Ictal Central Apnea Is Predictive of Mesial Temporal Seizure Onset: An Intracranial Investigation.

OBJECTIVE: Ictal central apnea (ICA) is a semiological sign of focal epilepsy, associated with temporal and frontal lobe seizures. In this study, using qualitative and quantitative approaches, we aimed to assess the localizational value of ICA. We also aimed to compare ICA clinical utility in relation to other seizure semiological features of focal epilepsy. METHODS: We analyzed seizures in patients with medically refractory focal epilepsy undergoing intracranial stereotactic electroencephalographic (SEEG) evaluations with simultaneous multimodal cardiorespiratory monitoring. A total of 179 seizures in 72 patients with reliable artifact-free respiratory signal were analyzed. RESULTS: ICA was seen in 55 of 179 (30.7%) seizures. Presence of ICA predicted a mesial temporal seizure onset compared to those without ICA (odds ratio = 3.8, 95% confidence interval = 1.3-11.6, p = 0.01). ICA specificity was 0.82. ICA onset was correlated with increased high-frequency broadband gamma (60-150Hz) activity in specific mesial or basal temporal regions, including amygdala, hippocampus, and fusiform and lingual gyri. Based on our results, ICA has an almost 4-fold greater association with mesial temporal seizure onset zones compared to those without ICA and is highly specific for mesial temporal seizure onset zones. As evidence of symptomatogenic areas, onset-synchronous increase in high gamma activity in mesial or basal temporal structures was seen in early onset ICA, likely representing anatomical substrates for ICA generation. INTERPRETATION: ICA recognition may help anatomoelectroclinical localization of clinical seizure onset to specific mesial and basal temporal brain regions, and the inclusion of these regions in SEEG evaluations may help accurately pinpoint seizure onset zones for resection. ANN NEUROL 2024;95:998-1008.

EEG microstates show different features in focal epilepsy and psychogenic nonepileptic seizures.

OBJECTIVE: Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's electric field into semistable topographical EEG activity maps at different time points. Our study aimed to investigate the features of EEG microstates in subjects with focal epilepsy and psychogenic nonepileptic seizures (PNES). METHODS: We included 62 adult subjects with focal epilepsy or PNES who received video-EEG monitoring at the epilepsy monitoring unit. The subjects (mean age = 42.8 ± 21.2 years) were distributed equally between epilepsy and PNES groups. We extracted microstates from a 4.4 ± 1.0-min, 21-channel resting-state EEG. We excluded subjects with interictal epileptiform discharges during resting-state EEGs. After preprocessing, we derived five main EEG microstates-MS1 to MS5-for the full frequency band (1-30 Hz) and frequency subbands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz), using the MATLAB-based EEGLAB toolkit. Statistical features of microstates (duration, occurrence, contribution, global field power [GFP]) were compared between the groups, using logistic regression corrected for age and sex. RESULTS: We detected no differences in microstate parameters in the full frequency band. We found a longer duration (delta: B = -7.680, p = .046; theta: B = -16.200, p = .043) and a higher contribution (delta: B = -7.414, p = .035; theta: B = -7.509, p = .031) of MS4 in lower frequency bands in the epilepsy group. The PNES group showed a higher occurrence of MS5 in the delta subband (B = 3.283, p = .032). In the theta subband, a higher GFP of MS1 was associated with the PNES group (B = 5.674, p = .025), whereas a higher GFP of MS2 was associated with the epilepsy group (B = -6.579, p = .026). SIGNIFICANCE: Microstate features show differences between patients with focal epilepsy and PNES. EEG microstates could be a promising parameter, helping to understand changes in brain dynamics in subjects with epilepsy, and should be explored as a potential biomarker.

Specific consistency score for rational selection of epilepsy resection surgery candidates.

OBJECTIVE: Degree of indication for epilepsy surgery is determined by taking multiple factors into account. This study aimed to investigate the usefulness of the Specific Consistency Score (SCS), a proposed score for focal epilepsy to rate the indication for epilepsy focal resection. METHODS: This retrospective cohort study included patients considered for resective epilepsy surgery in Kyoto University Hospital from 2011 to 2022. Plausible epileptic focus was tentatively defined. Cardinal findings were scored based on specificity and consistency with the estimated laterality and lobe. The total points represented SCS. The association between SCS and the following clinical parameters was assessed by univariate and multivariate analysis: (1) probability of undergoing resective epilepsy surgery, (2) good postoperative seizure outcome (Engel I and II or Engel I only), and (3) lobar concordance between the noninvasively estimated focus and intracranial electroencephalographic (EEG) recordings. RESULTS: A total of 131 patients were evaluated. Univariate analysis revealed higher SCS in the (1) epilepsy surgery group (8.4 [95% confidence interval (CI) = 7.8-8.9] vs. 4.9 [95% CI = 4.3-5.5] points; p < .001), (2) good postoperative seizure outcome group (Engel I and II; 8.7 [95% CI = 8.2-9.3] vs. 6.4 [95% CI = 4.5-8.3] points; p = .008), and (3) patients whose focus defined by intracranial EEG matched the noninvasively estimated focus (8.3 [95% CI = 7.3-9.2] vs. 5.4 [95% CI = 3.5-7.3] points; p = .004). Multivariate analysis revealed areas under the curve of .843, .825, and .881 for Parameters 1, 2, and 3, respectively. SIGNIFICANCE: SCS provides a reliable index of good indication for resective epilepsy surgery and can be easily available in many institutions not necessarily specializing in epilepsy.

Ictal fast activity chirps as markers of the epileptogenic zone.

The identification of the epileptogenic zone (EZ) boundaries is crucial for effective focal epilepsy surgery. We verify the value of a neurophysiological biomarker of focal ictogenesis, characterized by a low-voltage fast-activity ictal pattern (chirp) recorded with intracerebral electrodes during invasive presurgical monitoring (stereoelectroencephalography [SEEG]). The frequency content of SEEG signals was retrospectively analyzed with semiautomatic software in 176 consecutive patients with focal epilepsies that either were cryptogenic or presented with discordant anatomoelectroclinical findings. Fast activity seizure patterns with the spectrographic features of chirps were confirmed by computer-assisted analysis in 95.4% of patients who presented with heterogeneous etiologies and diverse lobar location of the EZ. Statistical analysis demonstrated (1) correlation between seizure outcome and concordance of sublobar regions included in the EZ defined by visual analysis and chirp-generating regions, (2) high concordance in contact-by contact analysis of 68 patients with Engel class Ia outcome, and (3) that discordance between chirp location and the visually outlined EZ correlated with worse seizure outcome. Seizure outcome analysis confirms the fast activity chirp pattern is a reproducible biomarker of the EZ in a heterogeneous group of patients undergoing SEEG.

New stimulation procedures for language mapping in stereo-EEG.

OBJECTIVE: Cortical intracerebral electrical stimulation is an important tool for language mapping in the presurgical work-up of patients with drug-resistant focal epilepsy. Language mapping with stereo-electroencephalography (EEG) is usually performed by high-frequency stimulations (HFS: 50 Hz), whereas low-frequency stimulations (LFS: 1 Hz) are usually considered useful for primary cortices mapping. Little is known in literature about "intermediate" frequencies (IFS: 6-15 Hz). Our objective is to explore the clinical usefulness of IFS in language mapping and identify factors, beyond the electrical parameters, that impact the mapping. METHODS: We studied 23 patients submitted to stereo-EEG for presurgical evaluation. Language mapping was performed in the anterior, posterior and/or basal language region of the dominant hemisphere for language. We included all contact positions within these regions stimulated by HFS (50 Hz, 5 s, 1-3 mA) and IFS (6-15 Hz, 15 s, 5 mA). We compared the capability of both stimulation methods to induce a language deficit without afterdischarges (ADs), and we analyzed factors related to clinical examination, region, and stimulation technique by multivariate analysis. RESULTS: A total of 211 stimulations (98 HFS, 113 IFS) in 70 cortical sites within the anterior (84 stimulations), posterior (137), and basal language region (60) were included. IFS induced more frequently language deficits not associated to AD compared to HFS (37.1% vs 25.7%, p = .0043), whereas HFS provoked more diffuse AD (34.7% vs 15.0%, p = .001). Investigating multiple language functions increased the probability of revealing a deficit (odds ratio [OR] 3.16, p = .0016), independently of the stimulation method. SIGNIFICANCE: IFS are valuable for language mapping, thereby improving the probability of inducing a clinical deficit not accompanied by an AD. The completeness of the clinical examination independently affects the sensitivity of the mapping. IFS are a new tool with potential usefulness for the cortical mapping of other associative cortical regions.

EEG in focal and generalized epilepsies: Pearls and perils.

Correctly diagnosing and classifying seizures and epilepsies is vital to ensure a tailored approach to patients with epilepsy. The ILAE seizure classification consists of two main groups: focal and generalized. Establishing if a seizure is focal or generalized is essential to classify the epilepsy type and the epilepsy syndrome, providing more personalized treatment and counseling about prognosis. EEG is one of the most essential tools for this classification process and further localization of the epileptogenic focus. However, some EEG findings are misleading and may postpone the correct diagnosis and proper treatment. Knowing the most common EEG pitfalls in focal and generalized epilepsies is valuable for clinical practice, avoiding misinterpretations. Some atypical features can be challenging in focal epilepsies, such as secondary bilateral synchrony, focal epileptiform activity induced by hyperventilation and photic stimulation, and non-focal slowing. On the other hand, more than 60 % of persons with idiopathic generalized epilepsies have at least one type of atypical abnormality. In this manuscript, we describe and illustrate some of the most common EEG findings that can make even experienced epileptologists question not only where the epileptogenic focus is but also if the patient has focal or generalized epilepsy. This review summarizes the perils and provide some pearls to assist EEG readers.

Learning difficulties often not documented in newly diagnosed focal epilepsy.

OBJECTIVE: A previous investigation of people with newly diagnosed focal epilepsy participating in the Human Epilepsy Project 1 (HEP1) revealed an association between learning difficulties and structural brain differences, suggesting an underlying relationship prior to seizure onset. To investigate physicians' practices of documentation learning difficulties during clinical encounters, we conducted a review of initial epileptologist encounter notes from HEP1 participants who self-reported early life learning difficulties separately as part of study enrollment. METHODS: HEP1 enrolled 67 North American participants between June 2012 and November 2017 who self-reported one or more difficulties with learning (i.e., having repeated grade, receiving learning support/remediation, and/or formal diagnosis of a learning disability) prior to epilepsy diagnosis as part of the study enrollment. The epileptologist's initial encounter note was then reviewed in detail for each of these participants. Documentation of learning issues and specific diagnoses of learning disabilities was compared to participant characteristics. Regression analysis was used to test for any independent associations between participant characteristics and physician documentation of learning difficulties. RESULTS: There were significant independent relationships between age, sex, and physician documentation of learning difficulties. On average, participants ages 22 and younger were 12.12 times more likely to have their learning difficulties documented compared to those 23 years and older (95 % CI: 2.226 to 66.02, p = 0.004). Additionally, male participants had 7.2 times greater odds of having their learning difficulty documented compared to female participants (95 % CI: 1.538 to 33.717, p = 0.012). There were no significant independent associations between race, language, employment, or geographical region. SIGNIFICANCE: These findings highlight disparities in physician documentation for people with newly diagnosed focal epilepsy and a history of learning difficulties. In the HEP1 cohort, physicians were more likely to document learning difficulties in males and in younger individuals. Systematic practice standards are important for reducing healthcare disparities across populations, improving clinical care to individuals, as well as enabling more accurate retrospective study of clinical phenomenon.

How do children with focal epilepsies perceive the moment they realize that they are about to have a seizure? A pilot study.

PROBLEM: A seizure is a challenging situation for children with epilepsy. Little is known regarding the experience of children who perceive in advance that they are about to have a seizure. METHODS: From September 2020 to February 2021, we invited children with focal epilepsies aged 6-18 years to participate in a semi-structured interview. RESULTS: Of 52 children with focal epilepsies, 22 (42 %) said they perceive in advance that they are about to experience a seizure [11 with self-limited epilepsy with centro-temporal spikes (SELECTs), 11 with other focal epilepsies]. All 22/22 (100 %) children described physical symptoms such as headache or a numb feeling in one half of the body. Of those children, 17/22 (77 %) stated they try to do something about the seizure. Those strategies were perceived as helpful by 0/11 (0 %) children with SELECTs and 9/11 (86 %) children with other focal epilepsies (p < 0.001). Of the children with SELECTs 5/11 (45 %), and of those with other focal epilepsies 9/11 (86 %) stated they would like to know in the morning if they are to experience a seizure that day (n.s.). CONCLUSION: Children who perceive in advance that they are about to have a seizure are well able to describe their experience. Most children take measures to manage their seizures. Those measures were regarded as helpful by most children with other focal epilepsies, but by no child with SELECTs. Larger studies are necessary to determine the factors contributing to the child's perception as well as the nature of the support that they require.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

Engagement in online cognitive testing with the Cogstate brief battery among a multinational cohort of people with focal epilepsy.

OBJECTIVE: The Human Epilepsy Project (HEP) is a large multinational cohort study of people with newly diagnosed and treated focal epilepsy. HEP utilized the Cogstate Brief Battery (CBB) as a self-directed online assessment to examine cognitive outcomes in study participants. The CBB has previously been validated in healthy individuals and people with various brain disorders, but its use in adults participating in HEP has not been assessed. In this study, we describe how the CBB was used in the HEP cohort and assess factors associated with test completion among study participants. METHODS: Enrollment data for HEP included 408 participants with comprehensive enrollment records, of whom 249 completed CBB assessments. HEP enrolled cognitively normal-range participants between the ages of 12 and 60 from June 29, 2012, to November 7, 2017, with newly diagnosed focal epilepsy and within 4 months of initial treatment. Baseline participant characteristics were analyzed, including demographics, pre-treatment seizure histories, MRI abnormalities, and the presence of any learning difficulties while in school, including formal learning disability diagnoses, repeated grades, and remediation. HEP participant characteristics for those who completed CBB testing were compared to those who did not using multiple logistic regression. RESULTS: The analysis of HEP participants who completed CBB testing showed that, after controlling for other factors, male participants were more likely to engage in testing (OR 2.14, 95 % CI 1.29 to 3.5, p < 0.01), Black subjects were less likely (OR 0.45, 95 % CI 0.22 to 0.9, p = 0.02), primary English speakers were more likely (OR 3.1, 95 % CI 1.21 to 7.96, p = 0.02), and those with a history of learning challenges were less likely (OR 0.69, 95 % CI 0.49 to 0.97, p = 0.03). There were no significant associations between completing CBB testing and age, employment (employed or student vs not), education (higher education vs not), diagnostic delay, pre-diagnostic seizure burden, or initial seizure semiology (motor vs non-motor). SIGNIFICANCE: The findings from this study highlight factors associated with the application of remote and unsupervised assessments of cognition in a prospective cohort of adults with focal epilepsy. These factors can be considered when interpreting performance on the CBB in HEP, as well as assisting the design of future studies that use similar approaches.

Comprehensive review of status gelasticus: Diagnostic challenges and therapeutic insights.

Status gelasticus is a rare form of status epilepticus characterized by prolonged and/or clustered gelastic seizures. The review encompasses an analysis of cases reported in the literature, focusing on causes, clinical-electroencephalographic features, and therapeutic interventions. The study reveals the challenges in defining and understanding status gelasticus due to its diverse etiologies and limited reported cases. The association with hypothalamic hamartomas and other brain abnormalities underscores the importance of thorough evaluations. The review also discusses new treatments, including medications and less invasive surgeries. While progress has been made, the study points out challenges in diagnosing and managing this complex condition, highlighting the importance of ongoing research.

Detection of somatic and germline pathogenic variants in adult cohort of drug-resistant focal epilepsies.

OBJECTIVE: This study investigates the prevalence of pathogenic variants in the mechanistic target of rapamycin (mTOR) pathway in surgical specimens of malformations of cortical development (MCDs) and cases with negative histology. The study also aims to evaluate the predictive value of genotype-histotype findings on the surgical outcome. METHODS: The study included patients with drug-resistant focal epilepsy who underwent epilepsy surgery. Cases were selected based on histopathological diagnosis, focusing on MCDs and negative findings. We included brain tissues both as formalin-fixed, paraffin-embedded (FFPE) or fresh frozen (FF) samples. Single-molecule molecular inversion probes (smMIPs) analysis was conducted, targeting the MTOR gene in FFPE samples and 10 genes within the mTOR pathway in FF samples. Correlations between genotype-histotype and surgical outcome were examined. RESULTS: We included 78 patients for whom we obtained 28 FFPE samples and 50 FF tissues. Seventeen pathogenic variants (22 %) were identified and validated, with 13 being somatic within the MTOR gene and 4 germlines (2 DEPDC5, 1 TSC1, 1 TSC2). Pathogenic variants in mTOR pathway genes were exclusively found in FCDII and TSC cases, with a significant association between FCD type IIb and MTOR genotype (P = 0.003). Patients carrying mutations had a slightly better surgical outcome than the overall cohort, however it results not significant. The FCDII diagnosed cases more frequently had normal neuropsychological test, a higher incidence of auras, fewer multiple seizure types, lower occurrence of seizures with awareness impairment, less ictal automatisms, fewer Stereo-EEG investigations, and a longer period long-life of seizure freedom before surgery. SIGNIFICANCE: This study confirms that somatic MTOR variants represent the primary genetic alteration detected in brain specimens from FCDII/TSC cases, while germline DEPDC5, TSC1/TSC2 variants are relatively rare. Systematic screening for these mutations in surgically treated patients' brain specimens can aid histopathological diagnoses and serve as a biomarker for positive surgical outcomes. Certain clinical features associated with pathogenic variants in mTOR pathway genes may suggest a genetic etiology in FCDII patients.

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

From cryogenic to on-scalp magnetoencephalography for the evaluation of paediatric epilepsy.

Magnetoencephalography (MEG) is a neurophysiological technique based on the detection of brain magnetic fields. Whole-head MEG systems typically house a few hundred sensors requiring cryogenic cooling in a rigid one-size-fits-all (commonly adult-sized) helmet to keep a thermal insulation space. This leads to an increased brain-to-sensor distance in children, because of their smaller head circumference, and decreased signal-to-noise ratio. MEG allows detection and localization of interictal and ictal epileptiform discharges, and pathological high frequency oscillations, as a part of the presurgical assessment of children with refractory focal epilepsy, where electroencephalography is not contributive. MEG can also map the eloquent cortex before surgical resection. MEG also provides insights into the physiopathology of both generalized and focal epilepsy. On-scalp recordings based on cryogenic-free sensors have demonstrated their use in the field of childhood focal epilepsy and should become a reference technique for diagnosing epilepsy in the paediatric population. WHAT THIS PAPER ADDS: Magnetoencephalography (MEG) contributes to the diagnosis and understanding of paediatric epilepsy. On-scalp MEG recordings demonstrate some advantages over cryogenic MEG.

The value of ictal scalp EEG in focal epilepsies surgery: a retrospective analysis.

OBJECTIVE: To describe the association between preoperative ictal scalp electroencephalogram (EEG) results and surgical outcomes in patients with focal epilepsies. METHODS: The data of consecutive patients with focal epilepsies who received surgical treatments at our center from January 2012 to December 2021 were retrospectively analyzed. RESULTS: Our data showed that 44.2% (322/729) of patients had ictal EEG recorded on video EEG monitoring during preoperative evaluation, of which 60.6% (195/322) had a concordant ictal EEG results. No significant difference of surgery outcomes between patients with and without ictal EEG was discovered. Among MRI-negative patients, those with concordant ictal EEG had a significantly better outcome than those without ictal EEG (75.7% vs. 43.8%, p = 0.024). Further logistic regression analysis showed that concordant ictal EEG was an independent predictor for a favorable outcome (OR = 4.430, 95%CI 1.175-16.694, p = 0.028). Among MRI-positive patients, those with extra-temporal lesions and discordant ictal EEG results had a worse outcome compared to those without an ictal EEG result (44.7% vs. 68.8%, p = 0.005). Further logistic regression analysis showed that discordant ictal EEG was an independent predictor of worse outcome (OR = 0.387, 95%CI 0.186-0.807, p = 0.011) in these patients. Furthermore, our data indicated that the number of seizures was not associated with the concordance rates of the ictal EEG, nor the surgical outcomes. CONCLUSIONS: The value of ictal scalp EEG for epilepsy surgery varies widely among patients. A concordant ictal EEG predicts a good surgical outcome in MRI-negative patients, whereas a discordant ictal EEG predicts a poor postoperative outcome in lesional extratemporal lobe epilepsy.

Epilepsia partialis continua: A review.

Epilepsia partialis continua (EPC) is a rare type of focal motor seizure characterized by continuous, involuntary muscle contractions in a specific part of the body. These contractions usually involve rhythmic, twitching movements and can last for several hours to days. The seizures are usually limited to one part of the body and can be clonic or dystonic. EPC can affect people of all ages but is more common in children and adolescents. The pathophysiology of EPC is complex and depends on the cause. There are several possible causes of EPC including structural brain abnormalities, infections, metabolic and genetic disorders, inflammatory conditions, traumatic brain injury, and vascular causes. The work-up of EPC includes electroencephalography (EEG), magnetic resonance imaging (MRI) of the brain, position emission tomography (PET) scan of the brain, autoimmune antibodies, infection work-up, and metabolic and genetic work-up. The management of EPC can be challenging. Antiseizure medications (ASDs) including benzodiazepines are an integral part of the management of EPC. Immunotherapy trials are recommended in resistant cases. Epilepsy surgery is one of the effective modalities in some surgically amenable cases. This article reviews the topic of EPC and summarizes diagnostic and .treatment recommendations.

Advances in Image Processing for Epileptogenic Zone Detection with MRI.

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

Wrist-worn smartwatch and predictive models for seizures.

OBJECTIVE: This study was undertaken to describe extracerebral biosignal characteristics of overall and various seizure types as compared with baseline physical activities using multimodal devices (Empatica E4); develop predictive models for overall and each seizure type; and assess diagnostic performance of each model. METHODS: We prospectively recruited patients with focal epilepsy who were admitted to the epilepsy monitoring unit for presurgical evaluation during January to December 2020. All study participants were simultaneously applied gold standard long-term video-electroencephalographic (EEG) monitoring and an index test, E4. Two certified epileptologists independently determined whether captured events were seizures and then indicated ictal semiology and EEG information. Both were blind to multimodal biosignal findings detected by E4. Biosignals during 5-min epochs of both seizure events and baseline were collected and compared. Predictive models for occurrence overall and of each seizure type were developed using a generalized estimating equation. Diagnostic performance of each model was then assessed. RESULTS: Thirty patients had events recorded and were recruited for analysis. One hundred eight seizure events and 120 baseline epochs were collected. Heart rate (HR), acceleration (ACC), and electrodermal activity (EDA) but not temperature were significantly elevated during seizures. Cluster analysis showed trends of greatest elevation of HR and ACC in bilateral tonic-clonic seizures (BTCs), as compared with non-BTCs and isolated auras. HR and ACC were independent predictors for overall seizure types, BTCs, and non-BTCs, whereas only HR was a predictor for isolated aura. Diagnostic performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the predictive model for overall seizures were 77.78%, 60%, and .696 (95% confidence interval = .628-.764), respectively. SIGNIFICANCE: Multimodal extracerebral biosignals (HR, ACC, EDA) detected by a wrist-worn smartwatch can help differentiate between epileptic seizures and normal physical activities. It would be worthwhile to implement our predictive algorithms in commercial seizure detection devices. However, larger studies to externally validate our predictive models are required.

In-hospital and home-based long-term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience.

OBJECTIVE: The aim is to report the performance of an electroencephalogram (EEG) seizure-detector algorithm on data obtained with a wearable device (WD) in patients with focal refractory epilepsy and their experience. METHODS: Patients used a WD, the Sensor Dot (SD), to measure two channels of EEG using dry electrode patches during presurgical evaluation and at home for up to 8 months. An automated seizure detection algorithm flagged EEG regions with possible seizures, which we reviewed to evaluate the algorithm's diagnostic yield. In addition, we collected data on usability, side effects, and patient satisfaction with an electronic seizure diary application (Helpilepsy). RESULTS: Sixteen inpatients used the SD for up to 5 days and had 21 seizures. Sixteen outpatients used the device for up to 8 months and reported 101 focal impaired awareness seizures during the periods selected for analysis. Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients. False detections/h, positive predictive value (PPV), and F1 scores were 7.13%, .11%, and .002% for inpatients and 7.77%, .04%, and .001% for outpatients. Artifacts and low signal quality contributed to poor performance metrics. The seizure detector identified 19 nonreported seizures during sleep, when the signal quality was better. Regarding patients' experience, the likelihood of using the device at 6 months was 62%, and side effects were the main reason for dropping out. Finally, daily and monthly questionnaire completion rates were 33% and 65%, respectively. SIGNIFICANCE: Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients, with high false alarm rates and low PPV and F1 scores. This unobtrusive wearable seizure detection device was well received but had side effects. The current workflow and low performance limit its implementation in clinical practice. We suggest different steps to improve these performance metrics and patient experience.

A region-specific modulation of sleep slow waves on interictal epilepsy markers in focal epilepsy.

OBJECTIVE: Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy. METHODS: Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording. Scalp SSWs and intracranial spikes and ripples (80-250 Hz) were detected; ripples were classified as type I (co-occurring with spikes) or type II (occurring alone). The Hilbert transform was used to analyze the distributions of spikes and ripples in the phases of SSWs. RESULTS: Thirty patients with discrete seizure-onset zone (SOZ) and discernable sleep architecture were included. Intracranial spikes and ripples accumulated around the negative peaks of SSWs and increased with SSW amplitude. Phase analysis revealed that spikes and both ripple subtypes in SOZ were similarly facilitated by SSWs exclusively during down state. In exclusively irritative zones outside SOZ (EIZ), SSWs facilitated spikes and type I ripples across a wider range of phases and to a greater extent than those in SOZ. The type II and type I ripples in EIZ were modulated by SSWs in different patterns. Ripples in normal zones decreased specifically during the up-to-down transition and then increased after the negative peak of SSW, with a characteristically high post-/pre-negative peak ratio. SIGNIFICANCE: SSWs modulate interictal events in an amplitude-dependent and region-specific pattern. Pathological ripples and spikes were facilitated predominantly during the cortical down state. Coupling analysis of SSWs could improve the discrimination of pathological and physiological ripples and facilitate seizure localization.