Automatic epileptic seizure detection approach based on multi-stage Quantized Kernel Least Mean Square filters. (September 2021)

• Record Type: Journal Article
• Authors: Eltrass, Ahmed S.; Tayel, Mazhar B.; EL-qady, Ahmed F.
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Adaptive Filters -- Electroencephalogram (EEG) -- Epileptic Seizure -- Grey Wolf Optimizer (GWO) -- Quantized Kernel Least Mean Square (QKLMS); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103031
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new automated real-time approach for EEG epileptic seizure detection. Serial concatenation of QKLMS adaptive filters for EEG energy prediction. The performance of the proposed QKLMS is compared versus other adaptive filters. EEG epileptic seizure diagnosis with accuracy 97.89 %, sensitivity 98.80 %, and specificity 97.65% Results show superior performance of the proposed approach over other techniques. Abstract: Epilepsy is one of the most common neurological disorders of the brain all over the world. For its detection, Electroencephalogram (EEG) is an important noninvasive diagnostic approach. This paper proposes a new automated real-time approach for EEG epileptic seizure detection based on serial concatenation of Quantized Kernel Least Mean Square (QKLMS) adaptive filters. The QKLMS filter is employed in the proposed system to achieve accurate real-time diagnosis with low hardware complexity. In the proposed serial concatenation filter design, the energy of prediction signal is investigated to detect the seizure interval on the EEG records and to classify healthy, epileptic inter-ictal, and epileptic ictal cases. The Grey Wolf Optimizer (GWO) algorithm is employed to find the optimum values of the energy threshold and the parameters of the QKLMS algorithm. The performance of the proposed QKLMS technique is compared with other adaptive filters, and the results reveal the superior performance of the QKLMS algorithm. The proposed system is examined with real EEG records taken from the Bonn University database, and the experimental results show that the proposed epileptic seizure detection system outperforms other state-of-the-art systems with an accuracy of 97.88 %, sensitivity of 98.80 %, specificity of 97.65 %, and computational time of 0.58 sec. This demonstrates that the proposed approach can be used to assist neurologists in enhancing the accuracy of epileptic seizure detection and speeding-up the diagnosis process without the need for visual inspection or analysis of a large volume of EEG data.