This paper deal with the problem of automatic detection of epileptic events in EEGs from depth electrodes using multiresolution wavelet analysis. The basic problems in events detection are considered: the time localization and characterization of epileptiform events, and the computational efficiency. The algorithm presented is based on a polynomial spline wavelet transform. The multiresolution representation obtained from this wavelet transform and the digital filters derived allow us an automatic detection, efficient and fast, of epileptiform activity. The detector proposed is based on the multiresolution energy function. This paper shows that it is possible to use a multiresolution wavelet scheme for detecting events in a nonstationary signal. EEG records from depth electrodes were analysed and the results obtained are shown.
| Identifer | oai:union.ndltd.org:PUCP/oai:tesis.pucp.edu.pe:123456789/97049 |
| Date | 25 September 2017 |
| Creators | D'Attellis, C. E., Isaacson, S. I., Sirne, R. O. |
| Publisher | Pontificia Universidad Católica del Perú |
| Source Sets | Pontificia Universidad Católica del Perú |
| Language | Español |
| Detected Language | English |
| Type | Artículo |
| Format | |
| Source | Pro Mathematica; Vol. 8, Núm. 15-16 (1994); 129-143 |
| Rights | Artículo en acceso abierto, Attribution 4.0 International, https://creativecommons.org/licenses/by/4.0/ |
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