Detection of Seizure Event and Its Onset/Offset Using Orthonormal Triadic Wavelet Based Features |
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Authors: | G. Chandel P. Upadhyaya O. Farooq Y.U. Khan |
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Affiliation: | 1. Department of Electronics and Communication Engineering, ITS Engineering College, Greater Noida-201310, Uttar Pradesh, India;2. Department of Electronics and Communication Engineering, KCNIT, Banda-210001, Uttar Pradesh, India;3. Department of Electronics Engineering, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh-202002, Uttar Pradesh, India;4. Department of Electrical Engineering, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh-202002, Uttar Pradesh, India |
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Abstract: | BackgroundEpileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.MethodsIn this paper, a combined approach for onset and offset detection is proposed using Triadic wavelet decomposition based features. Standard deviation, variance and higher order moments, extracted as significant features to represent different EEG activities.Classification between seizure and non-seizure EEG was carried out using linear discriminant analysis (LDA) and k-nearest neighbour (KNN) classifiers. The method was tested using two benchmark EEG datasets in the field of seizure detection.CHBMIT EEG dataset was used for evaluating the performance of proposed seizure onset and offset detection method.Further for testing the robustness of the algorithm, the effect of the signal-to-noise ratio on the detection accuracy has been also investigated using Bonn University EEG dataset.ResultsThe seizure onset and offset detection method yielded classification accuracy, specificity and sensitivity of 99.45%, 99.62% and 98.36% respectively with 6.3 s onset and ?1.17 s offset latency using KNN classifier.The seizure detection method using Bonn University EEG dataset got classification accuracy of 92% when SNR = 5 dB, 94% when SNR = 10 dB, and 96% when SNR = 20 dB, while it also yielded 96% accuracy for noiseless EEG.ConclusionThe present study focuses on detection of seizure onset and offset rather than only seizure detection. The major contribution of this work is that the novel triadic wavelet transform based method is developed for the analysis of EEG signals. The results show improvement over other existing dyadic wavelet based Triadic techniques. |
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Keywords: | Seizure detection EEG Wavelet transforms Linear discriminant analysis (LDA) Corresponding author at: ITS Engineering College Greater Noida, 46, Knowledge park III, Greater Noida, UP 201308, India. |
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