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Automatic Seizure Detection in EEG Based on Sparse Representation and Wavelet Transform

  • Shanshan Chen
  • Qingfang Meng
  • Yuehui Chen
  • Dong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9225)

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 %.

Keywords

Epileptic EEG Sparse representation based classification (SRC) Wavelet decomposition Classification 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shanshan Chen
    • 1
    • 2
  • Qingfang Meng
    • 1
    • 2
  • Yuehui Chen
    • 1
    • 2
  • Dong Wang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingJinanChina

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