Development of a Bed-Based Unconstrained Cardiac Auscultation Method Article Swipe
Keita Nishio
,
Takashi Kaburagi
,
Yuri Hamada
,
Yosuke Kurihara
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/lsens.2021.3096116
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/lsens.2021.3096116
In this letter, we propose a bed-based unconstrained cardiac auscultation method to obtain heart sounds while a person is lying on a bed. In the proposed method, a sensing device with a resonance frequency in the frequency range of heart sounds is designed to improve the decreased gain of heart sounds through propagation in the bed mattress. In addition, the Wiener filter is applied to reduce the noise component contained in the measurement signals obtained by the proposed device. We performed a validation experiment to evaluate whether the proposed method correctly measures heart sounds by comparing the reference heart sounds measured by a stethoscope and those by the proposed device under a combination of conditions with different bed mattress thicknesses, sleeping postures, and respiratory states. The accuracy is evaluated by the correlation coefficient of the spectrum of heart sounds between the proposed method and the stethoscope. The experimental results demonstrate that the proposed unconstrained cardiac auscultation method can measure heart sounds with a correlation coefficient of 0.75 ± 0.12 on average.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lsens.2021.3096116
- https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdf
- OA Status
- bronze
- Cited By
- 8
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3178018046
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3178018046Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/lsens.2021.3096116Digital Object Identifier
- Title
-
Development of a Bed-Based Unconstrained Cardiac Auscultation MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-09Full publication date if available
- Authors
-
Keita Nishio, Takashi Kaburagi, Yuri Hamada, Yosuke KuriharaList of authors in order
- Landing page
-
https://doi.org/10.1109/lsens.2021.3096116Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdfDirect OA link when available
- Concepts
-
Auscultation, Stethoscope, Heart sounds, Correlation coefficient, Acoustics, Noise (video), Computer science, Speech recognition, Artificial intelligence, Medicine, Cardiology, Physics, Machine learning, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1, 2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3178018046 |
|---|---|
| doi | https://doi.org/10.1109/lsens.2021.3096116 |
| ids.doi | https://doi.org/10.1109/lsens.2021.3096116 |
| ids.mag | 3178018046 |
| ids.openalex | https://openalex.org/W3178018046 |
| fwci | 0.88436621 |
| type | article |
| title | Development of a Bed-Based Unconstrained Cardiac Auscultation Method |
| biblio.issue | 8 |
| biblio.volume | 5 |
| biblio.last_page | 4 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T12419 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2740 |
| topics[0].subfield.display_name | Pulmonary and Respiratory Medicine |
| topics[0].display_name | Phonocardiography and Auscultation Techniques |
| topics[1].id | https://openalex.org/T11021 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9484999775886536 |
| 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 | ECG Monitoring and Analysis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2777324038 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8318954706192017 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q779054 |
| concepts[0].display_name | Auscultation |
| concepts[1].id | https://openalex.org/C2779055095 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8207639455795288 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q162339 |
| concepts[1].display_name | Stethoscope |
| concepts[2].id | https://openalex.org/C2779435589 |
| concepts[2].level | 2 |
| concepts[2].score | 0.738446831703186 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q967103 |
| concepts[2].display_name | Heart sounds |
| concepts[3].id | https://openalex.org/C2780092901 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5470779538154602 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3433612 |
| concepts[3].display_name | Correlation coefficient |
| concepts[4].id | https://openalex.org/C24890656 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5396883487701416 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[4].display_name | Acoustics |
| concepts[5].id | https://openalex.org/C99498987 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5017778873443604 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[5].display_name | Noise (video) |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.39996498823165894 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C28490314 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3881447911262512 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[7].display_name | Speech recognition |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.22428038716316223 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C71924100 |
| concepts[9].level | 0 |
| concepts[9].score | 0.21795138716697693 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[9].display_name | Medicine |
| concepts[10].id | https://openalex.org/C164705383 |
| concepts[10].level | 1 |
| concepts[10].score | 0.1562403440475464 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q10379 |
| concepts[10].display_name | Cardiology |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.108239084482193 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C119857082 |
| concepts[12].level | 1 |
| concepts[12].score | 0.06865650415420532 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[12].display_name | Machine learning |
| concepts[13].id | https://openalex.org/C115961682 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[13].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/auscultation |
| keywords[0].score | 0.8318954706192017 |
| keywords[0].display_name | Auscultation |
| keywords[1].id | https://openalex.org/keywords/stethoscope |
| keywords[1].score | 0.8207639455795288 |
| keywords[1].display_name | Stethoscope |
| keywords[2].id | https://openalex.org/keywords/heart-sounds |
| keywords[2].score | 0.738446831703186 |
| keywords[2].display_name | Heart sounds |
| keywords[3].id | https://openalex.org/keywords/correlation-coefficient |
| keywords[3].score | 0.5470779538154602 |
| keywords[3].display_name | Correlation coefficient |
| keywords[4].id | https://openalex.org/keywords/acoustics |
| keywords[4].score | 0.5396883487701416 |
| keywords[4].display_name | Acoustics |
| keywords[5].id | https://openalex.org/keywords/noise |
| keywords[5].score | 0.5017778873443604 |
| keywords[5].display_name | Noise (video) |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.39996498823165894 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/speech-recognition |
| keywords[7].score | 0.3881447911262512 |
| keywords[7].display_name | Speech recognition |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.22428038716316223 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/medicine |
| keywords[9].score | 0.21795138716697693 |
| keywords[9].display_name | Medicine |
| keywords[10].id | https://openalex.org/keywords/cardiology |
| keywords[10].score | 0.1562403440475464 |
| keywords[10].display_name | Cardiology |
| keywords[11].id | https://openalex.org/keywords/physics |
| keywords[11].score | 0.108239084482193 |
| keywords[11].display_name | Physics |
| keywords[12].id | https://openalex.org/keywords/machine-learning |
| keywords[12].score | 0.06865650415420532 |
| keywords[12].display_name | Machine learning |
| language | en |
| locations[0].id | doi:10.1109/lsens.2021.3096116 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210220808 |
| locations[0].source.issn | 2475-1472 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2475-1472 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IEEE Sensors Letters |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | |
| locations[0].pdf_url | https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Sensors Letters |
| locations[0].landing_page_url | https://doi.org/10.1109/lsens.2021.3096116 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5032306216 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3917-8303 |
| authorships[0].author.display_name | Keita Nishio |
| authorships[0].countries | JP |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I131231118 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Industrial and Systems Engineering, Aoyama Gakuin University, Sagamihara, Japan |
| authorships[0].institutions[0].id | https://openalex.org/I131231118 |
| authorships[0].institutions[0].ror | https://ror.org/002rw7y37 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I131231118 |
| authorships[0].institutions[0].country_code | JP |
| authorships[0].institutions[0].display_name | Aoyama Gakuin University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Keita Nishio |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Industrial and Systems Engineering, Aoyama Gakuin University, Sagamihara, Japan |
| authorships[1].author.id | https://openalex.org/A5012646269 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7136-9831 |
| authorships[1].author.display_name | Takashi Kaburagi |
| authorships[1].countries | JP |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I199080787 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Natural Sciences, International Christian University, Tokyo, Japan |
| authorships[1].institutions[0].id | https://openalex.org/I199080787 |
| authorships[1].institutions[0].ror | https://ror.org/01cg0k189 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I199080787 |
| authorships[1].institutions[0].country_code | JP |
| authorships[1].institutions[0].display_name | International Christian University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Takashi Kaburagi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Natural Sciences, International Christian University, Tokyo, Japan |
| authorships[2].author.id | https://openalex.org/A5021956037 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1200-4530 |
| authorships[2].author.display_name | Yuri Hamada |
| authorships[2].countries | JP |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I131231118 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Industrial and Systems Engineering, Aoyama Gakuin University, Sagamihara, Japan |
| authorships[2].institutions[0].id | https://openalex.org/I131231118 |
| authorships[2].institutions[0].ror | https://ror.org/002rw7y37 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I131231118 |
| authorships[2].institutions[0].country_code | JP |
| authorships[2].institutions[0].display_name | Aoyama Gakuin University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yuri Hamada |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Industrial and Systems Engineering, Aoyama Gakuin University, Sagamihara, Japan |
| authorships[3].author.id | https://openalex.org/A5090187549 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4592-0807 |
| authorships[3].author.display_name | Yosuke Kurihara |
| authorships[3].countries | JP |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I131231118 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Industrial and Systems Engineering, Aoyama Gakuin University, Sagamihara, Japan |
| authorships[3].institutions[0].id | https://openalex.org/I131231118 |
| authorships[3].institutions[0].ror | https://ror.org/002rw7y37 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I131231118 |
| authorships[3].institutions[0].country_code | JP |
| authorships[3].institutions[0].display_name | Aoyama Gakuin University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Yosuke Kurihara |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Industrial and Systems Engineering, Aoyama Gakuin University, Sagamihara, Japan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdf |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Development of a Bed-Based Unconstrained Cardiac Auscultation Method |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12419 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2740 |
| primary_topic.subfield.display_name | Pulmonary and Respiratory Medicine |
| primary_topic.display_name | Phonocardiography and Auscultation Techniques |
| related_works | https://openalex.org/W1970711085, https://openalex.org/W4206320658, https://openalex.org/W1601748943, https://openalex.org/W2028865152, https://openalex.org/W4386897925, https://openalex.org/W1972438460, https://openalex.org/W2754177862, https://openalex.org/W2038266136, https://openalex.org/W1980130537, https://openalex.org/W3004587664 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1109/lsens.2021.3096116 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210220808 |
| best_oa_location.source.issn | 2475-1472 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2475-1472 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | IEEE Sensors Letters |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Sensors Letters |
| best_oa_location.landing_page_url | https://doi.org/10.1109/lsens.2021.3096116 |
| primary_location.id | doi:10.1109/lsens.2021.3096116 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210220808 |
| primary_location.source.issn | 2475-1472 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2475-1472 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE Sensors Letters |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/7782634/9495104/09479720.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Sensors Letters |
| primary_location.landing_page_url | https://doi.org/10.1109/lsens.2021.3096116 |
| publication_date | 2021-07-09 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W6790582025, https://openalex.org/W2948952071, https://openalex.org/W2994955424, https://openalex.org/W2765443025, https://openalex.org/W3129660341, https://openalex.org/W1582534817, https://openalex.org/W1976756294 |
| referenced_works_count | 7 |
| abstract_inverted_index.a | 5, 16, 21, 27, 31, 81, 102, 111, 162 |
| abstract_inverted_index.In | 0, 23, 57 |
| abstract_inverted_index.We | 79 |
| abstract_inverted_index.by | 75, 94, 101, 106, 129 |
| abstract_inverted_index.in | 34, 53, 70 |
| abstract_inverted_index.is | 18, 41, 62, 127 |
| abstract_inverted_index.of | 38, 48, 113, 133, 136, 165 |
| abstract_inverted_index.on | 20, 169 |
| abstract_inverted_index.to | 11, 43, 64, 84 |
| abstract_inverted_index.we | 3 |
| abstract_inverted_index.± | 167 |
| abstract_inverted_index.The | 125, 146 |
| abstract_inverted_index.and | 104, 122, 143 |
| abstract_inverted_index.bed | 55, 117 |
| abstract_inverted_index.can | 157 |
| abstract_inverted_index.the | 24, 35, 45, 54, 59, 66, 71, 76, 87, 96, 107, 130, 134, 140, 144, 151 |
| abstract_inverted_index.0.12 | 168 |
| abstract_inverted_index.0.75 | 166 |
| abstract_inverted_index.bed. | 22 |
| abstract_inverted_index.gain | 47 |
| abstract_inverted_index.that | 150 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.with | 30, 115, 161 |
| abstract_inverted_index.heart | 13, 39, 49, 92, 98, 137, 159 |
| abstract_inverted_index.lying | 19 |
| abstract_inverted_index.noise | 67 |
| abstract_inverted_index.range | 37 |
| abstract_inverted_index.those | 105 |
| abstract_inverted_index.under | 110 |
| abstract_inverted_index.while | 15 |
| abstract_inverted_index.Wiener | 60 |
| abstract_inverted_index.device | 29, 109 |
| abstract_inverted_index.filter | 61 |
| abstract_inverted_index.method | 10, 89, 142, 156 |
| abstract_inverted_index.obtain | 12 |
| abstract_inverted_index.person | 17 |
| abstract_inverted_index.reduce | 65 |
| abstract_inverted_index.sounds | 14, 40, 50, 93, 99, 138, 160 |
| abstract_inverted_index.applied | 63 |
| abstract_inverted_index.between | 139 |
| abstract_inverted_index.cardiac | 8, 154 |
| abstract_inverted_index.device. | 78 |
| abstract_inverted_index.improve | 44 |
| abstract_inverted_index.letter, | 2 |
| abstract_inverted_index.measure | 158 |
| abstract_inverted_index.method, | 26 |
| abstract_inverted_index.propose | 4 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.sensing | 28 |
| abstract_inverted_index.signals | 73 |
| abstract_inverted_index.states. | 124 |
| abstract_inverted_index.through | 51 |
| abstract_inverted_index.whether | 86 |
| abstract_inverted_index.accuracy | 126 |
| abstract_inverted_index.average. | 170 |
| abstract_inverted_index.designed | 42 |
| abstract_inverted_index.evaluate | 85 |
| abstract_inverted_index.mattress | 118 |
| abstract_inverted_index.measured | 100 |
| abstract_inverted_index.measures | 91 |
| abstract_inverted_index.obtained | 74 |
| abstract_inverted_index.proposed | 25, 77, 88, 108, 141, 152 |
| abstract_inverted_index.sleeping | 120 |
| abstract_inverted_index.spectrum | 135 |
| abstract_inverted_index.addition, | 58 |
| abstract_inverted_index.bed-based | 6 |
| abstract_inverted_index.comparing | 95 |
| abstract_inverted_index.component | 68 |
| abstract_inverted_index.contained | 69 |
| abstract_inverted_index.correctly | 90 |
| abstract_inverted_index.decreased | 46 |
| abstract_inverted_index.different | 116 |
| abstract_inverted_index.evaluated | 128 |
| abstract_inverted_index.frequency | 33, 36 |
| abstract_inverted_index.mattress. | 56 |
| abstract_inverted_index.performed | 80 |
| abstract_inverted_index.postures, | 121 |
| abstract_inverted_index.reference | 97 |
| abstract_inverted_index.resonance | 32 |
| abstract_inverted_index.conditions | 114 |
| abstract_inverted_index.experiment | 83 |
| abstract_inverted_index.validation | 82 |
| abstract_inverted_index.coefficient | 132, 164 |
| abstract_inverted_index.combination | 112 |
| abstract_inverted_index.correlation | 131, 163 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.measurement | 72 |
| abstract_inverted_index.propagation | 52 |
| abstract_inverted_index.respiratory | 123 |
| abstract_inverted_index.stethoscope | 103 |
| abstract_inverted_index.auscultation | 9, 155 |
| abstract_inverted_index.experimental | 147 |
| abstract_inverted_index.stethoscope. | 145 |
| abstract_inverted_index.thicknesses, | 119 |
| abstract_inverted_index.unconstrained | 7, 153 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.72651232 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |