Focal electroclinical features in generalized tonic-clonic seizures: Decision flowchart for a diagnostic challenge.

OBJECTIVE: Bilateral tonic-clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal EEG or semiology. METHODS: We retrospectively analyzed video-EEG recordings of 101 bilateral tonic-clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1-year follow-up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. RESULTS: Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7-Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal features with an accuracy of 96.6%. SIGNIFICANCE: Focal semiology associated with bilateral tonic-clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies.

Progressive slowing of clonic phase predicts postictal generalized EEG suppression.

OBJECTIVE: Postictal generalized electroencephalography (EEG) suppression (PGES) is a surrogate marker of sudden unexpected death in epilepsy (SUDEP). It is still unclear which ictal phenomena lead to prolonged PGES and increased risk of SUDEP. Semiology features of generalized convulsive seizure (GCS type 1) have been reported as a predictor of prolonged PGES. Progressive slowing of clonic phase (PSCP) has been observed in GCSs, with gradually increasing inhibitory periods interrupting the tonic contractions. We hypothesized that PSCP is associated with prolonged PGES. METHODS: We analyzed 90 bilateral convulsive seizures in 50 consecutive patients (21 female; age: 11-62 years, median: 31 years) recruited to video-EEG monitoring. Five raters, blinded to all other data, independently assessed the presence of PSCP. PGES and seizure semiology were evaluated independently. We determined inter-rater agreement (IRA) for the presence of PSCP, and we evaluated its association, as well as that of other ictal features, with the occurrence of PGES, prolonged PGES (≥20 s) and very prolonged PGES (≥50 s) using multivariate logistic regression analysis. RESULTS: We found substantial IRA for the presence of PSCP (percent agreement: 80%; beyond-chance agreement coefficient: .655). PSCP was an independent predictor of the occurrence of PGES and prolonged PGES (p < .001). All seizures with very prolonged PGES had PSCP. GCS type 1 was an independent predictor of occurrence of PGES (p = .02) and prolonged PGES (p = .03) but not of very prolonged PGES. Only half of the seizures with very prolonged PGES were GCS type 1. SIGNIFICANCE: PSCP predicts prolonged PGES, emphasizing the importance of gradually increasing inhibitory phenomena at the end of the seizures. Our findings shed more light on the ictal phenomena leading to increased risk of SUDEP. These phenomena may provide basis for algorithms implemented into wearable devices for identifying GCS with increased risk of SUDEP.

Automated detection of nocturnal motor seizures using an audio-video system.

BACKGROUND: Unsupervised nocturnal tonic-clonic seizures (TCSs) may lead to sudden unexpected death in epilepsy (SUDEP). Major motor seizures (TCSs and hypermotor seizures) may lead to injuries. Our goal was to develop and validate an automated audio-video system for the real-time detection of major nocturnal motor seizures. METHODS: In this Phase-3 clinical validation study, we assessed the performance of automated detection of nocturnal motor seizures using audio-video streaming, computer vision and an artificial intelligence-based algorithm (Nelli). The detection threshold was predefined, the validation dataset was independent from the training dataset, patients were prospectively recruited, and the analysis was performed in real time. The gold standard was based on expert evaluation of long-term video electroencephalography (EEG). The primary outcome was the detection of nocturnal major motor seizures (TCSs and hypermotor seizures). The secondary outcome was the detection of other (minor) nocturnal motor seizures. RESULTS: We recruited 191 participants aged 1-72 years (median: 20 years), and we monitored them for 4183 h during the night. Device deficiency was present 10.5% of the time. Fifty-one patients had nocturnal motor seizures during the recording. The sensitivity for the major motor seizures was 93.7% (95% confidence interval: 69.8%-99.8%). The system detected all 11 TCS and four out of five (80%) hypermotor seizures. For the minor motor seizure types, the sensitivity was low (8.3%). The false detection rate was 0.16 per h. CONCLUSION: The Nelli system detects nocturnal major motor seizures with a high sensitivity and is suitable for implementation in institutions (hospitals, residential care facilities), where rapid interventions triggered by alarms can potentially reduce the risk of SUDEP and injuries.

Semiautomated classification of nocturnal seizures using video recordings.

OBJECTIVE: The objective of this study was to evaluate the accuracy of a semiautomated classification of nocturnal seizures using a hybrid system consisting of an artificial intelligence-based algorithm, which selects epochs with potential clinical relevance to be reviewed by human experts. METHODS: Consecutive patients with nocturnal motor seizures admitted for video-electroencephalographic long-term monitoring (LTM) were prospectively recruited. We determined the extent of data reduction by using the algorithm, and we evaluated the accuracy of seizure classification from the hybrid system compared with the gold standard of LTM. RESULTS: Forty consecutive patients (24 male; median age = 15 years) were analyzed. The algorithm reduced the duration of epochs to be reviewed to 14% of the total recording time (1874 h). There was a fair agreement beyond chance in seizure classification between the hybrid system and the gold standard (agreement coefficient = .33, 95% confidence interval = .20-.47). The hybrid system correctly identified all tonic-clonic and clonic seizures and 82% of focal motor seizures. However, there was low accuracy in identifying seizure types with more discrete or subtle motor phenomena. SIGNIFICANCE: Using a hybrid (algorithm-human) system for reviewing nocturnal video recordings significantly decreased the workload and provided accurate classification of major motor seizures (tonic-clonic, clonic, and focal motor seizures).

Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing.

OBJECTIVE: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. METHODS: We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one-channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full-array video-EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video-EEGs. RESULTS: We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%-88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%-100%). The average false detection rate was .53/h (95% CI = .32-.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was .823 (IQR = .57-1). For the total recording duration, F1 score was .74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. SIGNIFICANCE: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices.