Improving early seizure detection

Early seizure detection.

For patients with medically intractable epilepsy, there have been few effective alternatives to resective surgery, a destructive, irreversible treatment. A strategy receiving increased attention is using interictal spike patterns and continuous EEG measurements from epileptic patients to predict and ultimately control seizure activity via chemical or electrical control systems. This work compar...

Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term el...

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecom...

Early Detection of Seizure With a Sequential Analysis Approach

Improving Region Selection Through Early-Exit Detection

Many dynamic binary translation (DBT) systems and just-in-time compilers target traces, i.e. frequently-taken execution paths, as code regions to be translated/optimized. The Next-Tail-Execution (NET) trace selection method used in HP Dynamo is an early example of such techniques. Many current trace optimization schemes are actually variations of NET. These NET-like trace optimizations work ver...