Semiology of hypermotor (hyperkinetic) seizures

Computer vision for automated seizure detection and classification: A systematic review

Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.

Effects of Botulinum Toxin Injection on Reducing Myogenic Artifacts during Video-EEG Monitoring: A Longitudinal Study

Medically refractory seizures affect one-third of patients with epilepsy (PwE), for whom epilepsy surgery is considered. Video electroencephalography (vEEG) monitoring is a fundamental tool for pre-operative seizure localization. Facial and cranial myogenic artifacts can obscure vEEG findings, thus interfering with seizure localization. Studies have shown the beneficial effects of botulinum toxin type A (BTX-A) injection into cranial muscles for reducing myogenic artifacts. This longitudinal study aimed to assess the effects of BTX-A injection on these artifacts. Twenty-two patients with medically refractory hypermotor seizures with daily seizure frequency and undetermined epilepsy localization were included in this study and underwent Dysport® injection (200 units) into the frontotemporal region. vEEG recordings were performed at baseline (one week before the injection), and at three days and six days post-injection. Before and after the injection, the amplitudes of myogenic artifacts were compared during various states (ictal, blinking, chewing, bruxism, head lateralization, scowling, talking, and yawning). BTX-A injection significantly reduced the amplitudes of EEG myogenic artifacts, except during blinking (day three) and talking (days three and six). On day six, significant reduction in EEG myogenic artifacts were noted during blinking, chewing, and bruxism for the greatest number of patients (95.5%, 90.9%, 81.8%), while significant reductions in EEG myogenic artifacts during talking, head lateralization, and ictal phase were associated with the least number of patients (22.7%, 36.3%, and 40.9%). Therefore, BTX-A injection could be a convenient method for filtering myogenic contamination, improving EEG interpretation, and facilitating seizure localization in patients with medically refractory seizures.

Cortical regions and networks of hyperkinetic seizures: Electrocorticography and diffusion tensor imaging study

OBJECTIVE

The present study investigated the cortical areas and networks responsible for hyperkinetic seizures by analyzing invasive recordings and diffusion tensor imaging (DTI) tractography.

METHODS

Seven patients with intractable focal epilepsy in whom hyperkinetic seizures were recorded during an invasive evaluation at Sapporo Medical University between January 2012 and March 2020 were enrolled in the present study. Intracranial recordings were analyzed to localize seizure-onset zones (SOZs) and symptomatogenic zones (spread areas at clinical onset). DTI was used to identify the subcortical fibers originating from SOZs.

RESULTS

Ten SOZs were located in four areas: (1) the inferior parietal lobule (two SOZs in two patients), (2) temporo-occipital junction (three SOZs in two patients), (3) medial temporal area (three SOZs in three patients) and (4) medial/lateral frontal lobe (two SOZs in two patients). Symptomatogenic zones appeared to be the premotor area, basal temporal area, temporo-occipital junction, and the postcentral gyrus/supramarginal gyrus. The tractographic analysis revealed that the inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), middle longitudinal fasciculus (MLF), arcuate fasciculus (AF)/superior longitudinal fasciculus (SLF) II, III, and cingulum bundle may be associated with hyperkinetic seizures.

CONCLUSION

The present results suggest the cortical areas (the inferior parietal lobule, temporo-occipital junction, medial temporal area, and medial/lateral frontal lobe) and subcortical fibers (IFOF, ILF, MLF, AF/SLFII, III, and the cingulum bundle) responsible for generating hyperkinetic seizures.

Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers. Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria. Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p < 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27). Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability. Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z).

Investigation of networks underlying hyperkinetic seizures utilizing ictal SPECT

Objective

To study neural networks involved in hyperkinetic seizures (HKS) using ictal SPECT.

Methods

We retrospectively identified 18 patients with HKS evaluated at the Cleveland Clinic between 2005 and 2015 with video-EEG monitoring and ictal SPECT. Semiology was confirmed by the consensus of 2 epileptologists' independent reviews and classified as type 1, 2, or 3 HKS. SPECT data were analyzed by 2 independent physicians using a z score of 1.5. Ictal hyperperfusion patterns for each group were analyzed visually and with SPM. Spatial normalization to Montreal Neurological Institute space for each patient’s data was performed, followed by flipping of data from patients with left-sided ictal onset to the right side. Finally, an average z score map for each group was calculated.

Results

Visual analysis and SPM identified different patterns of ictal hyperperfusion in the 3 subtypes of HKS. Type 1 seizures showed hyperperfusion in a more anteriorly located network involving the anterior insula, orbitofrontal cortex, cingulate, and anterior perisylvian region and rostral midbrain. Type 2 seizures were associated with hyperperfusion in a more caudally located network involving the orbitofrontal cortex, cingulate (middle and posterior), basal ganglia, thalami, and cerebellum. Type 3 seizures showed a mixed pattern of SPECT hyperperfusion involving the temporal pole and anterior perisylvian region.

Conclusions

Each of the 3 different semiologic subtypes of HKS is associated with distinct patterns of hyperperfusion, providing further insight into the neural networks involved. This knowledge may inform placement of invasive EEG electrodes in patients with HKS semiology undergoing presurgical evaluation.

Insights into sleep-related hyper-motor epilepsy: an Egyptian case series

Objective: Sleep-related hypermotor epilepsy (SHE) is a sleep-related focal epilepsy which is often misdiagnosed. Despite active pharmacological therapy in the management of this disorder, satisfactory seizure control still cannot be achieved. Therefore, the aim of the present study was to identify this disorder among people who were seeking medical advice at Cairo University Epilepsy Unit (CUEU), characterizing its clinical, electroencephalographic and imaging features besides identifying possible indicators of inadequate seizure control on drug-therapy. Patients and methods: This study was carried out on 26 patients with SHE who were subjected to detailed history taking and examination in addition to home video recording, video electroencephalographic (EEG) monitoring and brain imaging. Ictal semiology and EEGs were reviewed and analyzed by experienced neurologists. Results: SHE is an uncommon sleep-related focal epilepsy. In our series, median age of the patients was 18.5 years. It is characterized by being sporadic, with often frontal lobe seizure onset (14/26, 53.8%) and with occasional occurrence in wakefulness. Adolescence age at disease onset (11 years, 6-15), duration less than 1 min, clustering, lack of auras and often uninformative brain imaging (22/25, 88%) are all documented features. Moreover, it has a relatively poor outcome on pharmacological therapy (16/26, 61.5%). Longer disease duration (>4.5 years) was a significant feature of the patients exhibiting inadequate seizure control. Conclusions: Our data show relatively poor prognosis of SHE on medical therapy. Its outcome is significantly related to disease duration at the time of diagnosis. Abbreviation: SHE = Sleep related hypermotor epilepsy.

Anatomo-electro-clinical correlations of hypermotor seizures with amygdala enlargement: Hippocampal seizure origin identified using stereoelectroencephalography

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A drug-resistant epilepsy case showed hypermotor seizures and amygdala enlargement.

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Seizure onset zone was the hippocampus, not amygdala, as revealed by SEEG.

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The enlarged amygdala pathology was classified as FCD type I.

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Selective amygdalohippocampectomy led to good outcomes.

Ictal coughing: Clinical features and differential diagnoses

PURPOSE

To describe a series of patients with ictal coughing to estimate its occurrence and characterize the clinical features and differential diagnoses.

METHODS

We retrospectively reviewed all the long-term video-EEG reports from Jefferson Comprehensive Epilepsy Center over a 7-year period (2010-2016) for the occurrence of the term "cough" in the text body. All the extracted reports were reviewed and patients with at least one documented ictal coughing at the epilepsy monitoring unit (EMU) were included in the study.

RESULTS

During the study period, 2487 patients were investigated at our EMU. Seven patients (0.28%) had at least one documented seizure accompanied by coughing. Four patients (0.16%) had epilepsy and three patients (0.12%) had psychogenic nonepileptic seizures (PNES). All patients with epilepsy had focal onset epilepsy; ictal coughing was not associated with any particular localization or lateralization.

CONCLUSION

We add to the literature on ictal coughing by providing additional information on its differential diagnosis and clinical features. Ictal coughing is a rare finding among patients evaluated at the EMUs. The differential diagnoses for ictal coughing include epilepsy and PNES. Epileptic ictal coughing is a rare semiological finding in patients with epilepsy and when present, it is invariably associated with focal epilepsy.

Temporal lobe origin is common in patients who have undergone epilepsy surgery for hypermotor seizures

RATIONALE

Hypermotor seizures are most often reported from the frontal lobe but may also have temporal, parietal, or insular origin. We noted a higher proportion of patients with temporal lobe epilepsy in our surgical cohort who had hypermotor seizures. We evaluated the anatomic localization and surgical outcome in patient with refractory hypermotor seizures who had epilepsy surgery in our center.

METHODS

We identified twenty three patients with refractory hypermotor seizures from our epilepsy surgery database. We analyzed demographics, presurgical evaluation including semiology, MRI, PET scan, interictal/ictal scalp video-EEG, intracranial recording, and surgical outcomes. We evaluated preoperative variables as predictors of outcome.

RESULTS

Most patients (65%) had normal brain MRI. Intracranial EEG was required in 20 patients (86.9%). Based on the presurgical evaluation, the resection was anterior temporal in fourteen patients, orbitofrontal in four patients, cingulate in four patients, and temporoparietal in one patient. The median duration of follow-up after surgery was 76.4months. Fourteen patients (60%) had been seizure free at the last follow up while 3 patients had rare disabling seizures.

CONCLUSIONS

Hypermotor seizures often originated from the temporal lobe in this series of patients who had epilepsy surgery. This large proportion of temporal lobe epilepsy may be the result of a selection bias, due to easier localization and expected better outcome in temporal lobe epilepsy. With extensive presurgical evaluation, including intracranial EEG when needed, seizure freedom can be expected in the majority of patients.