Generalized multiscale Lempel–Ziv complexity of cyclic alternating pattern during sleep View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-09

AUTHORS

Chien-Hung Yeh, Wenbin Shi

ABSTRACT

Increasing evidences show that multiscale complexity measure is an intuitive and effective measure in quantifying various physical and physiological states. In this study, we demonstrate that the classical algorithm of multiscale Lempel–Ziv complexity (multiscale LZC or MLZ) has a critical limitation in neglecting rapid rhythms in complex systems. To this end, simulations added with different levels of white noise are designed to examine whether or not MLZ calculation neglects the effects of high-frequency noise. In addition, an algorithm by obtaining coarse-grained multiscale LZC, so-called generalized multiscale LZC (gMLZ), is proposed to yield a spectrum of complexity. A series of simulated non-stationary signals are generated for comparing the performances between MLZ and gMLZ. Besides, cyclic alternating pattern (CAP), characterized by the excessive synchronization of neuronal activity, has been associated with its power and physiological states. To understand how the synchronization of neuronal activities in different phase-A subtypes in exerting an influence over its power and complexity, we analyze the gMLZ of the real CAP database and compare it to its power spectra as well as modified multiscale entropy (MMSE), which is one of the most well-known multiscale complexity-based measures. The novel algorithm reveals that the evaluated complexities in different phase-A subtypes are inversely related to both the power and excessive synchronization in different timescales in general. The impact of frequencies, sleep stages and pathophysiological conditions on these two complexity measures is also examined. The discerning abilities of different phase-A subtypes using coarse-grained complexity measures (gMLZ and MMSE) are more consistent than power across different time scales. Our approach makes up a deficiency in handling with high-frequency oscillations and enables us to examine complexities of nonlinear systems in a wide-range of timescales. More... »

PAGES

1899-1910

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11071-018-4296-9

DOI

http://dx.doi.org/10.1007/s11071-018-4296-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103623028


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0101", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Pure Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Chang Gung Memorial Hospital", 
          "id": "https://www.grid.ac/institutes/grid.413801.f", 
          "name": [
            "Department of Neurology, Chang Gung Memorial Hospital and University, 333, Taoyuan City, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yeh", 
        "givenName": "Chien-Hung", 
        "id": "sg:person.0714757464.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0714757464.04"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tsinghua University", 
          "id": "https://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, 100084, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shi", 
        "givenName": "Wenbin", 
        "id": "sg:person.011517356407.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011517356407.96"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.3390/e17053110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001675891"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physa.2013.07.075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002568723"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sleep.2004.06.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003549551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-2789(92)90023-g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004056050"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-2789(92)90023-g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004056050"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.012579499", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004887097"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2011.04.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007787550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2006.07.314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008910358"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1099745", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010299182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004691-200101000-00010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010360896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004691-200101000-00010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010360896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004691-200101000-00010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010360896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cnsns.2016.08.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010683287"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2012.04.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016219961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0013-4694(94)90108-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017693598"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0013-4694(94)90108-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017693598"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1389-9457(01)00149-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018436127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-2869.2000.00190.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019751959"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.homp.2012.05.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028549505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0019-9958(64)90131-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031502743"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1550059412475066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032107521"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1550059412475066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032107521"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.101.23.e215", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032570273"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2011.08.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032866874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1388-2457(99)00245-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033237735"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2014.07.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033884378"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2005.06.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036572207"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2005.06.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036572207"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2005.08.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037912198"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinph.2008.01.104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042567652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsta.2008.0197", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043422162"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-8760(02)00006-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045144311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.brainresbull.2003.12.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046550311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0013-4694(92)90026-e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046755412"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0013-4694(92)90026-e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046755412"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4061/2011/539621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049783673"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0013-4694(97)00079-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049842380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1388-2457(99)00013-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050310857"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1742-5468/2009/02/p02066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059163217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004691-199705000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060185629"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004691-199705000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060185629"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004691-199705000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060185629"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.36.842", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060477004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.36.842", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060477004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.71.021906", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060732566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.71.021906", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060732566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.50.346", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060788721"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.50.346", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060788721"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2002.804582", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061525776"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2006.883696", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061526807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.1976.1055501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061647733"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/155005949802900209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064061383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/155005949802900209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064061383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/ajpheart.2000.278.6.h2039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074650018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1075996329", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/sleep/8.2.137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1080081165"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-09", 
    "datePublishedReg": "2018-09-01", 
    "description": "Increasing evidences show that multiscale complexity measure is an intuitive and effective measure in quantifying various physical and physiological states. In this study, we demonstrate that the classical algorithm of multiscale Lempel\u2013Ziv complexity (multiscale LZC or MLZ) has a critical limitation in neglecting rapid rhythms in complex systems. To this end, simulations added with different levels of white noise are designed to examine whether or not MLZ calculation neglects the effects of high-frequency noise. In addition, an algorithm by obtaining coarse-grained multiscale LZC, so-called generalized multiscale LZC (gMLZ), is proposed to yield a spectrum of complexity. A series of simulated non-stationary signals are generated for comparing the performances between MLZ and gMLZ. Besides, cyclic alternating pattern (CAP), characterized by the excessive synchronization of neuronal activity, has been associated with its power and physiological states. To understand how the synchronization of neuronal activities in different phase-A subtypes in exerting an influence over its power and complexity, we analyze the gMLZ of the real CAP database and compare it to its power spectra as well as modified multiscale entropy (MMSE), which is one of the most well-known multiscale complexity-based measures. The novel algorithm reveals that the evaluated complexities in different phase-A subtypes are inversely related to both the power and excessive synchronization in different timescales in general. The impact of frequencies, sleep stages and pathophysiological conditions on these two complexity measures is also examined. The discerning abilities of different phase-A subtypes using coarse-grained complexity measures (gMLZ and MMSE) are more consistent than power across different time scales. Our approach makes up a deficiency in handling with high-frequency oscillations and enables us to examine complexities of nonlinear systems in a wide-range of timescales.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11071-018-4296-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1040905", 
        "issn": [
          "0924-090X", 
          "1573-269X"
        ], 
        "name": "Nonlinear Dynamics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "93"
      }
    ], 
    "name": "Generalized multiscale Lempel\u2013Ziv complexity of cyclic alternating pattern during sleep", 
    "pagination": "1899-1910", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "0849823a10fc012863bda64f23c696ebd5448afa8087f4808d9d3801e360e1c3"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11071-018-4296-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1103623028"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11071-018-4296-9", 
      "https://app.dimensions.ai/details/publication/pub.1103623028"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89807_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11071-018-4296-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11071-018-4296-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11071-018-4296-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11071-018-4296-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11071-018-4296-9'


 

This table displays all metadata directly associated to this object as RDF triples.

199 TRIPLES      21 PREDICATES      70 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11071-018-4296-9 schema:about anzsrc-for:01
2 anzsrc-for:0101
3 schema:author N0fce6f67d51e4e1e9c0d6c28f57d2ca2
4 schema:citation https://app.dimensions.ai/details/publication/pub.1075996329
5 https://doi.org/10.1016/0013-4694(92)90026-e
6 https://doi.org/10.1016/0013-4694(94)90108-2
7 https://doi.org/10.1016/0167-2789(92)90023-g
8 https://doi.org/10.1016/j.brainresbull.2003.12.013
9 https://doi.org/10.1016/j.clinph.2005.06.011
10 https://doi.org/10.1016/j.clinph.2005.08.013
11 https://doi.org/10.1016/j.clinph.2006.07.314
12 https://doi.org/10.1016/j.clinph.2008.01.104
13 https://doi.org/10.1016/j.clinph.2011.04.011
14 https://doi.org/10.1016/j.clinph.2011.08.003
15 https://doi.org/10.1016/j.clinph.2012.04.025
16 https://doi.org/10.1016/j.clinph.2014.07.012
17 https://doi.org/10.1016/j.cnsns.2016.08.019
18 https://doi.org/10.1016/j.homp.2012.05.007
19 https://doi.org/10.1016/j.physa.2013.07.075
20 https://doi.org/10.1016/j.sleep.2004.06.010
21 https://doi.org/10.1016/s0013-4694(97)00079-5
22 https://doi.org/10.1016/s0019-9958(64)90131-7
23 https://doi.org/10.1016/s0167-8760(02)00006-5
24 https://doi.org/10.1016/s1388-2457(99)00013-9
25 https://doi.org/10.1016/s1388-2457(99)00245-x
26 https://doi.org/10.1016/s1389-9457(01)00149-6
27 https://doi.org/10.1046/j.1365-2869.2000.00190.x
28 https://doi.org/10.1073/pnas.012579499
29 https://doi.org/10.1088/1742-5468/2009/02/p02066
30 https://doi.org/10.1093/sleep/8.2.137
31 https://doi.org/10.1097/00004691-199705000-00005
32 https://doi.org/10.1097/00004691-200101000-00010
33 https://doi.org/10.1098/rsta.2008.0197
34 https://doi.org/10.1103/physreva.36.842
35 https://doi.org/10.1103/physreve.71.021906
36 https://doi.org/10.1103/physrevlett.50.346
37 https://doi.org/10.1109/tbme.2002.804582
38 https://doi.org/10.1109/tbme.2006.883696
39 https://doi.org/10.1109/tit.1976.1055501
40 https://doi.org/10.1126/science.1099745
41 https://doi.org/10.1152/ajpheart.2000.278.6.h2039
42 https://doi.org/10.1161/01.cir.101.23.e215
43 https://doi.org/10.1177/1550059412475066
44 https://doi.org/10.1177/155005949802900209
45 https://doi.org/10.3390/e17053110
46 https://doi.org/10.4061/2011/539621
47 schema:datePublished 2018-09
48 schema:datePublishedReg 2018-09-01
49 schema:description Increasing evidences show that multiscale complexity measure is an intuitive and effective measure in quantifying various physical and physiological states. In this study, we demonstrate that the classical algorithm of multiscale Lempel–Ziv complexity (multiscale LZC or MLZ) has a critical limitation in neglecting rapid rhythms in complex systems. To this end, simulations added with different levels of white noise are designed to examine whether or not MLZ calculation neglects the effects of high-frequency noise. In addition, an algorithm by obtaining coarse-grained multiscale LZC, so-called generalized multiscale LZC (gMLZ), is proposed to yield a spectrum of complexity. A series of simulated non-stationary signals are generated for comparing the performances between MLZ and gMLZ. Besides, cyclic alternating pattern (CAP), characterized by the excessive synchronization of neuronal activity, has been associated with its power and physiological states. To understand how the synchronization of neuronal activities in different phase-A subtypes in exerting an influence over its power and complexity, we analyze the gMLZ of the real CAP database and compare it to its power spectra as well as modified multiscale entropy (MMSE), which is one of the most well-known multiscale complexity-based measures. The novel algorithm reveals that the evaluated complexities in different phase-A subtypes are inversely related to both the power and excessive synchronization in different timescales in general. The impact of frequencies, sleep stages and pathophysiological conditions on these two complexity measures is also examined. The discerning abilities of different phase-A subtypes using coarse-grained complexity measures (gMLZ and MMSE) are more consistent than power across different time scales. Our approach makes up a deficiency in handling with high-frequency oscillations and enables us to examine complexities of nonlinear systems in a wide-range of timescales.
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree false
53 schema:isPartOf N7186d6a8faa5425ab21b782dc9f1fc98
54 Nf5df9edccf084d4f9113775ff55c1a2a
55 sg:journal.1040905
56 schema:name Generalized multiscale Lempel–Ziv complexity of cyclic alternating pattern during sleep
57 schema:pagination 1899-1910
58 schema:productId N230d403f80614160add2b0d813fc69df
59 N4ff626cb9aaf4c29a0e34316bc4d5924
60 Nfdd4c571111b4dafa4f48e4a6794417e
61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103623028
62 https://doi.org/10.1007/s11071-018-4296-9
63 schema:sdDatePublished 2019-04-11T09:57
64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
65 schema:sdPublisher N923021963dc54f7e9a02129236a7f791
66 schema:url https://link.springer.com/10.1007%2Fs11071-018-4296-9
67 sgo:license sg:explorer/license/
68 sgo:sdDataset articles
69 rdf:type schema:ScholarlyArticle
70 N0fce6f67d51e4e1e9c0d6c28f57d2ca2 rdf:first sg:person.0714757464.04
71 rdf:rest N11eebc22ac0e4fd5b6d0a0a516b5efa3
72 N11eebc22ac0e4fd5b6d0a0a516b5efa3 rdf:first sg:person.011517356407.96
73 rdf:rest rdf:nil
74 N230d403f80614160add2b0d813fc69df schema:name doi
75 schema:value 10.1007/s11071-018-4296-9
76 rdf:type schema:PropertyValue
77 N4ff626cb9aaf4c29a0e34316bc4d5924 schema:name readcube_id
78 schema:value 0849823a10fc012863bda64f23c696ebd5448afa8087f4808d9d3801e360e1c3
79 rdf:type schema:PropertyValue
80 N7186d6a8faa5425ab21b782dc9f1fc98 schema:volumeNumber 93
81 rdf:type schema:PublicationVolume
82 N923021963dc54f7e9a02129236a7f791 schema:name Springer Nature - SN SciGraph project
83 rdf:type schema:Organization
84 Nf5df9edccf084d4f9113775ff55c1a2a schema:issueNumber 4
85 rdf:type schema:PublicationIssue
86 Nfdd4c571111b4dafa4f48e4a6794417e schema:name dimensions_id
87 schema:value pub.1103623028
88 rdf:type schema:PropertyValue
89 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
90 schema:name Mathematical Sciences
91 rdf:type schema:DefinedTerm
92 anzsrc-for:0101 schema:inDefinedTermSet anzsrc-for:
93 schema:name Pure Mathematics
94 rdf:type schema:DefinedTerm
95 sg:journal.1040905 schema:issn 0924-090X
96 1573-269X
97 schema:name Nonlinear Dynamics
98 rdf:type schema:Periodical
99 sg:person.011517356407.96 schema:affiliation https://www.grid.ac/institutes/grid.12527.33
100 schema:familyName Shi
101 schema:givenName Wenbin
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011517356407.96
103 rdf:type schema:Person
104 sg:person.0714757464.04 schema:affiliation https://www.grid.ac/institutes/grid.413801.f
105 schema:familyName Yeh
106 schema:givenName Chien-Hung
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0714757464.04
108 rdf:type schema:Person
109 https://app.dimensions.ai/details/publication/pub.1075996329 schema:CreativeWork
110 https://doi.org/10.1016/0013-4694(92)90026-e schema:sameAs https://app.dimensions.ai/details/publication/pub.1046755412
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/0013-4694(94)90108-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017693598
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/0167-2789(92)90023-g schema:sameAs https://app.dimensions.ai/details/publication/pub.1004056050
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.brainresbull.2003.12.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046550311
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.clinph.2005.06.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036572207
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.clinph.2005.08.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037912198
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.clinph.2006.07.314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008910358
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.clinph.2008.01.104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042567652
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.clinph.2011.04.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007787550
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.clinph.2011.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032866874
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.clinph.2012.04.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016219961
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.clinph.2014.07.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033884378
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.cnsns.2016.08.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010683287
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/j.homp.2012.05.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028549505
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/j.physa.2013.07.075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002568723
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/j.sleep.2004.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003549551
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/s0013-4694(97)00079-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049842380
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/s0019-9958(64)90131-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031502743
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/s0167-8760(02)00006-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045144311
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/s1388-2457(99)00013-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050310857
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/s1388-2457(99)00245-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1033237735
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/s1389-9457(01)00149-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018436127
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1046/j.1365-2869.2000.00190.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1019751959
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1073/pnas.012579499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004887097
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1088/1742-5468/2009/02/p02066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059163217
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1093/sleep/8.2.137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080081165
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1097/00004691-199705000-00005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060185629
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1097/00004691-200101000-00010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010360896
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1098/rsta.2008.0197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043422162
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1103/physreva.36.842 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060477004
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1103/physreve.71.021906 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060732566
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1103/physrevlett.50.346 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060788721
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1109/tbme.2002.804582 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061525776
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1109/tbme.2006.883696 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061526807
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1109/tit.1976.1055501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061647733
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1126/science.1099745 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010299182
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1152/ajpheart.2000.278.6.h2039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074650018
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1161/01.cir.101.23.e215 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032570273
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1177/1550059412475066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032107521
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1177/155005949802900209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064061383
189 rdf:type schema:CreativeWork
190 https://doi.org/10.3390/e17053110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001675891
191 rdf:type schema:CreativeWork
192 https://doi.org/10.4061/2011/539621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049783673
193 rdf:type schema:CreativeWork
194 https://www.grid.ac/institutes/grid.12527.33 schema:alternateName Tsinghua University
195 schema:name State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, 100084, Beijing, China
196 rdf:type schema:Organization
197 https://www.grid.ac/institutes/grid.413801.f schema:alternateName Chang Gung Memorial Hospital
198 schema:name Department of Neurology, Chang Gung Memorial Hospital and University, 333, Taoyuan City, Taiwan
199 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...