De-noising by soft-thresholding
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
The recording of seizures is of primary interest in the evaluation of epileptic transients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behaviour usually lasts from seconds to minutes. Since seizures in general occur infrequently and unpredictably, an automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper proposes a new method for the detection of epileptic transients in EEG by using continuous wavelet transform (CWT) with suitable mother wavelet functions and thresholding method. We demonstrate the efficiency of our method on data to identify and clearly locate in time the seizure activities. The method is superior both in separation from noise and in identifying superimposed epileptic action potentials based on in sets of combined scales. We prove that this method is fast and simple which also reduces real time computations.