected death in epilepsy (SUDEP). People with epilepsy have a three-fold increased risk of dying prematurely compared to the general population. Common contributors to this are cardiovascular comorbidities, of which I provide an overview. Cardiovascular conditions and epilepsy can both lead to transient loss of consciousness (TLOC) with overlapping semiology. Particularly, myoclonic jerks which are commonly observed during syncope can be mistaken for signs of epilepsy. A misdiagnosis with detrimental consequences. I provide evidence that a careful analysis of motor phenomena can distinguish the two conditions. SUDEP is the commonest direct epilepsy-related premature death (UK >500 people/year). It typically occurs following convulsive seizures (CS). Most victims are found prone and some suggested people should sleep supine. I assessed video-EEG recordings of 180 CS and demonstrated peri-ictal positions often change, and most ending prone turned during CS. Sleeping supine is thus unlikely to prevent a postictal prone position and reduce risk of SUDEP. Pathomechanisms underlying SUDEP are likely a combination of interacting cardiorespiratory and autonomic factors. People with Dravet syndrome (DS) have a particular high SUDEP risk. I show that 49% of reported deaths in DS are SUDEP cases, most <10 years (78%). In DS, SCN1A mutations are mostly found, encoding a sodium channel expressed in brain and heart. DS mouse models suggest a key role for peri-ictal cardiac arrhythmias in SUDEP. I conducted a multicentre observational study and recorded 547 seizures in 45 DS participants. No major peri-ictal arrhythmias were found. Peri-ictal QTc-lengthening was, however, more common in DS than controls. This may reflect unstable repolarisation and increased propensity for arrhythmias. Prospective data to determine whether these peri-ictal variables can predict SUDEP risk is warranted

Deep learning approach for epileptic seizure detection

Abstract. Epilepsy is the most common brain disorder that affects approximately fifty million people worldwide, according to the World Health Organization. The diagnosis of epilepsy relies on manual inspection of EEG, which is error-prone and time-consuming. Automated epileptic seizure detection of EEG signal can reduce the diagnosis time and facilitate targeting of treatment for patients. Current detection approaches mainly rely on the features that are designed manually by domain experts. The features are inflexible for the detection of a variety of complex patterns in a large amount of EEG data. Moreover, the EEG is non-stationary signal and seizure patterns vary across patients and recording sessions. EEG data always contain numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges deep learning approaches are examined in this paper. Deep learning methods were applied to a large publicly available dataset, the Children’s Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). The present study includes three experimental groups that are grouped based on the pre-processing steps. The experimental groups contain 3–4 experiments that differ between their objectives. The time-series EEG data is first pre-processed by certain filters and normalization techniques, and then the pre-processed signal was segmented into a sequence of non-overlapping epochs. Second, time series data were transformed into different representations of input signals. In this study time-series EEG signal, magnitude spectrograms, 1D-FFT, 2D-FFT, 2D-FFT magnitude spectrum and 2D-FFT phase spectrum were investigated and compared with each other. Third, time-domain or frequency-domain signals were used separately as a representation of input data of VGG or DenseNet 1D. The best result was achieved with magnitude spectrograms used as representation of input data in VGG model: accuracy of 0.98, sensitivity of 0.71 and specificity of 0.998 with subject dependent data. VGG along with magnitude spectrograms produced promising results for building personalized epileptic seizure detector. There was not enough data for VGG and DenseNet 1D to build subject-dependent classifier.Epileptisten kohtausten havaitseminen syväoppimisella lähestymistavalla. Tiivistelmä. Epilepsia on yleisin aivosairaus, joka Maailman terveysjärjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikä on virhealtista ja aikaa vievää. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vähentää diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmät tukeutuvat pääasiassa piirteisiin, jotka asiantuntijat ovat määritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta määrästä EEG-dataa. Lisäksi, EEG on epästationäärinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten välillä ja EEG-data sisältää aina useita kohinatyyppejä, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. Näihin haasteisiin vastaamiseksi tässä diplomityössä tarkastellaan soveltuvatko syväoppivat menetelmät epilepsian havaitsemiseen EEG-tallenteista. Aineistona käytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). Tämän työn tutkimus sisältää kolme koeryhmää, jotka eroavat toisistaan esikäsittelyvaiheiden osalta: aikasarja-EEG-data esikäsiteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja näin esikäsitelty signaali segmentoitiin epookkeihin. Kukin koeryhmä sisältää 3–4 koetta, jotka eroavat menetelmiltään ja tavoitteiltaan. Kussakin niistä epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. Tässä tutkimuksessa tutkittiin ja verrattiin keskenään EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriä. Näin saatuja aika- ja taajuusalueen signaaleja käytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina. Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tällöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstä riippuvaisella EEG-datalla. VGG yhdessä amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstä riippumattoman luokittelijan opettamiseksi

Dead in the Night: Sleep-Wake and Time-Of-Day Influences on Sudden Unexpected Death in Epilepsy

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related death in patients with refractory epilepsy. Convergent lines of evidence suggest that SUDEP occurs due to seizure induced perturbation of respiratory, cardiac, and electrocerebral function as well as potential predisposing factors. It is consistently observed that SUDEP happens more during the night and the early hours of the morning. The aim of this review is to discuss evidence from patient cases, clinical studies, and animal research which is pertinent to the nocturnality of SUDEP. There are a number of factors which might contribute to the nighttime predilection of SUDEP. These factors fall into four categories: influences of (1) being unwitnessed, (2) lying prone in bed, (3) sleep-wake state, and (4) circadian rhythms. During the night, seizures are more likely to be unwitnessed; therefore, it is less likely that another person would be able to administer a lifesaving intervention. Patients are more likely to be prone on a bed following a nocturnal seizure. Being prone in the accouterments of a bed during the postictal period might impair breathing and increase SUDEP risk. Sleep typically happens at night and seizures which emerge from sleep might be more dangerous. Lastly, there are circadian changes to physiology during the night which might facilitate SUDEP. These possible explanations for the nocturnality of SUDEP are not mutually exclusive. The increased rate of SUDEP during the night is likely multifactorial involving both situational factors, such as being without a witness and prone, and physiological changes due to the influence of sleep and circadian rhythms. Understanding the causal elements in the nocturnality of SUDEP may be critical to the development of effective preventive countermeasures