Multiscale entropy analysis of EEG signals

Authors

  • Jacek Kapica Department of Electrical Engineering and Measurement Systems, University of Life Sciences in Lublin, Poland Author
  • Jolanta Masiak Department of Psychiatry, Medical University of Lublin, Poland Author
  • Andy R. Eugene Institute for the Study of Child Development, Department of Pediatrics, UMDNJ-Robert Wood Johnson Medical School Author
  • Katarzyna Ziniuk Department of Psychiatry, Medical University of Lublin, Poland Author

Keywords:

multiscale entropy, EEG, complexity, diagnosis

Abstract

The paper presents a novel way to analyze biological signals using an signal processing technique, called multiscale entropy and its applications in psychiatry. As an entropy-based algorithm, it measures degree of complexity of a given signal. The multiscale feature enables to assess the performance of the human brain over the various frequency bands of the electroencephalography (EEG) signal. The complexity of the EEG signal may reflect the ability of the system to react to the changes in the surrounding environment and thus be a marker of disease.

In this paper, the classical definition of entropy and multiscale entropy (MSE) is presented. Then three examples of the application of the MSE in psychiatry are shown, namely changes of the MSE curve with age, as well as in Alzheimer's disease and for early diagnosis of autism in infants.

References

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Published

2012-05-15

Issue

Vol. 13 No. 1 (2012): Current Problems of Psychiatry

Section

Artykuły