Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial pressure

Authors:
Peng Xu;Xiao Hu;Dezhong Yao
Affiliations:
Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, United States and Key Laboratory for Neuro-Informati ...;Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, United States and Biomedical Engineering Graduate Pr ...;Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
Venue:
Computers in Biology and Medicine
Year:
2013

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ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III

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Abstract

The wavelet entropy is a novel way to measure the signal regularity, and its calculation is based on the energy distribution in wavelet sub-bands. However, wavelet entropy will be largely influenced by the noise usually existed in signals, especially in physiological signals. With aim to get more stable entropy calculation, a windowed wavelet entropy approach is proposed. In this paper, we systemically studied the difference between wavelet entropy and approximate entropy, which has yet not been studied in detail before. The conducted comparison with various signals reveals that wavelet entropy can measure the signal complexity like approximate entropy. Moreover, the relative wavelet entropy can be used to measure the dissimilarity between two signals. Compared to the original wavelet entropy approach, the comparison result also shows that the proposed window approach can get smoother and more stable calculation for both wavelet entropy and relative wavelet entropy, which is more meaningful to measure signal regularity and dissimilarity. The application to the time series recorded from a patient having the intracranial hypertension reveals that the new approach can clearly differentiate the normal and hypertension states, which may serve as a promising tool for prediction of intracranial pressure in future.