Name
Title | ||
|---|---|---|
An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals |
Abstract | ||
|---|---|---|
An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2-13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BIBE.2015.7367698 | IEEE International Conference on Bioinformatics and Bioengineering |
Keywords | Field | DocType |
epileptic seizures,long-term EEG signals,unsupervised methodology,short-time Fourier transform,EEG energy distribution,alpha rhythms,seizure rhythmicity,EEG recordings,public database,frequency 2 Hz to 13 Hz | Seizure detection,Alpha rhythms,Computer science,Short-time Fourier transform,Speech recognition,Time–frequency analysis,Artificial intelligence,Rhythm,Electroencephalography,Machine learning,Energy distribution | Conference |
Citations | PageRank | References |
1 | 0.36 | 1 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kostas M. Tsiouris | 1 | 14 | 1.98 |
| Spiros Konitsiotis | 2 | 69 | 8.69 |
| Sofia Markoula | 3 | 1 | 2.39 |
| Dimitrios Koutsouris | 4 | 25 | 16.43 |
| Antonis Sakellarios | 5 | 32 | 18.83 |
| Dimitrios I. Fotiadis | 6 | 941 | 121.32 |