A Statistical Seizure Prediction Approach Based on Savitzky-Golay Smoothing | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 3, Volume 27, Issue 1, January 2018, Page 53-70 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2018.64386 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Ahmed Sedik1; Turky Alotaiby2; Heba El-Khobby1; Mahmoud Atea1; Saleh A. Alshebeili3; Fathi E. Abd El-Samie4 | ||||
| 1Dept. of Electronics and Communications Engineering, Faculty of Engineering, Tanta University. | ||||
| 2King Abdalziz City for Science and Technology, Riyadh City, KSA | ||||
| 3king saud university, Riyadh City | ||||
| 4Dept. of Electronics and Electrical Communications, Faculty of Engineering, Menoufia University | ||||
| Abstract | ||||
| "> This paper presents an enhanced seizure prediction technique based on a statistical approach for channel selection depending on amplitude, median, mean, variance, and derivative of processed EEG signals. The EEG pre-processing depends on Savitzky Golay (S-G) digital filter for smoothing of the signals, while maintaining the signal peaks. This technique consists of two phases; training, by randomly selected hours from normal, ictal and pre-ictal periods, and then estimating five Probability Density Functions (PDFs), and testing, by discrimination between normal and pre-ictal periods, and then the determination of a discrimination count threshold to predict the epilepsy seizure. Applying this approach on patients’ data taken by MIT shows that we can achieve high prediction accuracy (93.5%) with low false alarm rate (0.148/h) and a good prediction time (51.8166 min). | ||||
| References | ||||
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