Complexity and Disorder of 1/fα Noises Article Swipe
Chang Francis Hsu
,
Long Hsu
,
Sien Chi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/e22101127
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/e22101127
The complexity and the disorder of a 1/fα noise time series are quantified by entropy of entropy (EoE) and average entropy (AE), respectively. The resulting EoE vs. AE plot of a series of 1/fα noises of various values of α exhibits a distinct inverted U curve. For the 1/fα noises, we have shown that α decreases monotonically as AE increases, which indicates that α is also a measure of disorder. Furthermore, a 1/fα noise and a cardiac interbeat (RR) interval series are considered equivalent as they have the same AE. Accordingly, we have found that the 1/fα noises for α around 1.5 are equivalent to the RR interval series of healthy subjects. The pink noise at α = 1 is equivalent to atrial fibrillation (AF) RR interval series while the white noise at α = 0 is more disordered than AF RR interval series. These results, based on AE, are different from the previous ones based on spectral analysis. The testing macro-average F-score is 0.93 when classifying the RR interval series of three groups using AE-based α, while it is 0.73 when using spectral-analysis-based α.
Related Topics
Concepts
Mathematics
White noise
Series (stratigraphy)
Monotonic function
Spectral analysis
Entropy (arrow of time)
Noise (video)
Interval (graph theory)
Statistics
Physics
Mathematical analysis
Combinatorics
Thermodynamics
Artificial intelligence
Quantum mechanics
Spectroscopy
Computer science
Image (mathematics)
Biology
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e22101127
- https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553
- OA Status
- gold
- Cited By
- 3
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3092593284
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3092593284Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/e22101127Digital Object Identifier
- Title
-
Complexity and Disorder of 1/fα NoisesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-04Full publication date if available
- Authors
-
Chang Francis Hsu, Long Hsu, Sien ChiList of authors in order
- Landing page
-
https://doi.org/10.3390/e22101127Publisher landing page
- PDF URL
-
https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553Direct OA link when available
- Concepts
-
Mathematics, White noise, Series (stratigraphy), Monotonic function, Spectral analysis, Entropy (arrow of time), Noise (video), Interval (graph theory), Statistics, Physics, Mathematical analysis, Combinatorics, Thermodynamics, Artificial intelligence, Quantum mechanics, Spectroscopy, Computer science, Image (mathematics), Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3092593284 |
|---|---|
| doi | https://doi.org/10.3390/e22101127 |
| ids.doi | https://doi.org/10.3390/e22101127 |
| ids.mag | 3092593284 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33286896 |
| ids.openalex | https://openalex.org/W3092593284 |
| fwci | 0.73991649 |
| type | article |
| title | Complexity and Disorder of 1/fα Noises |
| biblio.issue | 10 |
| biblio.volume | 22 |
| biblio.last_page | 1127 |
| biblio.first_page | 1127 |
| topics[0].id | https://openalex.org/T11270 |
| topics[0].field.id | https://openalex.org/fields/20 |
| topics[0].field.display_name | Economics, Econometrics and Finance |
| topics[0].score | 0.9975000023841858 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2002 |
| topics[0].subfield.display_name | Economics and Econometrics |
| topics[0].display_name | Complex Systems and Time Series Analysis |
| topics[1].id | https://openalex.org/T10745 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9943000078201294 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | Heart Rate Variability and Autonomic Control |
| topics[2].id | https://openalex.org/T12205 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9790999889373779 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1711 |
| topics[2].subfield.display_name | Signal Processing |
| topics[2].display_name | Time Series Analysis and Forecasting |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| concepts[0].id | https://openalex.org/C33923547 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6690099239349365 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[0].display_name | Mathematics |
| concepts[1].id | https://openalex.org/C112633086 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6410313844680786 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q381287 |
| concepts[1].display_name | White noise |
| concepts[2].id | https://openalex.org/C143724316 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6138976812362671 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q312468 |
| concepts[2].display_name | Series (stratigraphy) |
| concepts[3].id | https://openalex.org/C72169020 |
| concepts[3].level | 2 |
| concepts[3].score | 0.47687917947769165 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q194404 |
| concepts[3].display_name | Monotonic function |
| concepts[4].id | https://openalex.org/C2983668108 |
| concepts[4].level | 3 |
| concepts[4].score | 0.44071581959724426 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q280453 |
| concepts[4].display_name | Spectral analysis |
| concepts[5].id | https://openalex.org/C106301342 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4234526753425598 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q4117933 |
| concepts[5].display_name | Entropy (arrow of time) |
| concepts[6].id | https://openalex.org/C99498987 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4202117621898651 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[6].display_name | Noise (video) |
| concepts[7].id | https://openalex.org/C2778067643 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4108370542526245 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q166507 |
| concepts[7].display_name | Interval (graph theory) |
| concepts[8].id | https://openalex.org/C105795698 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3396907150745392 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[8].display_name | Statistics |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.234477698802948 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C134306372 |
| concepts[10].level | 1 |
| concepts[10].score | 0.18984884023666382 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[10].display_name | Mathematical analysis |
| concepts[11].id | https://openalex.org/C114614502 |
| concepts[11].level | 1 |
| concepts[11].score | 0.1655384600162506 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[11].display_name | Combinatorics |
| concepts[12].id | https://openalex.org/C97355855 |
| concepts[12].level | 1 |
| concepts[12].score | 0.10486793518066406 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[12].display_name | Thermodynamics |
| concepts[13].id | https://openalex.org/C154945302 |
| concepts[13].level | 1 |
| concepts[13].score | 0.06241869926452637 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[13].display_name | Artificial intelligence |
| concepts[14].id | https://openalex.org/C62520636 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[14].display_name | Quantum mechanics |
| concepts[15].id | https://openalex.org/C32891209 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q483666 |
| concepts[15].display_name | Spectroscopy |
| concepts[16].id | https://openalex.org/C41008148 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[16].display_name | Computer science |
| concepts[17].id | https://openalex.org/C115961682 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[17].display_name | Image (mathematics) |
| concepts[18].id | https://openalex.org/C86803240 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[18].display_name | Biology |
| concepts[19].id | https://openalex.org/C151730666 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[19].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/mathematics |
| keywords[0].score | 0.6690099239349365 |
| keywords[0].display_name | Mathematics |
| keywords[1].id | https://openalex.org/keywords/white-noise |
| keywords[1].score | 0.6410313844680786 |
| keywords[1].display_name | White noise |
| keywords[2].id | https://openalex.org/keywords/series |
| keywords[2].score | 0.6138976812362671 |
| keywords[2].display_name | Series (stratigraphy) |
| keywords[3].id | https://openalex.org/keywords/monotonic-function |
| keywords[3].score | 0.47687917947769165 |
| keywords[3].display_name | Monotonic function |
| keywords[4].id | https://openalex.org/keywords/spectral-analysis |
| keywords[4].score | 0.44071581959724426 |
| keywords[4].display_name | Spectral analysis |
| keywords[5].id | https://openalex.org/keywords/entropy |
| keywords[5].score | 0.4234526753425598 |
| keywords[5].display_name | Entropy (arrow of time) |
| keywords[6].id | https://openalex.org/keywords/noise |
| keywords[6].score | 0.4202117621898651 |
| keywords[6].display_name | Noise (video) |
| keywords[7].id | https://openalex.org/keywords/interval |
| keywords[7].score | 0.4108370542526245 |
| keywords[7].display_name | Interval (graph theory) |
| keywords[8].id | https://openalex.org/keywords/statistics |
| keywords[8].score | 0.3396907150745392 |
| keywords[8].display_name | Statistics |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.234477698802948 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/mathematical-analysis |
| keywords[10].score | 0.18984884023666382 |
| keywords[10].display_name | Mathematical analysis |
| keywords[11].id | https://openalex.org/keywords/combinatorics |
| keywords[11].score | 0.1655384600162506 |
| keywords[11].display_name | Combinatorics |
| keywords[12].id | https://openalex.org/keywords/thermodynamics |
| keywords[12].score | 0.10486793518066406 |
| keywords[12].display_name | Thermodynamics |
| keywords[13].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[13].score | 0.06241869926452637 |
| keywords[13].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.3390/e22101127 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S195231649 |
| locations[0].source.issn | 1099-4300 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1099-4300 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Entropy |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Entropy |
| locations[0].landing_page_url | https://doi.org/10.3390/e22101127 |
| locations[1].id | pmid:33286896 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Entropy (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33286896 |
| locations[2].id | pmh:oai:doaj.org/article:e5ae38188729456684b48604ff2f8206 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Entropy, Vol 22, Iss 10, p 1127 (2020) |
| locations[2].landing_page_url | https://doaj.org/article/e5ae38188729456684b48604ff2f8206 |
| locations[3].id | pmh:oai:mdpi.com:/1099-4300/22/10/1127/ |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400947 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | MDPI (MDPI AG) |
| locations[3].source.host_organization | https://openalex.org/I4210097602 |
| locations[3].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Entropy; Volume 22; Issue 10; Pages: 1127 |
| locations[3].landing_page_url | https://dx.doi.org/10.3390/e22101127 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:7597264 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Entropy (Basel) |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7597264 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5090716072 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9254-9364 |
| authorships[0].author.display_name | Chang Francis Hsu |
| authorships[0].countries | TW |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I148366613 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Electrophyics, National Chiao Tung University, Hsinchu 30010, Taiwan |
| authorships[0].institutions[0].id | https://openalex.org/I148366613 |
| authorships[0].institutions[0].ror | https://ror.org/00se2k293 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I148366613 |
| authorships[0].institutions[0].country_code | TW |
| authorships[0].institutions[0].display_name | National Yang Ming Chiao Tung University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chang Francis Hsu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Electrophyics, National Chiao Tung University, Hsinchu 30010, Taiwan |
| authorships[1].author.id | https://openalex.org/A5103575517 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Long Hsu |
| authorships[1].countries | TW |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I148366613 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Electrophyics, National Chiao Tung University, Hsinchu 30010, Taiwan |
| authorships[1].institutions[0].id | https://openalex.org/I148366613 |
| authorships[1].institutions[0].ror | https://ror.org/00se2k293 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I148366613 |
| authorships[1].institutions[0].country_code | TW |
| authorships[1].institutions[0].display_name | National Yang Ming Chiao Tung University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Long Hsu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Electrophyics, National Chiao Tung University, Hsinchu 30010, Taiwan |
| authorships[2].author.id | https://openalex.org/A5107005626 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8674-1963 |
| authorships[2].author.display_name | Sien Chi |
| authorships[2].countries | TW |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I148366613 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Photonics, National Chiao Tung University, Hsinchu 30010, Taiwan |
| authorships[2].institutions[0].id | https://openalex.org/I148366613 |
| authorships[2].institutions[0].ror | https://ror.org/00se2k293 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I148366613 |
| authorships[2].institutions[0].country_code | TW |
| authorships[2].institutions[0].display_name | National Yang Ming Chiao Tung University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Sien Chi |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Department of Photonics, National Chiao Tung University, Hsinchu 30010, Taiwan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2020-10-15T00:00:00 |
| display_name | Complexity and Disorder of 1/fα Noises |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11270 |
| primary_topic.field.id | https://openalex.org/fields/20 |
| primary_topic.field.display_name | Economics, Econometrics and Finance |
| primary_topic.score | 0.9975000023841858 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2002 |
| primary_topic.subfield.display_name | Economics and Econometrics |
| primary_topic.display_name | Complex Systems and Time Series Analysis |
| related_works | https://openalex.org/W4380682190, https://openalex.org/W4315701745, https://openalex.org/W1990290471, https://openalex.org/W2945307361, https://openalex.org/W2102386043, https://openalex.org/W2005710836, https://openalex.org/W2003813852, https://openalex.org/W2116636209, https://openalex.org/W4366779185, https://openalex.org/W2146950613 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/e22101127 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S195231649 |
| best_oa_location.source.issn | 1099-4300 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1099-4300 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Entropy |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Entropy |
| best_oa_location.landing_page_url | https://doi.org/10.3390/e22101127 |
| primary_location.id | doi:10.3390/e22101127 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S195231649 |
| primary_location.source.issn | 1099-4300 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1099-4300 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Entropy |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1099-4300/22/10/1127/pdf?version=1603177553 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Entropy |
| primary_location.landing_page_url | https://doi.org/10.3390/e22101127 |
| publication_date | 2020-10-04 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W4388331996, https://openalex.org/W2078940173, https://openalex.org/W2093266575, https://openalex.org/W6649829654, https://openalex.org/W2072388473, https://openalex.org/W2765520681, https://openalex.org/W2946484517, https://openalex.org/W3124758257, https://openalex.org/W1975401035, https://openalex.org/W1997644642, https://openalex.org/W2213795671, https://openalex.org/W2033180999, https://openalex.org/W2100540110, https://openalex.org/W2164877040, https://openalex.org/W2001619934, https://openalex.org/W2170505850, https://openalex.org/W2221831393, https://openalex.org/W2043559349, https://openalex.org/W1996410571, https://openalex.org/W1585976120, https://openalex.org/W1999320110, https://openalex.org/W2982375095, https://openalex.org/W3164135786 |
| referenced_works_count | 23 |
| abstract_inverted_index.0 | 135 |
| abstract_inverted_index.1 | 118 |
| abstract_inverted_index.= | 117, 134 |
| abstract_inverted_index.U | 44 |
| abstract_inverted_index.a | 6, 30, 41, 66, 71, 75 |
| abstract_inverted_index.AE | 27, 58 |
| abstract_inverted_index.AF | 140 |
| abstract_inverted_index.RR | 106, 125, 141, 168 |
| abstract_inverted_index.as | 57, 84 |
| abstract_inverted_index.at | 115, 132 |
| abstract_inverted_index.by | 13 |
| abstract_inverted_index.is | 64, 119, 136, 163, 179 |
| abstract_inverted_index.it | 178 |
| abstract_inverted_index.of | 5, 15, 29, 32, 35, 38, 68, 109, 171 |
| abstract_inverted_index.on | 147, 156 |
| abstract_inverted_index.to | 104, 121 |
| abstract_inverted_index.we | 50, 91 |
| abstract_inverted_index.α | 39, 54, 63, 99, 116, 133 |
| abstract_inverted_index.1.5 | 101 |
| abstract_inverted_index.AE, | 148 |
| abstract_inverted_index.AE. | 89 |
| abstract_inverted_index.EoE | 25 |
| abstract_inverted_index.For | 46 |
| abstract_inverted_index.The | 0, 23, 112, 159 |
| abstract_inverted_index.and | 2, 18, 74 |
| abstract_inverted_index.are | 11, 81, 102, 149 |
| abstract_inverted_index.for | 98 |
| abstract_inverted_index.the | 3, 47, 87, 95, 105, 129, 152, 167 |
| abstract_inverted_index.vs. | 26 |
| abstract_inverted_index.α, | 176 |
| abstract_inverted_index.α. | 184 |
| abstract_inverted_index.(AF) | 124 |
| abstract_inverted_index.(RR) | 78 |
| abstract_inverted_index.0.73 | 180 |
| abstract_inverted_index.0.93 | 164 |
| abstract_inverted_index.also | 65 |
| abstract_inverted_index.from | 151 |
| abstract_inverted_index.have | 51, 86, 92 |
| abstract_inverted_index.more | 137 |
| abstract_inverted_index.ones | 154 |
| abstract_inverted_index.pink | 113 |
| abstract_inverted_index.plot | 28 |
| abstract_inverted_index.same | 88 |
| abstract_inverted_index.than | 139 |
| abstract_inverted_index.that | 53, 62, 94 |
| abstract_inverted_index.they | 85 |
| abstract_inverted_index.time | 9 |
| abstract_inverted_index.when | 165, 181 |
| abstract_inverted_index.(AE), | 21 |
| abstract_inverted_index.(EoE) | 17 |
| abstract_inverted_index.1/fα | 7, 33, 48, 72, 96 |
| abstract_inverted_index.These | 144 |
| abstract_inverted_index.based | 146, 155 |
| abstract_inverted_index.found | 93 |
| abstract_inverted_index.noise | 8, 73, 114, 131 |
| abstract_inverted_index.shown | 52 |
| abstract_inverted_index.three | 172 |
| abstract_inverted_index.using | 174, 182 |
| abstract_inverted_index.which | 60 |
| abstract_inverted_index.while | 128, 177 |
| abstract_inverted_index.white | 130 |
| abstract_inverted_index.around | 100 |
| abstract_inverted_index.atrial | 122 |
| abstract_inverted_index.curve. | 45 |
| abstract_inverted_index.groups | 173 |
| abstract_inverted_index.noises | 34, 97 |
| abstract_inverted_index.series | 10, 31, 80, 108, 127, 170 |
| abstract_inverted_index.values | 37 |
| abstract_inverted_index.F-score | 162 |
| abstract_inverted_index.average | 19 |
| abstract_inverted_index.cardiac | 76 |
| abstract_inverted_index.entropy | 14, 16, 20 |
| abstract_inverted_index.healthy | 110 |
| abstract_inverted_index.measure | 67 |
| abstract_inverted_index.noises, | 49 |
| abstract_inverted_index.series. | 143 |
| abstract_inverted_index.testing | 160 |
| abstract_inverted_index.various | 36 |
| abstract_inverted_index.AE-based | 175 |
| abstract_inverted_index.disorder | 4 |
| abstract_inverted_index.distinct | 42 |
| abstract_inverted_index.exhibits | 40 |
| abstract_inverted_index.interval | 79, 107, 126, 142, 169 |
| abstract_inverted_index.inverted | 43 |
| abstract_inverted_index.previous | 153 |
| abstract_inverted_index.results, | 145 |
| abstract_inverted_index.spectral | 157 |
| abstract_inverted_index.analysis. | 158 |
| abstract_inverted_index.decreases | 55 |
| abstract_inverted_index.different | 150 |
| abstract_inverted_index.disorder. | 69 |
| abstract_inverted_index.indicates | 61 |
| abstract_inverted_index.interbeat | 77 |
| abstract_inverted_index.resulting | 24 |
| abstract_inverted_index.subjects. | 111 |
| abstract_inverted_index.complexity | 1 |
| abstract_inverted_index.considered | 82 |
| abstract_inverted_index.disordered | 138 |
| abstract_inverted_index.equivalent | 83, 103, 120 |
| abstract_inverted_index.increases, | 59 |
| abstract_inverted_index.quantified | 12 |
| abstract_inverted_index.classifying | 166 |
| abstract_inverted_index.Accordingly, | 90 |
| abstract_inverted_index.Furthermore, | 70 |
| abstract_inverted_index.fibrillation | 123 |
| abstract_inverted_index.macro-average | 161 |
| abstract_inverted_index.monotonically | 56 |
| abstract_inverted_index.respectively. | 22 |
| abstract_inverted_index.spectral-analysis-based | 183 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5107005626 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I148366613 |
| citation_normalized_percentile.value | 0.8299532 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |