Ranking Power Spectra: A Proof of Concept
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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/11/1057 |
| _version_ | 1798039774106222592 |
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| author | Xilin Yu Zhenning Mei Chen Chen Wei Chen |
| author_facet | Xilin Yu Zhenning Mei Chen Chen Wei Chen |
| author_sort | Xilin Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:58:23Z |
| format | Article |
| id | doaj.art-2c64f4e797c94a20b95843cb60ee513c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T21:58:23Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-2c64f4e797c94a20b95843cb60ee513c2022-12-22T04:01:02ZengMDPI AGEntropy1099-43002019-10-012111105710.3390/e21111057e21111057Ranking Power Spectra: A Proof of ConceptXilin Yu0Zhenning Mei1Chen Chen2Wei Chen3Center for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, ChinaCenter for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, ChinaCenter for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, ChinaCenter for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, ChinaTo 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.https://www.mdpi.com/1099-4300/21/11/1057biomedical signal processingorder structurespectral entropy |
| spellingShingle | Xilin Yu Zhenning Mei Chen Chen Wei Chen Ranking Power Spectra: A Proof of Concept Entropy biomedical signal processing order structure spectral entropy |
| title | Ranking Power Spectra: A Proof of Concept |
| title_full | Ranking Power Spectra: A Proof of Concept |
| title_fullStr | Ranking Power Spectra: A Proof of Concept |
| title_full_unstemmed | Ranking Power Spectra: A Proof of Concept |
| title_short | Ranking Power Spectra: A Proof of Concept |
| title_sort | ranking power spectra a proof of concept |
| topic | biomedical signal processing order structure spectral entropy |
| url | https://www.mdpi.com/1099-4300/21/11/1057 |
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