Scalogram-energy based segmentation of surface electromyography signals from swallowing related muscles
•The swallowing process could be evaluated by non invasive methods such as the surface electromyography (sEMG).•Swallowing related muscles are hard to assess via sEMG due to small size, depth, and cross-talk that reduces the signal-to-noise ratio (SNR).•The Scalogram-Energy based Segmentation is sui...
Uložené v:
| Vydané v: | Computer methods and programs in biomedicine Ročník 194; s. 105480 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Ireland
Elsevier B.V
01.10.2020
|
| Predmet: | |
| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •The swallowing process could be evaluated by non invasive methods such as the surface electromyography (sEMG).•Swallowing related muscles are hard to assess via sEMG due to small size, depth, and cross-talk that reduces the signal-to-noise ratio (SNR).•The Scalogram-Energy based Segmentation is suitable to detect activations in swallowing related sEMG signals despite of the low SNR.•Automatic segmentation is helpful to assess the swallowing process.
Background and Objective: The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks.
Methods: The proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F1 score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators.
Results: The algorithm achieved a F1 score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F1 score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators.
Conclusions: The proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications. |
|---|---|
| AbstractList | •The swallowing process could be evaluated by non invasive methods such as the surface electromyography (sEMG).•Swallowing related muscles are hard to assess via sEMG due to small size, depth, and cross-talk that reduces the signal-to-noise ratio (SNR).•The Scalogram-Energy based Segmentation is suitable to detect activations in swallowing related sEMG signals despite of the low SNR.•Automatic segmentation is helpful to assess the swallowing process.
Background and Objective: The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks.
Methods: The proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F1 score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators.
Results: The algorithm achieved a F1 score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F1 score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators.
Conclusions: The proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications. The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks.BACKGROUND AND OBJECTIVEThe swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks.The proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F1 score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators.METHODSThe proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F1 score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators.The algorithm achieved a F1 score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F1 score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators.RESULTSThe algorithm achieved a F1 score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F1 score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators.The proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications.CONCLUSIONSThe proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications. The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks. The proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators. The algorithm achieved a F score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators. The proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications. |
| ArticleNumber | 105480 |
| Author | Estefania, Perez-Giraldo Andres, Orozco-Duque Sebastian, Roldan-Vasco |
| Author_xml | – sequence: 1 givenname: Roldan-Vasco surname: Sebastian fullname: Sebastian, Roldan-Vasco email: sebastianroldan@itm.edu.co organization: Grupo de Investigación en Materiales Avanzados y Energía, Facultad de Ingenierías, Instituto Tecnológico Metropolitano, Medellín, Colombia – sequence: 2 givenname: Perez-Giraldo surname: Estefania fullname: Estefania, Perez-Giraldo organization: Grupo de Investigación e Innovación Biomédica, Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano, Medellín, Colombia – sequence: 3 givenname: Orozco-Duque surname: Andres fullname: Andres, Orozco-Duque email: andresorozco@itm.edu.co organization: Grupo de Investigación e Innovación Biomédica, Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano, Medellín, Colombia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32403048$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkT1v1TAYhS3Uit4W_gAD8siSi-OPfCAWVEGLVKkDMFuO8zr44tgXO6HKv69D2qVDO1l69TxH8jnn6MQHDwi9K8m-JGX18bDX47HbU0LXg-ANeYV2ZVPTohaVOEG7DLUFrUh9hs5TOhBCqBDVa3TGKCeM8GaHhh9auTBENRbgIQ4L7lSCHicYRvCTmmzwOBic5miUBgwO9BTDuKzO8feCkx28cgmbfMTpTjkX7qwfcASnphw0zkk7SG_QqckYvH14L9Cvb19_Xl4XN7dX3y-_3BSak3oqGg6sr2sgmildk5YaVjJa8lLkawu8rbpKc-hFR3tqetKKWhjOTG-4ZoZqdoE-bLnHGP7OkCY52qTBOeUhzEn-_zhrG0Ey-v4BnbsRenmMdlRxkY_lZKDZAB1DShGM1HZrZIrKOlkSue4gD3LdQa47yG2HrNIn6mP6s9LnTYJc0D8LUSZtwWvobcytyz7Y5_VPT3TtrLd53z-wvCTfA6QEtgk |
| CitedBy_id | crossref_primary_10_3390_s23073594 crossref_primary_10_1016_j_bspc_2024_107030 crossref_primary_10_1016_j_dsp_2022_103815 crossref_primary_10_3390_s22124513 crossref_primary_10_3390_app13020923 crossref_primary_10_1016_j_cmpb_2022_107058 crossref_primary_10_1016_j_bspc_2021_103122 crossref_primary_10_1016_j_bspc_2021_103201 |
| Cites_doi | 10.1109/TBME.2003.808829 10.1123/jab.13.2.135 10.1186/s40648-016-0048-0 10.1016/j.otc.2013.09.006 10.1016/j.bspc.2016.12.014 10.1109/10.661154 10.1016/j.jbiomech.2015.02.017 10.1016/j.jelekin.2019.06.010 10.1016/j.jelekin.2018.10.004 10.1016/j.jelekin.2017.05.001 10.1186/1475-925X-14-3 10.1007/s00455-014-9578-x 10.1016/S1388-2457(03)00237-2 10.1109/10.923782 10.1016/j.jelekin.2010.02.007 10.1016/j.jelekin.2015.07.009 10.1016/j.medengphy.2014.09.008 10.1109/TNSRE.2012.2226916 10.1044/1092-4388(2011/11-0214) 10.1093/ptj/67.1.43 10.1016/j.jelekin.2012.04.010 10.1016/j.ress.2005.11.018 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier B.V. Copyright © 2020 Elsevier B.V. All rights reserved. |
| Copyright_xml | – notice: 2020 Elsevier B.V. – notice: Copyright © 2020 Elsevier B.V. All rights reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1016/j.cmpb.2020.105480 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1872-7565 |
| ExternalDocumentID | 32403048 10_1016_j_cmpb_2020_105480 S0169260719323223 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M -~X .1- .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5RE 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HMK HMO HVGLF HZ~ IHE J1W KOM LG9 M29 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAE SBC SDF SDG SEL SES SEW SPC SPCBC SSH SSV SSZ T5K UHS WUQ XPP Z5R ZGI ZY4 ~G- ~HD AACTN AAIAV ABLVK ABTAH ABYKQ AFKWA AJBFU AJOXV AMFUW LCYCR RIG 9DU AAYXX CITATION AFCTW CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c407t-84e3d77e0c3ac7092f31321415d779e496b6c4ed5b2d2fd09575f43fdf4c3f2c3 |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000557906500016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0169-2607 1872-7565 |
| IngestDate | Sun Sep 28 02:03:36 EDT 2025 Wed Feb 19 02:29:42 EST 2025 Sat Nov 29 07:23:44 EST 2025 Tue Nov 18 22:08:41 EST 2025 Fri Feb 23 02:46:43 EST 2024 Tue Oct 14 19:32:55 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | sEMG Detection algorithm Surface electromyography Wavelet transform Segmentation Swallowing |
| Language | English |
| License | Copyright © 2020 Elsevier B.V. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c407t-84e3d77e0c3ac7092f31321415d779e496b6c4ed5b2d2fd09575f43fdf4c3f2c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 32403048 |
| PQID | 2403039850 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2403039850 pubmed_primary_32403048 crossref_citationtrail_10_1016_j_cmpb_2020_105480 crossref_primary_10_1016_j_cmpb_2020_105480 elsevier_sciencedirect_doi_10_1016_j_cmpb_2020_105480 elsevier_clinicalkey_doi_10_1016_j_cmpb_2020_105480 |
| PublicationCentury | 2000 |
| PublicationDate | October 2020 2020-10-00 2020-Oct 20201001 |
| PublicationDateYYYYMMDD | 2020-10-01 |
| PublicationDate_xml | – month: 10 year: 2020 text: October 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Ireland |
| PublicationPlace_xml | – name: Ireland |
| PublicationTitle | Computer methods and programs in biomedicine |
| PublicationTitleAlternate | Comput Methods Programs Biomed |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Bonato, D’Alessio, Knaflitz (bib0013) 1998; 45 Merlo, Farina, Merletti (bib0017) 2003; 50 De Luca (bib0022) 1997; 13 Roldan-Vasco, Restrepo-Agudelo, Valencia-Martinez, Orozco-Duque (bib0005) 2018; 43 Liu, Ying, Rymer (bib0020) 2015; 48 Yang, Zhang, Gu, Liu (bib0024) 2017; 33 Shaw, Martino (bib0001) 2013; 46 Solnik, DeVita, Rider, Long, Hortobágyi (bib0021) 2008; 10 Sifrim, Vilardell, Clavé (bib0004) 2014; vol. 33 Ertekin, Aydogdu (bib0003) 2003; 114 Di Fabio (bib0015) 1987; 67 Rashid, Niazi, Signal, Farina, Taylor (bib0031) 2019 Poorjavad (bib0008) 2016 Konak, Coit, Smith (bib0029) 2006; 91 Severini, Conforto, Schmid, Dalessio (bib0016) 2012; 22 Dudik, Jestrović, Luan, Coyle, Sejdić (bib0025) 2015; 14 Stepp (bib0007) 2012; 55 Carter, Gutierrez (bib0002) 2015; 25 Maria, Balata, Justino, Juliana, Moraes, Pernambuco, Regina, Moraes (bib0010) 2013; 17 Steele, Alsanei, Ayanikalath, Barbon, Chen, Cichero, Coutts, Dantas, Duivestein, Giosa, Hanson, Lam, Lecko, Leigh, Nagy, Namasivayam, Nascimento, Odendaal, Smith, Wang (bib0011) 2015; 30 Farina, Merletti (bib0030) 2001; 48 Cadavid-Arboleda, Ramirez-Arbelaez, Perez-Giraldo, Restrepo-Agudelo, Roldan-Vasco, Suarez-Escudero, Cantillo-Mackenzie, Bedoya-Londono, Martinez-Moreno, Orozco-Duque (bib0026) 2017 Sasaki, Onishi, Stefanov, Kamata, Nakayama, Yoshikawa, Obinata (bib0009) 2016; 3 Xu, Quan, Yang, He (bib0018) 2013; 21 Merletti, Di Torino (bib0027) 1999; 9 Restrepo-Agudelo, Roldan-Vasco, Ramirez-Arbelaez, Cadavid-Arboleda, Perez-Giraldo, Orozco-Duque (bib0006) 2017; 35 Roldan-Vasco, Perez-Giraldo, Orozco-Duque (bib0023) 2018 Vannozzi, Conforto, D’Alessio (bib0012) 2010; 20 Staude, Flachenecker, Daumer, Wolf (bib0014) 2001; 2 Liu, Ying, Zhou (bib0028) 2014; 36 Naseem, Jabloun, Buttelli, Ravier (bib0019) 2016 De Luca (10.1016/j.cmpb.2020.105480_bib0022) 1997; 13 Naseem (10.1016/j.cmpb.2020.105480_bib0019) 2016 Restrepo-Agudelo (10.1016/j.cmpb.2020.105480_bib0006) 2017; 35 Vannozzi (10.1016/j.cmpb.2020.105480_bib0012) 2010; 20 Solnik (10.1016/j.cmpb.2020.105480_bib0021) 2008; 10 Shaw (10.1016/j.cmpb.2020.105480_bib0001) 2013; 46 Ertekin (10.1016/j.cmpb.2020.105480_bib0003) 2003; 114 Roldan-Vasco (10.1016/j.cmpb.2020.105480_bib0005) 2018; 43 Cadavid-Arboleda (10.1016/j.cmpb.2020.105480_bib0026) 2017 Poorjavad (10.1016/j.cmpb.2020.105480_bib0008) 2016 Bonato (10.1016/j.cmpb.2020.105480_bib0013) 1998; 45 Merletti (10.1016/j.cmpb.2020.105480_bib0027) 1999; 9 Xu (10.1016/j.cmpb.2020.105480_bib0018) 2013; 21 Roldan-Vasco (10.1016/j.cmpb.2020.105480_bib0023) 2018 Maria (10.1016/j.cmpb.2020.105480_bib0010) 2013; 17 Sasaki (10.1016/j.cmpb.2020.105480_bib0009) 2016; 3 Merlo (10.1016/j.cmpb.2020.105480_bib0017) 2003; 50 Farina (10.1016/j.cmpb.2020.105480_bib0030) 2001; 48 Liu (10.1016/j.cmpb.2020.105480_bib0020) 2015; 48 Steele (10.1016/j.cmpb.2020.105480_bib0011) 2015; 30 Di Fabio (10.1016/j.cmpb.2020.105480_bib0015) 1987; 67 Severini (10.1016/j.cmpb.2020.105480_bib0016) 2012; 22 Yang (10.1016/j.cmpb.2020.105480_bib0024) 2017; 33 Liu (10.1016/j.cmpb.2020.105480_bib0028) 2014; 36 Sifrim (10.1016/j.cmpb.2020.105480_bib0004) 2014; vol. 33 Konak (10.1016/j.cmpb.2020.105480_bib0029) 2006; 91 Rashid (10.1016/j.cmpb.2020.105480_bib0031) 2019 Stepp (10.1016/j.cmpb.2020.105480_bib0007) 2012; 55 Carter (10.1016/j.cmpb.2020.105480_bib0002) 2015; 25 Staude (10.1016/j.cmpb.2020.105480_bib0014) 2001; 2 Dudik (10.1016/j.cmpb.2020.105480_bib0025) 2015; 14 |
| References_xml | – volume: 36 start-page: 1711 year: 2014 end-page: 1715 ident: bib0028 article-title: Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients publication-title: Med. Eng. Phys. – volume: 17 start-page: 329 year: 2013 end-page: 339 ident: bib0010 article-title: Use of surface electromyography in phonation studies: an integrative review publication-title: Int. Arch. Otorhinolaryngol. – volume: 91 start-page: 992 year: 2006 end-page: 1007 ident: bib0029 article-title: Multi-objective optimization using genetic algorithms: a tutorial publication-title: Reliab. Eng. Syst. Saf. – volume: 43 start-page: 193 year: 2018 end-page: 200 ident: bib0005 article-title: Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG publication-title: J. Electromyogr. Kinesiol. – volume: 9 start-page: 3 year: 1999 end-page: 4 ident: bib0027 article-title: Standards for reporting EMG data publication-title: J. Electromyogr. Kinesiol. – volume: 10 start-page: 65 year: 2008 ident: bib0021 article-title: Teager–Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio publication-title: Acta Bioeng. Biomech. – volume: 20 start-page: 767 year: 2010 end-page: 772 ident: bib0012 article-title: Automatic detection of surface EMG activation timing using a wavelet transform based method publication-title: J. Electromyogr. Kinesiol. – volume: vol. 33 start-page: 1 year: 2014 end-page: 13 ident: bib0004 article-title: Oropharyngeal dysphagia and swallowing dysfunction publication-title: Functional and GI Motility Disorders – volume: 114 start-page: 2226 year: 2003 end-page: 2244 ident: bib0003 article-title: Neurophysiology of swallowing publication-title: Clin. Neurophys. – volume: 22 start-page: 878 year: 2012 end-page: 885 ident: bib0016 article-title: Novel formulation of a double threshold algorithm for the estimation of muscle activation intervals designed for variable SNR environments publication-title: J. Electromyogr. Kinesiol. – volume: 55 start-page: 1232 year: 2012 end-page: 1247 ident: bib0007 article-title: Surface electromyography for speech and swallowing systems: measurement, analysis, and interpretation publication-title: J. Speech Lang. Hear. Res. – volume: 13 start-page: 135 year: 1997 end-page: 163 ident: bib0022 article-title: The use of surface electromyography in biomechanics publication-title: J. Appl. Biomech. – volume: 35 start-page: 1 year: 2017 end-page: 8 ident: bib0006 article-title: Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis publication-title: J. Electromyogr. Kinesiol. – volume: 25 start-page: 731 year: 2015 end-page: 741 ident: bib0002 article-title: The concurrent validity of three computerized methods of muscle activity onset detection publication-title: J. Electromyogr. Kinesiol. – volume: 45 start-page: 287 year: 1998 end-page: 299 ident: bib0013 article-title: A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait publication-title: IEEE Trans. Biomed. Eng. – volume: 33 start-page: 306 year: 2017 end-page: 315 ident: bib0024 article-title: Accurate EMG onset detection in pathological, weak and noisy myoelectric signals publication-title: Biomed. Signal Process. Control – volume: 48 start-page: 1193 year: 2015 end-page: 1197 ident: bib0020 article-title: EMG burst presence probability: a joint time–frequency representation of muscle activity and its application to onset detection publication-title: J. Biomech. – year: 2019 ident: bib0031 article-title: Optimal automatic detection of muscle activation intervals publication-title: J. Electromyogr. Kinesiol. – start-page: 245 year: 2018 end-page: 255 ident: bib0023 article-title: Continuous wavelet transform for muscle activity detection in surface EMG signals during swallowing publication-title: Workshop on Engineering Applications – year: 2016 ident: bib0008 article-title: Surface electromyographic assessment of swallowing function publication-title: Iran J. Med. Sci. – volume: 3 start-page: 9 year: 2016 ident: bib0009 article-title: Tongue interface based on surface EMG signals of suprahyoid muscles publication-title: ROBOMECH J. – volume: 46 start-page: 937 year: 2013 end-page: 956 ident: bib0001 article-title: The normal swallow: muscular and neurophysiological control. publication-title: Otolaryngol. Clin. North Am. – volume: 50 start-page: 316 year: 2003 end-page: 323 ident: bib0017 article-title: A fast and reliable technique for muscle activity detection from surface EMG signals publication-title: IEEE Trans. Biomed. Eng. – start-page: 165 year: 2017 end-page: 168 ident: bib0026 article-title: Assessment of surface electromyography during orofacial praxis in healthy subjects publication-title: VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th-28th, 2016 – start-page: 1713 year: 2016 end-page: 1717 ident: bib0019 article-title: Detection of sEMG muscle activation intervals using gaussian mixture model and ant colony classifier publication-title: Signal Processing Conference (EUSIPCO), 2016 24th European – volume: 48 start-page: 637 year: 2001 end-page: 646 ident: bib0030 article-title: A novel approach for precise simulation of the EMG signal detected by surface electrodes publication-title: IEEE Trans. Biomed. Eng. – volume: 67 start-page: 43 year: 1987 end-page: 48 ident: bib0015 article-title: Reliability of computerized surface electromyography for determining the onsent of muscle activity publication-title: Phys. Ther. – volume: 21 start-page: 65 year: 2013 end-page: 73 ident: bib0018 article-title: An adaptive algorithm for the determination of the onset and offset of muscle contraction by EMG signal processing publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 14 start-page: 3 year: 2015 ident: bib0025 article-title: A comparative analysis of swallowing accelerometry and sounds during saliva swallows publication-title: Biomed. Eng. Online – volume: 30 start-page: 2 year: 2015 end-page: 26 ident: bib0011 article-title: The influence of food texture and liquid consistency modification on swallowing physiology and function: a Systematic review publication-title: Dysphagia – volume: 2 start-page: 67 year: 2001 end-page: 81 ident: bib0014 article-title: Onset detection in surface electromyographic signals: publication-title: J. Appl. Signal Process. – volume: 50 start-page: 316 issue: 3 year: 2003 ident: 10.1016/j.cmpb.2020.105480_bib0017 article-title: A fast and reliable technique for muscle activity detection from surface EMG signals publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2003.808829 – volume: 10 start-page: 65 issue: 2 year: 2008 ident: 10.1016/j.cmpb.2020.105480_bib0021 article-title: Teager–Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio publication-title: Acta Bioeng. Biomech. – volume: 13 start-page: 135 issue: 2 year: 1997 ident: 10.1016/j.cmpb.2020.105480_bib0022 article-title: The use of surface electromyography in biomechanics publication-title: J. Appl. Biomech. doi: 10.1123/jab.13.2.135 – year: 2016 ident: 10.1016/j.cmpb.2020.105480_bib0008 article-title: Surface electromyographic assessment of swallowing function publication-title: Iran J. Med. Sci. – volume: 3 start-page: 9 issue: 1 year: 2016 ident: 10.1016/j.cmpb.2020.105480_bib0009 article-title: Tongue interface based on surface EMG signals of suprahyoid muscles publication-title: ROBOMECH J. doi: 10.1186/s40648-016-0048-0 – volume: 46 start-page: 937 issue: 6 year: 2013 ident: 10.1016/j.cmpb.2020.105480_bib0001 article-title: The normal swallow: muscular and neurophysiological control. publication-title: Otolaryngol. Clin. North Am. doi: 10.1016/j.otc.2013.09.006 – start-page: 1713 year: 2016 ident: 10.1016/j.cmpb.2020.105480_bib0019 article-title: Detection of sEMG muscle activation intervals using gaussian mixture model and ant colony classifier – volume: 33 start-page: 306 year: 2017 ident: 10.1016/j.cmpb.2020.105480_bib0024 article-title: Accurate EMG onset detection in pathological, weak and noisy myoelectric signals publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2016.12.014 – volume: 17 start-page: 329 issue: 3 year: 2013 ident: 10.1016/j.cmpb.2020.105480_bib0010 article-title: Use of surface electromyography in phonation studies: an integrative review publication-title: Int. Arch. Otorhinolaryngol. – volume: 45 start-page: 287 issue: 3 year: 1998 ident: 10.1016/j.cmpb.2020.105480_bib0013 article-title: A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.661154 – volume: 2 start-page: 67 year: 2001 ident: 10.1016/j.cmpb.2020.105480_bib0014 article-title: Onset detection in surface electromyographic signals: publication-title: J. Appl. Signal Process. – volume: 48 start-page: 1193 issue: 6 year: 2015 ident: 10.1016/j.cmpb.2020.105480_bib0020 article-title: EMG burst presence probability: a joint time–frequency representation of muscle activity and its application to onset detection publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2015.02.017 – year: 2019 ident: 10.1016/j.cmpb.2020.105480_bib0031 article-title: Optimal automatic detection of muscle activation intervals publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2019.06.010 – volume: 9 start-page: 3 issue: 1 year: 1999 ident: 10.1016/j.cmpb.2020.105480_bib0027 article-title: Standards for reporting EMG data publication-title: J. Electromyogr. Kinesiol. – volume: 43 start-page: 193 year: 2018 ident: 10.1016/j.cmpb.2020.105480_bib0005 article-title: Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2018.10.004 – volume: 35 start-page: 1 year: 2017 ident: 10.1016/j.cmpb.2020.105480_bib0006 article-title: Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2017.05.001 – volume: 14 start-page: 3 issue: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105480_bib0025 article-title: A comparative analysis of swallowing accelerometry and sounds during saliva swallows publication-title: Biomed. Eng. Online doi: 10.1186/1475-925X-14-3 – volume: 30 start-page: 2 issue: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105480_bib0011 article-title: The influence of food texture and liquid consistency modification on swallowing physiology and function: a Systematic review publication-title: Dysphagia doi: 10.1007/s00455-014-9578-x – volume: 114 start-page: 2226 issue: 12 year: 2003 ident: 10.1016/j.cmpb.2020.105480_bib0003 article-title: Neurophysiology of swallowing publication-title: Clin. Neurophys. doi: 10.1016/S1388-2457(03)00237-2 – volume: 48 start-page: 637 issue: 6 year: 2001 ident: 10.1016/j.cmpb.2020.105480_bib0030 article-title: A novel approach for precise simulation of the EMG signal detected by surface electrodes publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.923782 – volume: 20 start-page: 767 issue: 4 year: 2010 ident: 10.1016/j.cmpb.2020.105480_bib0012 article-title: Automatic detection of surface EMG activation timing using a wavelet transform based method publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2010.02.007 – volume: 25 start-page: 731 issue: 5 year: 2015 ident: 10.1016/j.cmpb.2020.105480_bib0002 article-title: The concurrent validity of three computerized methods of muscle activity onset detection publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2015.07.009 – start-page: 165 year: 2017 ident: 10.1016/j.cmpb.2020.105480_bib0026 article-title: Assessment of surface electromyography during orofacial praxis in healthy subjects – volume: 36 start-page: 1711 issue: 12 year: 2014 ident: 10.1016/j.cmpb.2020.105480_bib0028 article-title: Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2014.09.008 – volume: 21 start-page: 65 issue: 1 year: 2013 ident: 10.1016/j.cmpb.2020.105480_bib0018 article-title: An adaptive algorithm for the determination of the onset and offset of muscle contraction by EMG signal processing publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2012.2226916 – volume: vol. 33 start-page: 1 year: 2014 ident: 10.1016/j.cmpb.2020.105480_bib0004 article-title: Oropharyngeal dysphagia and swallowing dysfunction – volume: 55 start-page: 1232 issue: August year: 2012 ident: 10.1016/j.cmpb.2020.105480_bib0007 article-title: Surface electromyography for speech and swallowing systems: measurement, analysis, and interpretation publication-title: J. Speech Lang. Hear. Res. doi: 10.1044/1092-4388(2011/11-0214) – volume: 67 start-page: 43 issue: 1 year: 1987 ident: 10.1016/j.cmpb.2020.105480_bib0015 article-title: Reliability of computerized surface electromyography for determining the onsent of muscle activity publication-title: Phys. Ther. doi: 10.1093/ptj/67.1.43 – start-page: 245 year: 2018 ident: 10.1016/j.cmpb.2020.105480_bib0023 article-title: Continuous wavelet transform for muscle activity detection in surface EMG signals during swallowing – volume: 22 start-page: 878 issue: 6 year: 2012 ident: 10.1016/j.cmpb.2020.105480_bib0016 article-title: Novel formulation of a double threshold algorithm for the estimation of muscle activation intervals designed for variable SNR environments publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2012.04.010 – volume: 91 start-page: 992 issue: 9 year: 2006 ident: 10.1016/j.cmpb.2020.105480_bib0029 article-title: Multi-objective optimization using genetic algorithms: a tutorial publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2005.11.018 |
| SSID | ssj0002556 |
| Score | 2.3360465 |
| Snippet | •The swallowing process could be evaluated by non invasive methods such as the surface electromyography (sEMG).•Swallowing related muscles are hard to assess... The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles.... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 105480 |
| SubjectTerms | Algorithms Deglutition Detection algorithm Electromyography Humans Muscle, Skeletal Muscles Reproducibility of Results Segmentation sEMG Signal Processing, Computer-Assisted Signal-To-Noise Ratio Surface electromyography Swallowing Wavelet transform |
| Title | Scalogram-energy based segmentation of surface electromyography signals from swallowing related muscles |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0169260719323223 https://dx.doi.org/10.1016/j.cmpb.2020.105480 https://www.ncbi.nlm.nih.gov/pubmed/32403048 https://www.proquest.com/docview/2403039850 |
| Volume | 194 |
| WOSCitedRecordID | wos000557906500016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7565 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002556 issn: 0169-2607 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Ja9tAFB4cp4RcQtc0XcIUejMyWjOjY2jTDZoGkhbfxGj0FBIsyVh2tp_RX9w3m-I6OG0PvQgjzYyEv09vvhm9hZC3QnKQXEhPQBl5qG99tIOQe1wEgYC0UDnedLEJdnjIR6P0qNf76WJhLsasrvnVVTr5r1DjOQRbhc7-A9zdoHgCfyPoeETY8fhXwB9LofNQVx6YuD41URWDFk4rG2ekFWI7n5YCX2pbB6e6trmrB8qjQ-VU1oEn7aX6MH-p9hN01AsOVM1b5Um3qGpdaQhbj7q12Qf0U2h_WxPk_9tH_GPA51Lmxfh3jwtRez9EK5tO4yP_SlEbZ94jmMKN9_FMbaB1LbQzpubht2lzIxvv_dy4kHf7GLhodR5xOA0Z28sZiv3ElI7ojHMaL5hXFIOxKfx0x_KbTYjzoawm-VCNP7zbGIGaVBp2nYbQNzk-l_Jtu0trZD1kScr7ZH3_88HoSze_q6RtNvzKeAou33KTbLhBVqmdVasZrWpOHpItuxyh-4ZGj0gP6sdk46vF6gk5XWYT1Wyii2yiTUktm-gym6hlE1VsordsopZN1LLpKfn-4eDk3SfP1ubwZOyzmcdjiArGwJeRkMxPw1LlAA1QDuLZFOJ0L9-TMRRJHhZhWaCQZ0kZR2VRxjIqQxk9I_26qeE5oQEwoVR2kEIRRxxQcsoQIh8kaqU8zHdI4P7DTNrE9ap-yjhzHornmYIgUxBkBoIdMuj6TEzalntbRw6azAUk4xSaIbfu7ZV0vaxcNTL0j_3eOPQztOXqA52ooZm3mSZNlPIE22wbWnRP7xj1YuWVl2Tz9r16Rfqz6RxekwfyYnbWTnfJGhvxXcvlX_NeymE |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Scalogram-energy+based+segmentation+of+surface+electromyography+signals+from+swallowing+related+muscles&rft.jtitle=Computer+methods+and+programs+in+biomedicine&rft.au=Sebastian%2C+Roldan-Vasco&rft.au=Estefania%2C+Perez-Giraldo&rft.au=Andres%2C+Orozco-Duque&rft.date=2020-10-01&rft.eissn=1872-7565&rft.volume=194&rft.spage=105480&rft_id=info:doi/10.1016%2Fj.cmpb.2020.105480&rft_id=info%3Apmid%2F32403048&rft.externalDocID=32403048 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0169-2607&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0169-2607&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0169-2607&client=summon |