Name
Abstract | ||
|---|---|---|
Epilepsy is one of the most common chronic neurological diseases and the most common neurological chronic disease of childhood. The electroencephalogram (EEG) signal provides significant information neurologists take into consideration in the investigation and analysis of epileptic seizures. The Approximate Entropy (ApEn) is a formulated statistical parameter commonly used to quantify the regularity of a time series data of physiological signals. In this paper ApEn is used in order to detect the onset of epileptic seizures. The results show that the method provides promising results towards efficient detection of onset and ending of seizures, based on analyzing the corresponding EEG signals. ApEn parameters affect the method's behavior, suggesting that a more detailed study and a consistent methodology of their determination should be established. A preliminary analysis for the proper determination of these parameters is performed towards improving the results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6609524 | 2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
noise,approximate entropy,neurophysiology,time series analysis,accuracy,eeg,statistical analysis,electroencephalography,entropy | Statistical parameter,Approximate entropy,Neuroscience,Neurophysiology,Computer science,Epilepsy,Signal classification,Chronic disease,Electroencephalography,Statistical analysis | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 3 |
PageRank | References | Authors |
0.47 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giorgos Giannakakis | 1 | 8 | 3.00 |
| Vangelis Sakkalis | 2 | 110 | 23.68 |
| Matthew Pediaditis | 3 | 3 | 0.81 |
| Christina Farmaki | 4 | 3 | 0.47 |
| Pelagia Vorgia | 5 | 12 | 2.14 |
| Manolis Tsiknakis | 6 | 18 | 4.71 |