Ranking Power Spectra: A Proof of Concept

Autor: Xilin Yu, Zhenning Mei, Chen Chen, Wei Chen
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Entropy, Vol 21, Iss 11, p 1057 (2019)
Druh dokumentu: article
ISSN: 1099-4300
21111057
DOI: 10.3390/e21111057
Popis: To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power spectrum estimations on a predefined grid. This erases the inherent order structure in the spectrum. To disentangle the order structure and extract meaningful information from raw digital signal, a novel analysis method is necessary. In this paper, we tried to unfold this order structure by defining descriptors mapping real- and vector-valued power spectrum estimation of a signal into a scalar value. The proposed descriptors showed its potential in diverse problems. Significant differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as a candidate feature for seizure detection and endpoint detection in speech signal. Since the order structure in the spectrum of physiological signal carries previously ignored information, which cannot be properly extracted by existing techniques, this paper takes one step forward along this direction by proposing computationally efficient descriptors with guaranteed information gain. To the best of our knowledge, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing.
Databáze: Directory of Open Access Journals
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