Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria
Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the E...
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| Published in | Medical & biological engineering & computing Vol. 61; no. 9; pp. 2227 - 2240 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0140-0118 1741-0444 1741-0444 |
| DOI | 10.1007/s11517-023-02802-5 |
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| Abstract | Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely,
K
neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings.
Graphical Abstract
Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring |
|---|---|
| AbstractList | Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings.
Graphical AbstractClinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring.Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring. Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring. Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. |
| Author | Blanco-Velasco, Manuel Plaza-Seco, Carmen Lovisolo, Lisandro Holgado-Cuadrado, Roberto |
| Author_xml | – sequence: 1 givenname: Roberto orcidid: 0000-0002-5913-341X surname: Holgado-Cuadrado fullname: Holgado-Cuadrado, Roberto email: roberto.holgado@uah.es organization: Department for Signal Theory and Communications, Universidad de Alcalá – sequence: 2 givenname: Carmen orcidid: 0000-0002-4714-1789 surname: Plaza-Seco fullname: Plaza-Seco, Carmen organization: Department for Signal Theory and Communications, Universidad de Alcalá – sequence: 3 givenname: Lisandro orcidid: 0000-0002-7404-9371 surname: Lovisolo fullname: Lovisolo, Lisandro organization: Department for Signal Theory and Communications, Universidad de Alcalá, DETEL - Dep. of Electronics and Communications Engineering, UERJ - Rio de Janeiro State University – sequence: 4 givenname: Manuel orcidid: 0000-0001-6593-1517 surname: Blanco-Velasco fullname: Blanco-Velasco, Manuel organization: Department for Signal Theory and Communications, Universidad de Alcalá |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37010711$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_talanta_2024_126180 crossref_primary_10_3390_bioengineering11030222 crossref_primary_10_1109_TBME_2024_3454545 crossref_primary_10_1016_j_bspc_2025_107523 |
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| Issue | 9 |
| Keywords | Long-term monitoring (LTM) Machine learning (ML) Signal quality Electrocardiogram (ECG) Clinical noise severity |
| Language | English |
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In: 2016 computing in cardiology conference (CinC), pp 641–644 Varon C, Testelmans D, Buyse B, Suykens JA, Van Huffel S (2012) Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis. In: IEEE engineering in medicine and biology conference, pp 3151–3154 JabaudonDSztajzelJSievertKLandisTSztajzelRUsefulness of ambulatory 7–day ECG monitoring for the detection of atrial fibrillation and flutter after acute stroke and transient ischemic attackStroke20043571647165110.1161/01.STR.0000131269.69502.d915155965 DagresNKottkampHPiorkowskiCWeisSAryaASommerPBodeKGerds-LiJHKremastinosDTHindricksGInfluence of the duration of Holter monitoring on the detection of arrhythmia recurrences after catheter ablation of atrial fibrillation: Implications for patient follow–upInt J Cardiol2010139330530610.1016/j.ijcard.2008.10.00418990460 BishopCMPattern recognition and machine learning (Information Science and Statistics)2006New YorkSpringer-Verlag LiQRajagopalanCCliffordGDA machine learning approach to multi-level ECG signal quality classificationComput Methods Programs Biomed2014117343544710.1016/j.cmpb.2014.09.00225306242 SatijaURamkumarBManikandanMSA review of signal processing techniques for electrocardiogram signal quality assessmentIEEE Rev Biomed Eng201811365210.1109/RBME.2018.281095729994590 AhsanMAQayyumARaziAQadirJAn active learning method for diabetic retinopathy classification with uncertainty quantificationMed Biol Eng Comput202260102797281110.1007/s11517-022-02633-w35859243 LuzEJdSSchwartzWRCámara-ChávezGMenottiDECG–based heartbeat classification for arrhythmia detection: a surveyComput Methods Programs Biomed201612714416410.1016/j.cmpb.2015.12.00826775139 SatijaURamkumarBManikandanMSAutomated ECG noise detection and classification system for unsupervised healthcare monitoringIEEE J Biomed Health Inform201822372273210.1109/JBHI.2017.268643628333651 SwerskyKSnoekJAdamsRPMulti-task Bayesian optimizationAdv Neural Inf Process Syst20132629512959 BujaLMButanyJCardiovascular pathology20164th edn.San DiegoAcademic Press PortaABaselliGLambardiFCeruttiSAntoliniRDel GrecoMRavelliFNolloGPerformance assessment of standard algorithms for dynamic RT interval measurement: comparison between R-Tapex and R-Tend approachMed Biol Eng Comput199836135421:STN:280:DyaK1c3ns12msw%3D%3D10.1007/BF025228559614746 HeumannCShomakerMShalabh: introduction to statistics and data analysis2016SwitzerlandSpringer10.1007/978-3-319-46162-5 Clifford GD, Azuaje F, McSharry P et al (2006) Advanced methods and tools for ECG data analysis. Artech Hsouse Boston Blanco-VelascoMWengBBarnerKEECG signal denoising and baseline wander correction based on the empirical mode decompositionComput Biol Med200838111310.1016/j.compbiomed.2007.06.00317669389 GoodfellowIBengioYCourvilleADeep learning2016CambridgeMIT Press SnoekJLarochelleHAdamsRPPractical Bayesian optimization of machine learning algorithmsAdv Neural Inf Process Syst20122529512959 HolterNJNew method for heart studiesScience19611343486121412201:STN:280:DyaF38%2FkvF2kuw%3D%3D10.1126/science.134.3486.121413908591 WuXKumarVRoss QuinlanJGhoshJYangQMotodaHMcLachlanGJNgALiuBYuPSTop 10 algorithms in data miningKnowl Inf Syst200814113710.1007/s10115-007-0114-2 HastieTTibshiraniRFriedmanJThe elements of statistical learning: data mining, inference and prediction20092nd edn.New YorkSpringer10.1007/978-0-387-84858-7 Nuñez Y, Lovisolo L, da Silva Mello L, Orihuela C (2022) On the interpretability of machine learning regression for path-loss prediction of millimeter-wave links. Expert Systems with Applications p 119324 Jiali W, Yue Z (2014) Research and implementation of Holter data format unification. In: International conference on medical biometrics, pp 141–146 ChiangHTHsiehYYFuSWHungKHTsaoYChienSYNoise reduction in ECG signals using fully convolutional denoising autoencodersIEEE Access20197608066081310.1109/ACCESS.2019.2912036 OrphanidouCBonniciTCharltonPCliftonDVallanceDTarassenkoLSignal–quality indices for the electrocardiogram and photoplethysmogram: Derivation and applications to wireless monitoringIEEE J Biomed Health Inform201519383283825069129 CliffordGBeharJLiQRezekISignal quality indices and data fusion for determining clinical acceptability of electrocardiogramsPhysiol Meas2012339141914331:STN:280:DC%2BC38fovF2kug%3D%3D10.1088/0967-3334/33/9/141922902749 Redmond SJ, Lovell NH, Basilakis J, Celler BG (2008) ECG quality measures in telecare monitoring. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society, pp 2869–2872 ChiccoDJurmanGThe advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluationBMC Genomics202021111310.1186/s12864-019-6413-7 MorgadoEAlonso-AtienzaFSantiago-MozosRBarquero-PérezÓSilvaIRamosJMarkRQuality estimation of the electrocardiogram using cross-correlation among leadsBiomed Eng Online201514111910.1186/s12938-015-0053-1 TobónDPFalkTHMaierMMS-QI: a modulation spectrum-based ECG quality index for telehealth applicationsIEEE Trans Biomed Eng20146381613162210.1109/TBME.2014.2355135 GoldbergerAAmaralLGlassLHausdorffJIvanovPMarkRMietusJMoodyGBPengCStanleyHPhysiobank, Physiotoolkit, and Physionet: components of a new research resource for complex physiologic signalsCirculation200010123E215201:STN:280:DC%2BD3czhtFGisw%3D%3D10.1161/01.CIR.101.23.e21510851218 XieJPengLWeiLGongYZuoFWangJYinCLiYA signal quality assessment–based ECG waveform delineation method used for wearable monitoring systemsMed Biol Eng Comput202159102073208410.1007/s11517-021-02425-834432182 MoeyersonsJSmetsEMoralesJVillaADe RaedtWTestelmansDBuyseBVan HoofCWillemsRVan HuffelSArtefact detection and quality assessment of ambulatory ECG signalsComput Methods Programs Biomed201918210505010.1016/j.cmpb.2019.105050314734426891233 Blanco-VelascoMCruz-RoldánFGodino-LlorenteJIBarnerKENonlinear trend estimation of the ventricular repolarization segment for T-wave alternans detectionIEEE Trans Biomed Eng201057102402241210.1109/TBME.2010.204810920409985 RodriguesJBeloDGamboaHNoise detection on ECG based on agglomerative clustering of morphological featuresComput Biol Med20178732233410.1016/j.compbiomed.2017.06.00928649031 Pastor-PérezFJManzano-FernándezSGoya-EstebanRPascual-FigalDABarquero-PérezOComparison of detection of arrhythmias in patients with chronic heart failure secondary to non-ischemic versus ischemic cardiomyopathy by 1 versus 7-day Holter monitoringAm J Cardiol2010106567768110.1016/j.amjcard.2010.04.02720723645 ZhaoZZhangYSQI quality evaluation mechanism of single-lead ECG signal based on simple heuristic fusion and fuzzy comprehensive evaluationFront Physiol2018972710.3389/fphys.2018.00727299629626011094 T Hastie (2802_CR33) 2009 NJ Holter (2802_CR3) 1961; 134 2802_CR4 B Tomas (2802_CR16) 2022; 72 LM Buja (2802_CR1) 2016 J Qin (2802_CR41) 2021; 231 MA Ahsan (2802_CR42) 2022; 60 FJ Pastor-Pérez (2802_CR7) 2010; 106 G Clifford (2802_CR24) 2012; 33 Z Zhao (2802_CR19) 2018; 9 A Goldberger (2802_CR29) 2000; 101 J Snoek (2802_CR30) 2012; 25 U Satija (2802_CR12) 2018; 11 Q Li (2802_CR26) 2014; 117 J Rodrigues (2802_CR25) 2017; 87 L Breiman (2802_CR34) 2001; 45 D Chicco (2802_CR39) 2020; 21 A Porta (2802_CR14) 1998; 36 C Orphanidou (2802_CR18) 2015; 19 I Goodfellow (2802_CR36) 2016 C Heumann (2802_CR38) 2016 N Dagres (2802_CR6) 2010; 139 2802_CR17 E Everss-Villalba (2802_CR10) 2017; 17 2802_CR9 2802_CR8 U Satija (2802_CR27) 2018; 22 M Blanco-Velasco (2802_CR37) 2010; 57 EJdS Luz (2802_CR2) 2016; 127 J Xie (2802_CR23) 2021; 59 E Morgado (2802_CR22) 2015; 14 X Wu (2802_CR35) 2008; 14 CM Bishop (2802_CR32) 2006 K Swersky (2802_CR31) 2013; 26 2802_CR20 2802_CR40 M Blanco-Velasco (2802_CR11) 2008; 38 J Moeyersons (2802_CR21) 2019; 182 DP Tobón (2802_CR15) 2014; 63 D Jabaudon (2802_CR5) 2004; 35 HT Chiang (2802_CR13) 2019; 7 2802_CR28 |
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| Snippet | Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for... |
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| SubjectTerms | Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Classification Comparative studies comparative study Computer Applications data collection Decision trees Diagnosis EKG Electrocardiography Human Physiology Imaging Learning algorithms Machine learning Noise monitoring patients Performance indices Quality assessment Radiology Review Review Article Signal quality Support vector machines Taxonomy Test sets Waveforms |
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| Title | Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria |
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