Integration Design of Portable ECG Signal Acquisition With Deep-Learning Based Electrode Motion Artifact Removal on an Embedded System

For long-term electrocardiogram (ECG) signal monitoring, a portable and small size acquisition device with Bluetooth low energy (BLE) communication is designed and integrated with a Nvidia Jetson Xavier NX for realizing the electrode motion artifact removal technique. The digitalized ECG codes are c...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE access Vol. 10; pp. 57555 - 57564
Main Authors:	Jhang, Yu-Syuan, Wang, Szu-Ting, Sheu, Ming-Hwa, Wang, Szu-Hong, Lai, Shin-Chi
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive filters Algorithms Artificial intelligence Bluetooth Circuits Convolution Deep learning denoising autoencoder (DAE) ECG signal enhancement Electrocardiogram (ECG) Electrocardiography Electrodes embedded system Embedded systems Machine learning Noise Noise generation Noise measurement Noise reduction Portable equipment Signal monitoring Signal to noise ratio
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	For long-term electrocardiogram (ECG) signal monitoring, a portable and small size acquisition device with Bluetooth low energy (BLE) communication is designed and integrated with a Nvidia Jetson Xavier NX for realizing the electrode motion artifact removal technique. The digitalized ECG codes are converted from a front-end circuit, which contains several amplifiers and filters in the acquisition system. Thereafter, a zero padding scheme is applied for each 10-bits data to separate them into two-bytes data for BLE transmission. Xavier Edge AI platform receives these transmitted data and removes the electrode motion (EM) noise using the proposed low memory shortcut connection-based denoised autoencoder (LMSC-DAE). The simulation results demonstrate that the proposed algorithm significantly improves the signal-to-noise ratio (SNR) by 5.41 dB under the condition of SNR in = 12 dB, compared with convolutional denoising autoencoder with long short-term memory (CNN-LSTM-DAE) method. For practical test, an Arduino DUE platform is employed to generate noise interference by controlling a commercial digital-to-analog convertor. By combining the proposed ECG acquisition device with a non-inverting weighted summer, it can be applied to verify the reproducibility of measurement for the proposed method. The measurement results clearly indicate that the proposed LMSC-DAE has a higher improvement of SNR and lower percentage root-mean-square difference than the state-of-the-art Fully Convolutional Denoising Autoencoder (FCN-DAE).
AbstractList	For long-term electrocardiogram (ECG) signal monitoring, a portable and small size acquisition device with Bluetooth low energy (BLE) communication is designed and integrated with a Nvidia Jetson Xavier NX for realizing the electrode motion artifact removal technique. The digitalized ECG codes are converted from a front-end circuit, which contains several amplifiers and filters in the acquisition system. Thereafter, a zero padding scheme is applied for each 10-bits data to separate them into two-bytes data for BLE transmission. Xavier Edge AI platform receives these transmitted data and removes the electrode motion (EM) noise using the proposed low memory shortcut connection-based denoised autoencoder (LMSC-DAE). The simulation results demonstrate that the proposed algorithm significantly improves the signal-to-noise ratio (SNR) by 5.41 dB under the condition of SNRin = 12 dB, compared with convolutional denoising autoencoder with long short-term memory (CNN-LSTM-DAE) method. For practical test, an Arduino DUE platform is employed to generate noise interference by controlling a commercial digital-to-analog convertor. By combining the proposed ECG acquisition device with a non-inverting weighted summer, it can be applied to verify the reproducibility of measurement for the proposed method. The measurement results clearly indicate that the proposed LMSC-DAE has a higher improvement of SNR and lower percentage root-mean-square difference than the state-of-the-art Fully Convolutional Denoising Autoencoder (FCN-DAE). For long-term electrocardiogram (ECG) signal monitoring, a portable and small size acquisition device with Bluetooth low energy (BLE) communication is designed and integrated with a Nvidia Jetson Xavier NX for realizing the electrode motion artifact removal technique. The digitalized ECG codes are converted from a front-end circuit, which contains several amplifiers and filters in the acquisition system. Thereafter, a zero padding scheme is applied for each 10-bits data to separate them into two-bytes data for BLE transmission. Xavier Edge AI platform receives these transmitted data and removes the electrode motion (EM) noise using the proposed low memory shortcut connection-based denoised autoencoder (LMSC-DAE). The simulation results demonstrate that the proposed algorithm significantly improves the signal-to-noise ratio (SNR) by 5.41 dB under the condition of SNR in = 12 dB, compared with convolutional denoising autoencoder with long short-term memory (CNN-LSTM-DAE) method. For practical test, an Arduino DUE platform is employed to generate noise interference by controlling a commercial digital-to-analog convertor. By combining the proposed ECG acquisition device with a non-inverting weighted summer, it can be applied to verify the reproducibility of measurement for the proposed method. The measurement results clearly indicate that the proposed LMSC-DAE has a higher improvement of SNR and lower percentage root-mean-square difference than the state-of-the-art Fully Convolutional Denoising Autoencoder (FCN-DAE).
Author	Sheu, Ming-Hwa Wang, Szu-Ting Jhang, Yu-Syuan Lai, Shin-Chi Wang, Szu-Hong
Author_xml	– sequence: 1 givenname: Yu-Syuan surname: Jhang fullname: Jhang, Yu-Syuan organization: Department of Electronics Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan – sequence: 2 givenname: Szu-Ting surname: Wang fullname: Wang, Szu-Ting organization: Doctor's Program of Smart Industry Technology Research and Design, National Formosa University, Huwei, Taiwan – sequence: 3 givenname: Ming-Hwa surname: Sheu fullname: Sheu, Ming-Hwa organization: Department of Electronics Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan – sequence: 4 givenname: Szu-Hong orcidid: 0000-0002-3889-1764 surname: Wang fullname: Wang, Szu-Hong organization: Department of Electronics Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan – sequence: 5 givenname: Shin-Chi orcidid: 0000-0003-0011-3649 surname: Lai fullname: Lai, Shin-Chi email: shivan0111@nfu.edu.tw organization: Department of Automation Engineering, National Formosa University, Huwei, Taiwan
BookMark	eNqFkc1uEzEUhUeoSJTSJ-jGEusJ_hmP7WUY0hIpCERALC2PfSc4moxT20HqC_DcuJmqQmzwxtbxd861fF5XF1OYoKpuCF4QgtW7ZdetttsFxZQuGBFSNuJFdUlJq2rGWXvx1_lVdZ3SHpcli8TFZfV7PWXYRZN9mNAHSH43oTCgLyFm04-AVt0d2hbRjGhp708--TP5w-efBYdjvQETJz_t0HuTwKHVCDbH4AB9CmdyGbMfjM3oKxzCrxJTNDOh1aEH54ph-5AyHN5ULwczJrh-2q-q77erb93HevP5bt0tN7VtsMx13_OWGUYG7oSTlFrC-KDcgB0oZgl1SlnbskEagwltbDNwKXhDDHPcupazq2o957pg9voY_cHEBx2M12chxJ025cF2BA2OiRZzappWNgyUtLSXQHsMzJi2FSXr7Zx1jOH-BCnrfTjF8lNJ01Y0WHDesEKxmbIxpBRheJ5KsH7sT8_96cf-9FN_xaX-cVmfzyXlaPz4H-_N7PUA8DxNCdmocvsHMnWqtA
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1109_TCSI_2025_3533544 crossref_primary_10_1007_s00034_024_02658_6 crossref_primary_10_1016_j_autcon_2024_105537 crossref_primary_10_1016_j_measurement_2025_117067
Cites_doi	10.1109/HealthCom.2014.7001883 10.1016/j.measurement.2013.07.009 10.1109/ACCESS.2019.2912036 10.1166/jmihi.2015.1649 10.1109/JSEN.2011.2111453 10.1161/CIRCULATIONAHA.106.180200 10.1016/j.dsp.2005.12.003 10.1155/2021/5574376 10.1109/CNSC.2014.6906684 10.1016/j.irbm.2014.10.004 10.3390/info10020035 10.1016/j.neunet.2014.09.003 10.1155/2018/1868519 10.3390/s20061796 10.1109/IEMBS.2006.260464 10.1109/NFSI-ICFBI.2007.4387719 10.1016/j.bspc.2020.102225 10.1109/MECBME.2014.6783250 10.1109/IMCEC.2016.7867525 10.4028/www.scientific.net/AMM.357-360.2267 10.1109/CVPR.2016.90
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2022.3178847
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ: Directory of Open Access Journal (DOAJ)
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	57564
ExternalDocumentID	oai_doaj_org_article_ed376052a46843e98c2b8e2b0e3aa667 10_1109_ACCESS_2022_3178847 9784947
Genre	orig-research
GrantInformation_xml	– fundername: Ministry of Education (MOE) Female Researching Talent Cultivation Project for Science, Technology, Engineering, and Mathematics (STEM) field – fundername: Smart Machinery and Intelligent Manufacturing Research Center funderid: 10.13039/501100013304 – fundername: Higher Education SPROUT Project, National Formosa University – fundername: Ministry of Science and Technology, Taiwan grantid: MOST 110-2622-E-224-006; 110-2221-E-150-045; 109-2221-E-150-043 funderid: 10.13039/501100004663
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c408t-bb563a31f5d7d822c135f9df0de93c12d99cc63f8aa0124c4f587541a3d5cd653
IEDL.DBID	RIE
ISICitedReferencesCount	7
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000808034000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:50:51 EDT 2025 Mon Jun 30 07:21:04 EDT 2025 Sat Nov 29 06:32:09 EST 2025 Tue Nov 18 20:53:17 EST 2025 Wed Aug 27 02:24:33 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-bb563a31f5d7d822c135f9df0de93c12d99cc63f8aa0124c4f587541a3d5cd653
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-0011-3649 0000-0002-3889-1764
OpenAccessLink	https://ieeexplore.ieee.org/document/9784947
PQID	2674075543
PQPubID	4845423
PageCount	10
ParticipantIDs	ieee_primary_9784947 crossref_primary_10_1109_ACCESS_2022_3178847 doaj_primary_oai_doaj_org_article_ed376052a46843e98c2b8e2b0e3aa667 crossref_citationtrail_10_1109_ACCESS_2022_3178847 proquest_journals_2674075543
PublicationCentury	2000
PublicationDate	20220000 2022-00-00 20220101 2022-01-01
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– year: 2022 text: 20220000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref15 ref14 ref11 kligfield (ref23) 2007; 115 ref2 vincent (ref18) 2010; 11 ref1 ref17 haykin (ref10) 2005 ref16 ref19 apandi (ref5) 2020; 7 moody (ref29) 2022 galeotti (ref30) 2013 moody (ref24) 2022 maas (ref27) 2013 lin (ref26) 2021 chua (ref4) 2020; 36 ref25 ref20 ref22 ref21 ref28 ref8 ref7 ref9 ref3 ref6
References_xml	– ident: ref2 doi: 10.1109/HealthCom.2014.7001883 – ident: ref9 doi: 10.1016/j.measurement.2013.07.009 – ident: ref21 doi: 10.1109/ACCESS.2019.2912036 – volume: 36 start-page: 251 year: 2020 ident: ref4 article-title: Comparison of arrhythmia detection by 24-hour Holter and 14-day continuous electrocardiography patch monitoring publication-title: Acta Cardiol Sin – ident: ref20 doi: 10.1166/jmihi.2015.1649 – ident: ref8 doi: 10.1109/JSEN.2011.2111453 – volume: 11 start-page: 3371 year: 2010 ident: ref18 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J Mach Learn Res – start-page: 511 year: 2013 ident: ref30 article-title: Measurement of noise in ECG signals to improve automatic delineation publication-title: Proc Comput Cardiol – start-page: 3 year: 2013 ident: ref27 article-title: Rectifier nonlinearities improve neural network acoustic models publication-title: Proc ICML Workshop Deep Learn Audio Speech Lang Process – year: 2022 ident: ref24 publication-title: MIT-BIH Noise Stress Test Database V1 0 0 – volume: 7 start-page: 1 year: 2020 ident: ref5 article-title: An analysis of the effects of noisy electrocardiogram signal on heartbeat detection performance publication-title: Bioengineering – volume: 115 start-page: 1306 year: 2007 ident: ref23 article-title: Recommendations for the standardization and interpretation of the electrocardiogram: Part I: The electrocardiogram and its technology: A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.106.180200 – ident: ref17 doi: 10.1016/j.dsp.2005.12.003 – ident: ref1 doi: 10.1155/2021/5574376 – ident: ref13 doi: 10.1109/CNSC.2014.6906684 – ident: ref14 doi: 10.1016/j.irbm.2014.10.004 – ident: ref6 doi: 10.3390/info10020035 – ident: ref19 doi: 10.1016/j.neunet.2014.09.003 – year: 2021 ident: ref26 article-title: MCUNetV2: Memory-efficient patch-based inference for tiny deep learning publication-title: arXiv 2110 15352 – year: 2022 ident: ref29 publication-title: MIT-BIH Arrhythmia Database v1 0 0 – ident: ref28 doi: 10.1155/2018/1868519 – ident: ref3 doi: 10.3390/s20061796 – ident: ref7 doi: 10.1109/IEMBS.2006.260464 – ident: ref11 doi: 10.1109/NFSI-ICFBI.2007.4387719 – year: 2005 ident: ref10 publication-title: Least-Mean-Square Adaptive Filters – ident: ref22 doi: 10.1016/j.bspc.2020.102225 – ident: ref12 doi: 10.1109/MECBME.2014.6783250 – ident: ref15 doi: 10.1109/IMCEC.2016.7867525 – ident: ref16 doi: 10.4028/www.scientific.net/AMM.357-360.2267 – ident: ref25 doi: 10.1109/CVPR.2016.90
SSID	ssj0000816957
Score	2.287835
Snippet	For long-term electrocardiogram (ECG) signal monitoring, a portable and small size acquisition device with Bluetooth low energy (BLE) communication is designed...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	57555
SubjectTerms	Adaptive filters Algorithms Artificial intelligence Bluetooth Circuits Convolution Deep learning denoising autoencoder (DAE) ECG signal enhancement Electrocardiogram (ECG) Electrocardiography Electrodes embedded system Embedded systems Machine learning Noise Noise generation Noise measurement Noise reduction Portable equipment Signal monitoring Signal to noise ratio
SummonAdditionalLinks	– databaseName: DOAJ: Directory of Open Access Journal (DOAJ) dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQ1QMcEG1BLC2VDxyJmvgV-9gu24cEFeLZm-WMbVip3e1j25_A7-740dVKleDC1XLGiWfi8ZeMv4-Qd5i2IYBiDVfaNOlPU-NA8sZFMC6d3OWZeP7Hx_70VJ-dmc8rUl-pJqzQA5eJ2ws-lW1I5oTSggejgQ06sKEN3Dml8jnytjcrYCqvwbpTRvaVZqhrzd7-eIxPhICQMcSpCPySoMpKKsqM_VVi5dG6nJPN4QvyvO4S6X65uw3yJMw2ybMV7sAt8uekEj3gxNIPuQ6DziPNlaHDeaCT8RH9io3JDFzdTkttFv05XfzG7uGyqcyqv-gBJjJPJ0UPxwf6KQv75MHTsQf6JVzMMR4ptrkZnVwMARcrTwvX-Uvy_XDybXzcVFGFBkSrF80wSMUd76L0vcfdAXRcRuNj64Ph0DFvDIDiUTuHuUuAiBIhjegc9xK8kvwVWZvNZ-E1oeCYCW2UvXFceCWcV6qFQSk0FKPmI8Ie5tdCZRxPwhfnNiOP1tjiFJucYqtTRuT98qLLQrjx9-4HyXHLroktOzdgDNkaQ_ZfMTQiW8ntSyOIrIVJtncewsDWN_vGMtUjBsZNGH_zP4beJk_T45SPOjtkbXF9G96SdbhbTG-ud3NQ3wP7Affb priority: 102 providerName: Directory of Open Access Journals
Title	Integration Design of Portable ECG Signal Acquisition With Deep-Learning Based Electrode Motion Artifact Removal on an Embedded System
URI	https://ieeexplore.ieee.org/document/9784947 https://www.proquest.com/docview/2674075543 https://doaj.org/article/ed376052a46843e98c2b8e2b0e3aa667
Volume	10
WOSCitedRecordID	wos000808034000001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources (ISSN International Center) customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELbaigMcoFAQC23lA8cGEr8SH9ttCki0Qjx7i5zxGFZqd0u7y5Ejv5ux40aVipC4RJFlO04-O-MZz3zD2AsS24BgRCFNY4t40lQ40LJwAayLkbsyEc9_eVefnDSnp_b9GtsbY2EQMTmf4ct4m87y_QJW0VQW2WCVVfU6W69rM8RqjfaUmEDC6joTC1WlfbU_ndI7kAooBGmmpOrFFCo3hE_i6M9JVW79iZN4OXrwfwPbZPfzNpLvD7g_ZGs4f8Tu3SAX3GK_32YmCPry_DA5avBF4Ml1tD9D3k5f849UGLuBH6vZ4LzFv86W36k6XhSZevUbPyBJ53k7JMzxyI9T5p_08BgXwT_g-YImLKcyN-fteY_0N_N8IEN_zD4ftZ-mb4qcdaEAVTbLou-1kU5WQfva0_YBKqmD9aH0aCVUwlsLYGRonCPhpkAFTTqPqpz0GrzR8gnbmC_m-JRxcMJiGXRtnVTeKOeNKaE3hjoKoZETJq7h6CBTksfMGGddUk1K2w0YdhHDLmM4YXtjo4uBkePf1Q8izmPVSKedCgjALq_ODn30DdLCKdMoibYB0Tco-hKlc8ZQJ1sR9LGTjPeEbV_Pmi4v_atOmJqUZNqlyWd_b_Wc3Y0DHOw422xjebnCHXYHfi5nV5e7yShA1-Nf7W6a4X8Awk_3hw
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dTxQxEG8QTZQHv8B4gtoHH1nd7dduH-E8hHhcjKLy1nTbqV4Cdwh3_An-3Uy7ZUMCMfFt07Sz3f21nU478xtC3qHaduAUK7hqdBFvmgrrJC9scNrGyF2eiOd_jOvJpDk-1l9WyHYfCwMAyfkM3sfHdJfv524Zj8oiG6zQor5H7kshWNlFa_UnKjGFhJZ1phaqSv1hZzjEr0AjkDG0TdHYi0lUbqifxNKf06rcWouTgtl78n9de0oe540k3emQf0ZWYPacrN2gF1wnfw8yFwT-e_oxuWrQeaDJebQ9AToafqLfsDCKcX-W0859i_6cLn5jdTgrMvnqL7qLus7TUZcyxwM9TLl_0stjZAT9CqdzHLIUy-yMjk5bwPXM044OfYN83xsdDfeLnHehcKJsFkXbSsUtr4L0tccNhKu4DNqH0oPmrmJea-cUD421qN6EE0Gi1SMqy710Xkn-gqzO5jN4SaizTEMZZK0tF14J65UqXasUCgqh4QPCruEwLpOSx9wYJyYZJ6U2HYYmYmgyhgOy3Tc66zg5_l19N-LcV42E2qkAATR5fhrw0TtIMitUIzjoxrG2AdaWwK1VCoWsR9B7IRnvAdm6HjUmT_4Lw1SNZjLu0_iru1u9JQ_3jw7HZnww-bxJHsXOdqc6W2R1cb6E1-SBu1xML87fpBF-BRe4-Kg
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=Integration+Design+of+Portable+ECG+Signal+Acquisition+With+Deep-Learning+Based+Electrode+Motion+Artifact+Removal+on+an+Embedded+System&rft.jtitle=IEEE+access&rft.au=Jhang%2C+Yu-Syuan&rft.au=Wang%2C+Szu-Ting&rft.au=Sheu%2C+Ming-Hwa&rft.au=Wang%2C+Szu-Hong&rft.date=2022&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=10&rft.spage=57555&rft.epage=57564&rft_id=info:doi/10.1109%2FACCESS.2022.3178847&rft.externalDocID=9784947
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon