{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:48:28Z","timestamp":1760240908764,"version":"build-2065373602"},"reference-count":13,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFE0112000"],"award-info":[{"award-number":["2017YFE0112000"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["No.2018T110346 and No.2018M632019"],"award-info":[{"award-number":["No.2018T110346 and No.2018M632019"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e21111057","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"1057","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Ranking Power Spectra: A Proof of Concept"],"prefix":"10.3390","volume":"21","author":[{"given":"Xilin","family":"Yu","sequence":"first","affiliation":[{"name":"Center for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenning","family":"Mei","sequence":"additional","affiliation":[{"name":"Center for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Intelligent Medical Electronics (CIME), Fudan University, Shanghai 200433, China"},{"name":"Human Phenome Institute, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TAU.1969.1162036","article-title":"The finite Fourier transform","volume":"17","author":"Cooley","year":"1969","journal-title":"IEEE Trans. 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Maximum-Entropy and Bayesian Methods in Inverse Problems, Springer.","DOI":"10.1007\/978-94-017-2221-6"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On Information and Sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sapsanis, C., Georgoulas, G., Tzes, A., and Lymberopoulos, D. (2013, January 3\u20137). Improving EMG based classification of basic hand movements using EMD. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610858"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C.E. (2001). 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