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Quantitative methods for detecting cerebral infarction from multiple channel EEG recordings

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Abstract

EEG has been known to be non-stationary and time varying. Time–frequency representation (TFR) is a proper tool for such non-stationary signals. In the present paper, TFR-based quantitative methods that can translate complicated and subjective waveform-based EEG analysis into objective measures are introduced to characterize EEG recorded from normal subjects and cerebral infarction (CI) patients. Relative frequency band energy (RFBE) is computed from time–frequency plane for the five subbands: delta, theta, alpha, beta and gamma. Moreover, we propose the Shannon entropy (SE) of TFR to detect the difference in EEG for the two kinds of subjects. Finally, the temporal evolutions of these quantitative parameters are presented to trace EEG changes. The experiment results show that CI results in the RFBE changes of the five rhythms; however, the RFBEs of some rhythms have stronger association with CI. Increase in EEG SE of CI patients is obvious. The time evolutions of RFBE and SE as valuable objective measures can be displayed in real time and be used as helpful references in detection and monitoring of CI.

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Acknowledgments

The authors would like to thank Dr. X.B. Miao and Dr. J.F. Zhu for experiments. The authors gratefully acknowledge support from the Fundamental Research Funds for the Central Universities (Project No. CDJZR10150003) and the Scientific Research Foundation of State Key Laboratory of Power Transmission Equipment and System Security (Project No. 2007DA10512710503).

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Authors and Affiliations

  1. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing, 400030, China

    Li Zhang

  2. College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing, 400047, China

    Chuanhong He

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  1. Li Zhang
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  2. Chuanhong He
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Correspondence to Li Zhang.

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Zhang, L., He, C. Quantitative methods for detecting cerebral infarction from multiple channel EEG recordings. Neural Comput & Applic 21, 1159–1166 (2012). https://doi.org/10.1007/s00521-012-0835-3

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  • DOI: https://doi.org/10.1007/s00521-012-0835-3

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