Seizure Forecasting and the Preictal State in Canine Epilepsy

Varatharajah, Yogatheesan; Iyer, Ravishankar K.; Berry, Brent; Worrell, Gregory A.; Brinkmann, Benjamin H.

doi:10.1142/s0129065716500465

Cited by 46 publications

(45 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, our approach presents a general paradigm for EEG-based anomaly detection, which can be beneficial for other EEG applications such as seizure forecasting. 45 In addition, our approach to characterize the health of brain function using the alpha rhythm can form the basis for the growing area of research on EEG and brain health 46 and can be used to study a variety of other neurological conditions.…”

Section: Discussionmentioning

confidence: 99%

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Varatharajah

Berry

Joseph

et al. 2021

Brain Communications

Self Cite

View full text Add to dashboard Cite

Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated a) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients and b) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analyzed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy (DRFE) who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8-13 Hz) relative to a wide band of 0.5-40 Hz within 10-second windows. We analyzed the extracted features for a) the extent to which people with DRFE differed from healthy subjects and b) whether differences within the DRFE patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with DRFE, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders such as acquisition system, age, and medications. We found that the fraction of alpha power is generally reduced a) in DRFE compared to healthy controls, and b) in right-handed DRFE subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing DRFE from healthy and 0.77 in identifying the epileptic hemisphere in DRFE patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age, and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures, or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

show abstract

Section: Discussionmentioning

confidence: 99%

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Varatharajah

Berry

Joseph

et al. 2021

Brain Communications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most importantly, the temporal association of psychiatric symptoms to seizures should be determined so that peri-ictal and inter-ictal symptoms may be identified accurately. The iEEG time-series must be reviewed for electrographic seizures, and each seizure should denote a two-hour pre-ictal and post-ictal period to be excluded from data analysis (Varatharajah et al, 2017 ). Clinical seizure prediction algorithms indicate the pre-ictal iEEG changes last for a period averaging around 2 h, although subtle changes in excitability have been detected as far out as 24 h preceding and following seizures (Badawy et al, 2009 ; Cook et al, 2013 ).…”

Section: Rigorous Practices: Confounding Factors and Recommendations For Experimental Designsmentioning

confidence: 99%

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Balzekas

Sladky

Nejedlý

et al. 2021

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

show abstract

“…Consequently, many studies apply different length of seizure-free period, even when using the same data set. For instance, for American Epilepsy Society Seizure Prediction Challenge dataset, several research teams apply different definitions of lead seizures, as summarized next:  4-hour seizure-free period in Brinkmann et al [25], Howbert et al [26], Nejedly et al [32], Varatharajah et al [29].  80-minute seizure-free period in Assi et al [24], even though it was not explicitly stated in their paper.…”

Section: Lead Seizuresmentioning

confidence: 99%

“…Proposed system is evaluated using canine iEEG data (~American Epilepsy Society Seizure Prediction Challenge dataset) previously used in several seizure prediction studies [15,16,19,[24][25][26][27][28][29]. Canine data from epileptic dogs is used as a translational model for human seizure prediction, due to:  Similarity of biological mechanisms of epilepsy in humans and dogs.…”

Section: Description Of Available Ieeg Data and Analysis Of Seizure Clusteringmentioning

confidence: 99%

See 1 more Smart Citation

Online Prediction of Lead Seizures from iEEG Data

Chen¹,

Shiao²,

Cherkassky³

2021

Preprint

View full text Add to dashboard Cite

We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, machine learning part of the system is implemented using the Group Learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with non-stationarity of noisy iEEG signal. They include: (1) periodic re-training of SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; (3) introducing new adaptive post-processing technique for combining many predictions made for 20-second windows into a single prediction for 4 hr segment. Application of the proposed system requires only 2 lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). Proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during 169–364 days test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).

show abstract

Seizure Forecasting and the Preictal State in Canine Epilepsy

Cited by 46 publications

References 36 publications

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Online Prediction of Lead Seizures from iEEG Data

Contact Info

Product

Resources

About