Andere Kunden interessierten sich auch für
![Using support vector machines in fuzzy classification]( "Using support vector machines in fuzzy classification")
Using support vector machines in fuzzy classification23,99 €![Support Vector Machines and Particle Swarm Optimization]( "Support Vector Machines and Particle Swarm Optimization")
Support Vector Machines and Particle Swarm Optimization32,99 €![A kernel study on Support Vector Machines]( "A kernel study on Support Vector Machines")
A kernel study on Support Vector Machines32,99 €![Support Vector Machine]( "Support Vector Machine")
Support Vector Machine22,99 €![Support Vector Machines for Antenna Array Processing and Electromagnetics]( "Support Vector Machines for Antenna Array Processing and Electromagnetics")
Support Vector Machines for Antenna Array Processing and Electromagnetics35,30 €![Seizure Detection Using Soft Computing Techniques]( "Seizure Detection Using Soft Computing Techniques")
Seizure Detection Using Soft Computing Techniques30,99 €![Optimisation of Rock Support in Headrace Tunnel using Phase2]( "Optimisation of Rock Support in Headrace Tunnel using Phase2")
Optimisation of Rock Support in Headrace Tunnel using Phase226,99 €
Epilepsy is the second most common neurologic disorder which is characterized by recurrent and spontaneous seizures. Seizures occur unpredictably which makes everyday tasks such as driving and working extremely difficult resulting in a reduced quality of life. Epilepsy monitoring units measure the electrical activity of the brain using electroencephalography in an attempt to locate the epileptic brain tissue. However since seizures occur unpredictably and are generally infrequent, long recording times generate massive quantities of data. Automating this process using a seizure detection algorithm will ultimately save time and money, allow for superior and safer care of patients, and provide a better diagnostic tool. Although seizure detection has been well studied in the laboratory and clinic, a widely accepted algorithm has not been developed largely due to the fact that automated routines do not perform as well as a neurologist. In order to improve performance, we tested the hypothesis that multiple algorithms would work better than any single approach. Multiple algorithms were used as feature extractors and were implemented into a support vector machine (SVM) algorithm.