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EEG Spike Detection using Stationary Wavelet Transform and Time-Varying Autoregressive Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 83 - Number 13 |
| Year of Publication: 2013 |
| Authors: Mehdi Radmehr, Seyed Mahmoud Anisheh |
10.5120/14505-2117
|
Mehdi Radmehr, Seyed Mahmoud Anisheh . EEG Spike Detection using Stationary Wavelet Transform and Time-Varying Autoregressive Model. International Journal of Computer Applications. 83, 13 ( December 2013), 1-3. DOI=10.5120/14505-2117
Abstract
Spikes are short-time broadband events, which can last 20ms-70ms and amplitude is among 100?V-200?V. In this research, a novel spike detection method based on stationary wavelet transform (SWT) and time-varying autoregressive model is proposed. In the proposed method, the discrete stationary wavelet transform is initially applied on the signal under analysis to show the important underlying unadulterated form of the data. The time-varying AR (TVAR) is used as an effective tool for analyzing non-stationary signals such as spikes. The performance of the proposed method is compared with other existing methods using both synthetic signals and real newborn Electroencephalogram (EEG). The simulation results indicate the absolute advantages of the proposed method.
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