Abstract
Sparse representation has been widely applied to pattern classification in recent years. In the framework of sparse representation based classification (SRC), the test sample is represented as a sparse linear combination of the training samples. Due to the epileptic EEG signals are non-stationary and transitory, wavelet transform as a time-frequency analysis method is widely used to analyze EEG signals. In this work, a novel EEG signal classification method based on sparse representation and wavelet transform was proposed to detect the epileptic EEG from EEG recordings. The frequency subbands decomposed by wavelet transform provided more information than the entire EEG. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 98 %.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61201428, 61302090), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2010FQ020, ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003, BS2014DX015), the Graduate Innovation Foundation of University of Jinan (Grant No. YCX13011).
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Chen, S., Meng, Q., Chen, Y., Wang, D. (2015). Automatic Seizure Detection in EEG Based on Sparse Representation and Wavelet Transform. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_20
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DOI: https://doi.org/10.1007/978-3-319-22180-9_20
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