A device capable of detecting seizures and alerting caregivers would be a major advance for epilepsy management, and could be used to guide early intervention and prevent seizure-related injuries. The objective of this work was to evaluate a seizure advisory system (SAS) that alerts caregivers of seizures in canines with naturally occurring epilepsy. Four dogs with epilepsy were implanted with a SAS that wirelessly transmits continuous intracranial EEG (iEEG) to an external device embedded with a seizure detection algorithm and the capability to alert caregivers. In this study a veterinarian was alerted by automated text message if prolonged or repetitive seizures occurred, and a rescue therapy protocol was implemented. The performance of the SAS caregiver alert was evaluated over the course of 8 weeks. Following discontinuation of antiepileptic drugs, the dogs experienced spontaneous unprovoked partial seizures that secondarily generalized. Three prolonged or repetitive seizure episodes occurred in 2 of the dogs. On each occasion, the SAS caregiver alert successfully alerted an on call veterinarian who confirmed the seizure activity via remote video-monitoring. A rescue medication was then administered and the seizures were aborted. This study demonstrates the feasibility of a SAS to alert caregivers to the occurrence of prolonged or repetitive seizures and enables rescue medications to be delivered in a timely manner. The SAS may improve the management of human epilepsy by alerting caregivers of seizures, enabling early interventions, and potentially improving outcomes and quality of life of patients and caregivers.
"In dogs, the EEG was recorded from an implanted device which acquired data from 16 subdural electrodes . Two 4-contact strips were implanted over each hemisphere in an anterior-posterior orientation. "
[Show abstract][Hide abstract] ABSTRACT: A system for detection of seizures in intracranial EEG is presented that is based on a combination of generative, discriminative and hybrid approaches. We present a methodology to effectively benefit from the advantages each classifier offers. In particular, Gaussian mixture models, Support Vector Machines, hybrid likelihood ratio and Gaussian supervector approaches are developed and combined for the task. This system participated in the UPenn and Mayo Clinic's Seizure Detection Challenge, ranking in the top 5 of over 200 participants. The drawbacks of the proposed method with respect to the winning solutions are critically assessed.
IEEE Engineering in Medicine and Biology Society (EMBS), Milano, Italy; 08/2015
[Show abstract][Hide abstract] ABSTRACT: Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.
PLoS ONE 01/2014; 9(1):e81920. DOI:10.1371/journal.pone.0081920 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The mainstay of comparative research for epilepsy has been rodent models of induced epilepsy. This rodent basic science is essential, but it does not always translate to similar results in people, likely because induced epilepsy is not always similar enough to naturally occurring epilepsy. A good large animal, intermediate model would be very helpful to potentially bridge this translational gap. Epilepsy is the most common medical neurologic disease of dogs. It has been proposed since the 1970s that dogs with naturally occurring epilepsy could potentially be used as a comparative model for people of the underlying basis and therapy of epilepsy. There have been sporadic studies in the decades since then, with a relative surge in the last 10 years. These canine studies in the areas of genetics, drug therapy, dietary therapy, electroencelphalogram research, and devices for epilepsy show proof of concept that canine epilepsy can be a very good model for comparative research for many, but not all, facets of epilepsy. Results of research in canine epilepsy can and have benefited the improvement of treatment for both people and dogs.
ILAR journal / National Research Council, Institute of Laboratory Animal Resources 06/2014; 55(1):182-6. DOI:10.1093/ilar/ilu021 · 2.39 Impact Factor
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