Comparison of Epileptic Seizure Detection using Auto-Regressive Model and Linear Prediction Model?
Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 5)Publication Date: 2014-05-30
Authors : Priyanka Jain; Pradeep Kumar Govindaiah;
Page : 263-267
Keywords : EEG; Artifacts; Electrical Activity; SWT; Adaptive Filtering; Pre-Ictal; Ictal;
Abstract
Artifacts causes the incorrect reading of Electroencephalography (EEG) Signal. Specific filtering technique is to be followed to remove the artifacts. In this paper, combination of Adaptive Filtering (AF) and Stationary Wavelet Transform (SWT) is proposed to remove artifacts from the EEG signal. EEG Signals from a healthy subject and from an Epileptic subject are compared using the Autoregressive Model and Linear Prediction Model. These models does not account for the presence of noise. The dominant pole (closest to the unit circle in the z-plane) of Linear Prediction Model shows better result as compared to the dominant pole of Autoregressive Model.
Other Latest Articles
- Probabilistic Topic Modeling using LDA of Taxonomic Structure of Genomic Data?
- Confronts and Applications in Marine Sensor Networks?
- Soft Computing Based Intrusion Detection System?
- A Survey of Sinkhole Attack on DSR in MANET?
- Improved Service Broker Algorithm Based On Weighted Moving Average Forecast Model for Cloud Computing?
Last modified: 2014-05-16 20:42:25