A multi-center comparison of surgical techniques for corpus Callosotomy in pediatric drug-resistant epilepsy.

OBJECTIVES: Corpus callosotomy (CC) is used to reduce seizures, primarily in patients with generalized drug-resistant epilepsy (DRE). The invasive nature of the procedure contributes to underutilization despite its potential superiority to other palliative procedures. The goal of this study was to use a multi-institutional epilepsy surgery database to characterize the use of CC across participating centers. METHODS: Data were acquired from the Pediatric Epilepsy Research Consortium (PERC) Surgery Database, a prospective observational study collecting data on children 0-18 years referred for surgical evaluation of DRE across 22 U.S. pediatric epilepsy centers. Patient, epilepsy, and surgical characteristics were collected across multiple CC modalities. Outcomes and complications were recorded and analyzed statistically. RESULTS: Eighty-three patients undergoing 85 CC procedures at 14 participating epilepsy centers met inclusion criteria. Mean age at seizure onset was 2.3 years (0-9.4); mean age for Phase I evaluation and surgical intervention were 9.45 (.1-20) and 10.46 (.2-20.6) years, respectively. Generalized seizure types were the most common (59%). Complete CC was performed in 88%. The majority of CC procedures (57%) were via open craniotomy, followed by laser interstitial thermal therapy (LiTT) (20%) and mini-craniotomy/endoscopic (mc/e) (22%). Mean operative times were significantly longer for LiTT, whereas mean estimated blood loss was greater in open cases. Complications occurred in 11 cases (13%) and differed significantly between surgical techniques (p < .001). There was no statistically significant difference in length of postoperative stay across approaches. Mean follow-up was 12.8 months (range 1-39). Favorable Engel outcomes were experienced by 37 (78.7%) of the patients who underwent craniotomy, 10 (58.8%) with LiTT, and 12 (63.2%) with mc/e; these differences were not statistically significant. SIGNIFICANCE: CC is an effective surgical modality for children with DRE. Regardless of surgical modality, complication rates are acceptable and seizure outcomes generally favorable. Newer, less-invasive, surgical approaches may lead to increased adoption of this efficacious therapeutic option for pediatric DRE.

Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.

OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected "nothing," suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.

A novel method for dynamically altering the surface area of intracranial EEG electrodes.

Objective.Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain.Approach.We first present a theoretical model and anin vitrovalidation of the method. We then report the results of anin vivoimplementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes.Main Results.We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike.Significance.Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.

Epilepsy surgery in children with genetic etiologies: A prospective evaluation of current practices and outcomes.

OBJECTIVE: This study assesses current practices and outcomes of epilepsy surgery in children with a genetic etiology. It explores the pre-surgical workup, types of surgeries, and post-surgical outcomes in a broad array of disorders. METHODS: Patients ≤18 years who completed epilepsy surgery and had a known genetic etiology prior to surgical intervention were extrapolated from the Pediatric Epilepsy Research Consortium (PERC) surgery database, across 18 US centers. Data were assessed univariably by neuroimaging and EEG results, genetic group (structural gene, other gene, chromosomal), and curative intent. Outcomes were based on a modified International League Against Epilepsy (ILAE) outcome score. RESULTS: Of 81 children with genetic epilepsy, 72 % had daily seizures when referred for surgery evaluation, which occurred a median of 2.2 years (IQR 0.3, 5.2) after developing drug resistance. Following surgery, 68 % of subjects had >50 % seizure reduction, with 33 % achieving seizure freedom [median follow-up 11 months (IQR 6, 17). Seizure freedom was most common in the monogenic structural group, but significant palliation was present across all groups. Presence of a single EEG focus was associated with a greater likelihood of seizure freedom (p=0.02). SIGNIFICANCE: There are meaningful seizure reductions following epilepsy surgery in the majority of children with a genetic etiology, even in the absence of a single structural lesion and across a broad spectrum of genetic causes. These findings highlight the need for expedited referral for epilepsy surgery and support of a broadened view of which children may benefit from epilepsy surgery, even when the intent is palliative.

EEG biomarkers for the diagnosis and treatment of infantile spasms.

Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.

Evolution of Cortical Functional Networks in Healthy Infants.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Surgical evaluation in children <3 years of age with drug-resistant epilepsy: Patient characteristics, diagnostic utilization, and potential for treatment delays.

OBJECTIVE: Drug-resistant epilepsy (DRE) occurs at higher rates in children <3 years old. Epilepsy surgery is effective, but rarely utilized in young children despite developmental benefits of early seizure freedom. The present study aims to identify unique patient characteristics and evaluation strategies in children <3 years old who undergo epilepsy surgery evaluation as a means to assess contributors and potential solutions to health care disparities in this group. METHODS: The Pediatric Epilepsy Research Consortium Epilepsy Surgery Database, a multicentered, cross-sectional collaboration of 21 US pediatric epilepsy centers, collects prospective data on children <18 years of age referred for epilepsy surgery evaluation. We compared patient characteristics, diagnostic utilization, and surgical treatment between children <3 years old and those older undergoing initial presurgical evaluation. We evaluated patient characteristics leading to delayed referral (>1 year) after DRE diagnosis in the very young. RESULTS: The cohort included 437 children, of whom 71 (16%) were <3 years of age at referral. Children evaluated before the age of 3 years more commonly had abnormal neurological examinations (p = .002) and daily seizures (p = .001). At least one ancillary test was used in 44% of evaluations. Fifty-nine percent were seizure-free following surgery (n = 34), with 35% undergoing limited focal resections. Children with delayed referrals more often had focal aware (p < .001) seizures and recommendation for palliative surgeries (p < .001). SIGNIFICANCE: There are relatively few studies of epilepsy surgery in the very young. Surgery is effective, but may be disproportionally offered to those with severe presentations. Relatively low utilization of ancillary testing may contribute to reduced surgical therapy for those without evident lesions on magnetic resonance imaging. Despite this, a sizeable portion of patients have favorable outcome after focal epilepsy surgery resections.

Scalp EEG markers of normal infant development using visual and computational approaches.

The infant brain is rapidly developing, and these changes are reflected in scalp electroencephalography (EEG) features, including power spectrum and sleep spindle characteristics. These biomarkers not only mirror infant development, but they are also altered by conditions such as epilepsy, autism, developmental delay, and trisomy 21. Prior studies of early development were generally limited by small cohort sizes, lack of a specific focus on infancy (0-2 years), and exclusive use of visual marking for sleep spindles. Therefore, we measured the EEG power spectrum and sleep spindles in 240 infants ranging from 0-24 months. To rigorously assess these metrics, we used both clinical visual assessment and computational techniques, including automated sleep spindle detection. We found that the peak frequency and power of the posterior dominant rhythm (PDR) increased with age, and a corresponding peak occurred in the EEG power spectra. Based on both clinical and computational measures, spindle duration decreased with age, and spindle synchrony increased with age. Our novel metric of spindle asymmetry suggested that peak spindle asymmetry occurs at 6-9 months of age.Clinical Relevance- Here we provide a robust characterization of the development of EEG brain rhythms during infancy. This can be used as a basis of comparison for studies of infant neurological disease, including epilepsy, autism, developmental delay, and trisomy 21.

Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness.

OBJECTIVE: Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. METHODS: Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). RESULTS: The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). CONCLUSIONS: We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.

Underrepresented Populations in Pediatric Epilepsy Surgery.

As awareness of pediatric epilepsy increases, accompanied by advancements in technology and research, it is important to identify certain types of patients that are overlooked for surgical management of epilepsy. Identifying these populations will allow us to study and elucidate the factors contributing to the underutilization and/or delayed application of surgical interventions. Demographically, African-American and Hispanic patients, as well as patients of certain Asian ethnicities, have relatively lower rates of undergoing epilepsy surgery than non-Hispanic and white patients. Among patients with epilepsy, those with higher odds of seizure-freedom following surgery are more likely to be referred for surgical evaluation by their neurologists, with the most common diagnosis being lesional focal epilepsy. However, patients with multifocal or generalized epilepsy, genetic etiologies, or normal (non-lesional) brain magnetic resonance imaging (MRI) are less likely be to referred for evaluation for resective surgery. With an increasing number of high-quality imaging modalities to help localize the epileptogenic zone as well as new techniques for both curative and palliative epilepsy surgery, there are very few populations of patients and/or types of epilepsy that should be precluded from evaluation to determine the suitability of epilepsy surgery. Ultimately, a clearer understanding of the populations who are underrepresented among those considered for epilepsy surgery, coupled with further study of the underlying reasons for this trend, will lead to less disparity in access to this critical treatment among patients with epilepsy.

Computational characteristics of interictal EEG as objective markers of epileptic spasms.

OBJECTIVE: Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES. METHODS: We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. RESULTS: In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). CONCLUSIONS: Computational EEG features successfully distinguish ES patients from controls in a large, blinded study. SIGNIFICANCE: These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.

Infant functional networks are modulated by state of consciousness and circadian rhythm.

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms.

Objective.Scalp high-frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques.Approach.We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adrenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 h for controls and 84.5 h for IS subjects (∼1300 h total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm.Main results.HFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5 min-1and 2.9 min-1, respectively;p = 0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98 min-1and 0.82 min-1, respectively). Spatially, IS patients exhibited significantly higher rates of HFOs in the posterior parasaggital region and significantly lower HFO rates in frontal channels, and this difference was more pronounced during sleep. In IS subjects, ACTH therapy significantly decreased the rate of HFOs.Significance.Here we provide a detailed characterization of the spatial distribution and rates of HFOs associated with IS, which may have relevance for diagnosis and assessment of treatment response. We also demonstrate that our fully automated algorithm can be used to detect HFOs in long-term scalp EEG with sufficient accuracy to clearly discriminate healthy subjects from those with IS.

Management of Infantile Spasms During the COVID-19 Pandemic.

Circumstances of the COVID-19 pandemic have mandated a change to standard management of infantile spasms. On April 6, 2020, the Child Neurology Society issued an online statement of immediate recommendations to streamline diagnosis and treatment of infantile spasms with utilization of telemedicine, outpatient studies, and selection of first-line oral therapies as initial treatment. The rationale for the recommendations and specific guidance including follow-up assessment are provided in this manuscript. These recommendations are indicated as enduring if intended to outlast the pandemic, and limited if intended only for the pandemic health care crisis but may be applicable to future disruptions of health care delivery.

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks.

OBJECTIVE: Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. METHODS: We introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed. RESULTS: IEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure. CONCLUSIONS: Increases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS. SIGNIFICANCE: Dynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.

Felbamate in the treatment of refractory epileptic spasms.

Several small case series provide conflicting impressions of the efficacy of felbamate for treatment of epileptic spasms. Using a large single-center cohort of children with epileptic spasms, we retrospectively evaluated the efficacy and safety of felbamate. We identified all patients with video-EEG confirmed epileptic spasms who were treated with felbamate at our center. We quantified felbamate exposure by calculating peak and weighted-average weight-based dose. Clinical response was defined as resolution of epileptic spasms for at least 28 days, beginning not more than 3 months after felbamate initiation. Electroclinical response was defined as clinical response accompanied by overnight video-EEG demonstrating freedom from epileptic spasms and hypsarrhythmia. Among a cohort of 476 infants, we identified 62 children who were treated with felbamate, of whom 58 had previously failed treatment with hormonal therapy or vigabatrin. Median peak and weighted-average felbamate dosages were 47 and 40 mg/kg/day, respectively. Five (8%) children were classified as clinical responders and two (3%) children were classified as electroclinical responders. Among 17 patients with latency from epileptic spasms onset to felbamate initiation of less than 12 months, we observed 4 (24%) clinical responders. This study suggests that felbamate may be efficacious for treatment of epileptic spasms and that further rigorous study is warranted.

Inference on Long-Range Temporal Correlations in Human EEG Data.

Detrended Fluctuation Analysis (DFA) is a statistical estimation algorithm used to assess long-range temporal dependence in neural time series. The algorithm produces a single number, the DFA exponent, that reflects the strength of long-range temporal correlations in the data. No methods have been developed to generate confidence intervals for the DFA exponent for a single time series segment. Thus, we present a statistical measure of uncertainty for the DFA exponent in electroencephalographic (EEG) data via application of a moving-block bootstrap (MBB). We tested the effect of three data characteristics on the DFA exponent: (1) time series length, (2) the presence of artifacts, and (3) the presence of discontinuities. We found that signal lengths of ∼5 minutes produced stable measurements of the DFA exponent and that the presence of artifacts positively biased DFA exponent distributions. In comparison, the impact of discontinuities was small, even those associated with artifact removal. We show that it is possible to combine a moving block bootstrap with DFA to obtain an accurate estimate of the DFA exponent as well as its associated confidence intervals in both simulated data and human EEG data. We applied the proposed method to human EEG data to (1) calculate a time-varying estimate of long-range temporal dependence during a sleep-wake cycle of a healthy infant and (2) compare pre- and post-treatment EEG data within individual subjects with pediatric epilepsy. Our proposed method enables dynamic tracking of the DFA exponent across the entire recording period and permits within-subject comparisons, expanding the utility of the DFA algorithm by providing a measure of certainty and formal tests of statistical significance for the estimation of long-range temporal dependence in neural data.

Quantitative Characteristics of Hypsarrhythmia in Infantile Spasms.

Infantile spasms is a type of epilepsy characterized by clinical seizures termed "spasms" and often an electroencephalographic (EEG) pattern known as hypsarrhythmia. Multiple studies have shown that the interrater reliability for human visual recognition of hypsarrhythmia is poor. Quantitative measurements of this EEG pattern would provide objective basis for identification; however, the basic temporal and spectral characteristics of hypsarrhythmia have never been assessed. Thus, we measured EEG amplitude and power spectra in 21 infantile spasms patients before and after treatment, as well as 21 control subjects. The hypsarrhythmia EEG pattern was associated with (1) high broadband amplitude, especially in frontal and central brain regions, (2) high median power in the delta and alpha frequency bands, and (3) low spectral edge frequency. Our results indicate that hypsarrhythmia can be quantitatively distinguished from data without hypsarrhythmia. Introduction of these quantitative measures into clinical practice may increase diagnostic accuracy, expediting proper treatment and improving outcomes.