Early epileptiform EEG activity in infants with tuberous sclerosis complex predicts epilepsy and neurodevelopmental outcomes.

OBJECTIVE: To study the association between timing and characteristics of the first electroencephalography (EEG) with epileptiform discharges (ED-EEG) and epilepsy and neurodevelopment at 24 months in infants with tuberous sclerosis complex (TSC). METHODS: Patients enrolled in the prospective Epileptogenesis in a genetic model of epilepsy - Tuberous sclerosis complex (EPISTOP) trial, had serial EEG monitoring until the age of 24 months. The timing and characteristics of the first ED-EEG were studied in relation to clinical outcome. Epilepsy-related outcomes were analyzed separately in a conventionally followed group (initiation of vigabatrin after seizure onset) and a preventive group (initiation of vigabatrin before seizures, but after appearance of interictal epileptiform discharges [IEDs]). RESULTS: Eighty-three infants with TSC were enrolled at a median age of 28 days (interquartile range [IQR] 14-54). Seventy-nine of 83 patients (95%) developed epileptiform discharges at a median age of 77 days (IQR 23-111). Patients with a pathogenic TSC2 variant were significantly younger (P-value .009) at first ED-EEG and more frequently had multifocal IED (P-value .042) than patients with a pathogenic TSC1 variant. A younger age at first ED-EEG was significantly associated with lower cognitive (P-value .010), language (P-value .001), and motor (P-value .013) developmental quotients at 24 months. In the conventional group, 48 of 60 developed seizures. In this group, the presence of focal slowing on the first ED-EEG was predictive of earlier seizure onset (P-value .030). Earlier recording of epileptiform discharges (P-value .019), especially when multifocal (P-value .026) was associated with higher risk of drug-resistant epilepsy. In the preventive group, timing, distribution of IED, or focal slowing, was not associated with the epilepsy outcomes. However, when multifocal IEDs were present on the first ED-EEG, preventive treatment delayed the onset of seizures significantly (P-value <.001). SIGNIFICANCE: Early EEG findings help to identify TSC infants at risk of severe epilepsy and neurodevelopmental delay and those who may benefit from preventive treatment with vigabatrin.

EEG phase synchronization during absence seizures.

Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.

Absence Seizure Detection Algorithm for Portable EEG Devices.

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics.

Tuberous Sclerosis Complex (TSC) is a multisystem genetic disorder with a high risk of early-onset epilepsy and a high prevalence of neurodevelopmental comorbidities, including intellectual disability and autism spectrum disorder (ASD). Therefore, TSC is an interesting disease model to investigate early biomarkers of neurodevelopmental comorbidities when interventions are favourable. We investigated whether early EEG characteristics can be used to predict neurodevelopment in infants with TSC. The first recorded EEG of 64 infants with TSC, enrolled in the international prospective EPISTOP trial (recorded at a median gestational age 42 4/7 weeks) was first visually assessed. EEG characteristics were correlated with ASD risk based on the ADOS-2 score, and cognitive, language, and motor developmental quotients (Bayley Scales of Infant and Toddler Development III) at the age of 24 months. Quantitative EEG analysis was used to validate the relationship between EEG background abnormalities and ASD risk. An abnormal first EEG (OR = 4.1, p-value = 0.027) and more specifically a dysmature EEG background (OR = 4.6, p-value = 0.017) was associated with a higher probability of ASD traits at the age of 24 months. This association between an early abnormal EEG and ASD risk remained significant in a multivariable model, adjusting for mutation and treatment (adjusted OR = 4.2, p-value = 0.029). A dysmature EEG background was also associated with lower cognitive (p-value = 0.029), language (p-value = 0.001), and motor (p-value = 0.017) developmental quotients at the age of 24 months. Our findings suggest that early EEG characteristics in newborns and infants with TSC can be used to predict neurodevelopmental comorbidities.

Preventive Antiepileptic Treatment in Tuberous Sclerosis Complex: A Long-Term, Prospective Trial.

BACKGROUD: Drug-resistant epilepsy is the main risk factor for future intellectual disability in patients with tuberous sclerosis complex. Clinical epileptic seizures are often preceded by electroencephalographic changes, which provide an opportunity for preventive treatment. We evaluated the neuropsychologic and epilepsy outcomes at school age in children with tuberous sclerosis complex who received preventive antiepileptic treatment in infancy. METHODS: We performed a prospective, nonrandomized clinical trial with 14 infants diagnosed with tuberous sclerosis complex in whom serial electroencephalographic recordings were performed and preventive treatment with vigabatrin initiated when active epileptic discharges were detected. An age-matched control group consisted of 31 infants with tuberous sclerosis complex in whom treatment with vigabatrin was given only after onset of clinical seizures. Results of clinical assessment of epilepsy and cognitive outcomes were analyzed. RESULTS: All patients in the preventive group (n = 14) and 25 of 31 patients in the standard treatment group were followed through minimum age five years, median 8.8 and 8.0 years in the preventive and standard groups, respectively. The median intelligence quotient was 94 for the preventive group when compared with 46 for the standard group (P < 0.03). Seven of 14 patients (50%) in the preventive group never had a clinical seizure when compared with one of 25 patients (5%) in the standard treatment group (P = 0.001). CONCLUSIONS: This study provides evidence that preventive antiepileptic treatment in infants with tuberous sclerosis complex improves long-term epilepsy control and cognitive outcome at school age.