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Home / Equine Research Bank / PLoS One / A Novel Algorithm for Movement Artifact Removal in ECG Signa...
PloS one2015; 10(10); e0140783; doi: 10.1371/journal.pone.0140783

A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses.

Abstract: This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p-value < 0.0.5).
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Publication Date: 2015-10-20 PubMed ID: 26484686PubMed Central: PMC4618928DOI: 10.1371/journal.pone.0140783Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research presents a new method to detect and decrease movement artifacts in electrocardiogram readings taken from horses. The method proved more successful in reducing these artifacts than existing techniques.

Objective of the Study

  • The objective of the research was to propose and analyze a new algorithm called Stationary Wavelet Movement Artifact Reduction (SWMAR). This algorithm uses the Stationary Wavelet Transform (SWT) decomposition technique to identify and remove movement artifacts from electrocardiogram (ECG) signals, specifically those recorded in horses. The study aimed to improve the quality of ECG signals acquired from horses in free movement conditions, where much of the noise or distortions can originate from their motion.

Methodology

  • The research involved continuous acquisition of ECG and physical activity data from seven horses, utilizing fully integrated textile electrodes to monitor the ECG. This wearable system also included a triaxial accelerometer to track movement.
  • Comparative analysis was made between the new SWMAR algorithm and the Normalized Least Mean Square Adaptive Filter technique (NLMSAF), an existing technique prevalent in literature for removing artifacts when noise bandwidth overlaps with the primary source bandwidth.

Findings

  • The SWMAR algorithm resulted in a significant reduction in movement artifacts. This was demonstrated by analysis of seven hours of recordings, which displayed a reduction greater than 40% in MA percentage (comparison between before and after the application of the SWMAR algorithm).
  • Comparative analysis with NLMSAF indicated a greater reduction in artifact percentage with the application of SWMAR. The statistical significance of these findings was confirmed with the p-value being less than 0.05.

Conclusions

  • The research concluded that the proposed SWMAR algorithm was effective at identifying and reducing movement artifacts in horse ECG signals.
  • Compared to the existing NLMSAF technique, SWMAR showed a statistically significant superior performance in reducing such artifacts.

This tool could potentially mitigate problems encountered in accurately interpreting ECG readings due to movement artifacts, thereby improving the relevance and accuracy of such data for veterinary and equine health studies.

Cite This Article

APA
Lanata A, Guidi A, Baragli P, Valenza G, Scilingo EP. (2015). A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses. PLoS One, 10(10), e0140783. https://doi.org/10.1371/journal.pone.0140783

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 10
Issue: 10
Pages: e0140783
PII: e0140783

Researcher Affiliations

Lanata, Antonio
  • Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
Guidi, Andrea
  • Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
Baragli, Paolo
  • Department of Veterinary Sciences, University of Pisa, Pisa, Italy.
Valenza, Gaetano
  • Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
Scilingo, Enzo Pasquale
  • Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.

MeSH Terms

  • Algorithms
  • Animals
  • Artifacts
  • Electrocardiography
  • Horses
  • Movement / physiology
  • Signal Processing, Computer-Assisted
  • Wavelet Analysis

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 73 references
  1. Hamilton P. Comparison of methods for adaptive removal of motion artifact. In: Computers in Cardiology 2000. IEEE; 2000. p. 383–386.
  2. Aase SO, Eftestøl T, Husøy JH, Sunde K, Steen PA. CPR artifact removal from human ECG using optimal multichannel filtering.. IEEE Trans Biomed Eng 2000 Nov;47(11):1440-9.
    doi: 10.1109/10.880095pubmed: 11077737google scholar: lookup
  3. Rahman MZU. Adaptive noise removal in the ECG using the block LMS algorithm. In: Adaptive Science & Technology, 2009. ICAST 2009. 2nd International Conference on. IEEE; 2009. p. 380–383.
  4. Deepu CJ. An ECG-on-chip for wearable cardiac monitoring devices. In: Electronic Design, Test and Application, 2010. DELTA’10. Fifth IEEE International Symposium on. IEEE; 2010. p. 225–228.
  5. Zito D, Pepe D, Neri B, Zito F, De Rossi D, Lanatà A. Feasibility Study and Design of a Wearable System-on-a-Chip Pulse Radar for Contactless Cardiopulmonary Monitoring.. Int J Telemed Appl 2008;2008:328597.
    doi: 10.1155/2008/328597pmc: PMC2277553pubmed: 18389068google scholar: lookup
  6. Lanatà A, Scilingo EP, De Rossi D. A multimodal transducer for cardiopulmonary activity monitoring in emergency.. IEEE Trans Inf Technol Biomed 2010 May;14(3):817-25.
    doi: 10.1109/TITB.2009.2024414pubmed: 19527961google scholar: lookup
  7. Anapagamini S, Rajavel R. Removal of artifacts in ECG using Empirical mode decomposition. In: Communications and Signal Processing (ICCSP), 2013 International Conference on. IEEE; 2013. p. 288–292.
  8. Erçelebi E. Electrocardiogram signals de-noising using lifting-based discrete wavelet transform.. Comput Biol Med 2004 Sep;34(6):479-93.
    doi: 10.1016/S0010-4825(03)00090-8pubmed: 15265720google scholar: lookup
  9. Wu Y, Rangayyan RM. An algorithm for evaluating the performance of adaptive filters for the removal of artifacts in ECG signals. In: Electrical and Computer Engineering, 2007. CCECE 2007. Canadian Conference on. IEEE; 2007. p. 864–867.
  10. Shoker L. Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. Signal Processing Letters, IEEE 2005;12(10):721–724.
    doi: 10.1109/LSP.2005.855539google scholar: lookup
  11. Velazquez R. An optimal adaptive filtering approach for stress-tests motion artifacts removal: application on an ECG for telediagnosis. In: Signal Processing, 2002 6th International Conference on. vol. 2. IEEE; 2002. p. 1504–1507.
  12. Hedley M, Yan H. Motion artifact suppression: a review of post-processing techniques.. Magn Reson Imaging 1992;10(4):627-35.
    doi: 10.1016/0730-725X(92)90014-Qpubmed: 1501533google scholar: lookup
  13. Greco A, Lanatà A, Valenza G, Rota G, Vanello N, Scilingo EP. On the deconvolution analysis of electrodermal activity in bipolar patients.. Annu Int Conf IEEE Eng Med Biol Soc 2012;2012:6691-4.
    pubmed: 23367464doi: 10.1109/embc.2012.6347529google scholar: lookup
  14. Lanatà A, Valenza G, Greco A, Gentili C, Bartolozzi R, Bucchi F. How the Autonomic Nervous System and Driving Style Change With Incremental Stressing Conditions During Simulated Driving. Intelligent Transportation Systems, IEEE Transactions on 2015;16(3):1505–1517.
    doi: 10.1109/TITS.2014.2365681google scholar: lookup
  15. Serteyn A, Vullings R, Meftah M, Bergmans J. Using an injection signal to reduce motion artifacts in capacitive ECG measurements.. Annu Int Conf IEEE Eng Med Biol Soc 2013;2013:4795-8.
    pubmed: 24110807doi: 10.1109/embc.2013.6610620google scholar: lookup
  16. Lim YG, Kim KK, Park KS. ECG recording on a bed during sleep without direct skin-contact.. IEEE Trans Biomed Eng 2007 Apr;54(4):718-25.
    doi: 10.1109/TBME.2006.889194pubmed: 17405379google scholar: lookup
  17. Sameni R, Jutten C, Shamsollahi MB. Multichannel electrocardiogram decomposition using periodic component analysis.. IEEE Trans Biomed Eng 2008 Aug;55(8):1935-40.
    doi: 10.1109/TBME.2008.919714pubmed: 18632355google scholar: lookup
  18. Sameni R, Clifford GD. A Review of Fetal ECG Signal Processing; Issues and Promising Directions.. Open Pacing Electrophysiol Ther J 2010 Jan 1;3:4-20.
    pmc: PMC3100207pubmed: 21614148doi: 10.2174/1876536x01003010004google scholar: lookup
  19. Webster JG. Reducing motion artifacts and interference in biopotential recording.. IEEE Trans Biomed Eng 1984 Dec;31(12):823-6.
    doi: 10.1109/TBME.1984.325244pubmed: 6396207google scholar: lookup
  20. Odman S, Oberg PA. Movement-induced potentials in surface electrodes.. Med Biol Eng Comput 1982 Mar;20(2):159-66.
    doi: 10.1007/BF02441351pubmed: 7098572google scholar: lookup
  21. Webster J. Medical instrumentation: application and design. John Wiley & Sons; 2009.
  22. de Talhouet H, Webster JG. The origin of skin-stretch-caused motion artifacts under electrodes.. Physiol Meas 1996 May;17(2):81-93.
    doi: 10.1088/0967-3334/17/2/003pubmed: 8724520google scholar: lookup
  23. Thakor N, Webster J. The origin of skin potential and its variations. In: Proc. Ann. Conf. Eng. Biol. Med. vol. 20; 1978. p. 212.
  24. Tam HW, Webster JG. Minimizing electrode motion artifact by skin abrasion.. IEEE Trans Biomed Eng 1977 Mar;24(2):134-9.
    doi: 10.1109/TBME.1977.326117pubmed: 892816google scholar: lookup
  25. Burbank DP, Webster JG. Reducing skin potential motion artefact by skin abrasion.. Med Biol Eng Comput 1978 Jan;16(1):31-8.
    doi: 10.1007/BF02442929pubmed: 305999google scholar: lookup
  26. Khan A, Greatbatch W. Physiologic electrodes. Medical engineering 1974;p. 1073–1082.
  27. Yelderman M, Widrow B, Cioffi JM, Hesler E, Leddy JA. ECG enhancement by adaptive cancellation of electrosurgical interference.. IEEE Trans Biomed Eng 1983 Jul;30(7):392-8.
    doi: 10.1109/TBME.1983.325039pubmed: 6618506google scholar: lookup
  28. Ferrara ER, Widrow B. Fetal electrocardiogram enhancement by time-sequenced adaptive filtering.. IEEE Trans Biomed Eng 1982 Jun;29(6):458-60.
    doi: 10.1109/TBME.1982.324973pubmed: 7106797google scholar: lookup
  29. Vullings R, de Vries B, Bergmans JW. An adaptive Kalman filter for ECG signal enhancement.. IEEE Trans Biomed Eng 2011 Apr;58(4):1094-103.
    doi: 10.1109/TBME.2010.2099229pubmed: 21156383google scholar: lookup
  30. Widrow B. Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE 1975;63(12):1692–1716.
    doi: 10.1109/PROC.1975.10036google scholar: lookup
  31. Thakor NV, Zhu YS. Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection.. IEEE Trans Biomed Eng 1991 Aug;38(8):785-94.
    doi: 10.1109/10.83591pubmed: 1937512google scholar: lookup
  32. Devlin PH. Detection electrode motion noise in ecg signals by monitoring electrode impedance. Computers in Cardiology 1984;p. 51–56.
  33. Hamilton PS, Curley MG. Adaptive removal of motion artifact. In: Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE. vol. 1. IEEE; 1997. p. 297–299.
  34. Hamilton PS, Curley M, Aimi R. Effect of adaptive motion-artifact reduction on QRS detection.. Biomed Instrum Technol 2000 May-Jun;34(3):197-202.
    pubmed: 10868261
  35. Luo S, Tompkins WJ. Experimental study: brachial motion artifact reduction in the ECG. In: Computers in Cardiology 1995. IEEE; 1995. p. 33–36.
  36. Liu Y, Pecht MG. Reduction of skin stretch induced motion artifacts in electrocardiogram monitoring using adaptive filtering.. Conf Proc IEEE Eng Med Biol Soc 2006;2006:6045-8.
    pubmed: 17945928doi: 10.1109/iembs.2006.260006google scholar: lookup
  37. Tong D. Adaptive reduction of motion artifact in the electrocardiogram. In: Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint. vol. 2. IEEE; 2002. p. 1403–1404.
  38. Raya MAD, Sison LG. Adaptive noise cancelling of motion artifact in stress ECG signals using accelerometer. In: Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint. vol. 2. IEEE; 2002. p. 1756–1757.
  39. Barros AK. Removing artifacts from electrocardiographic signals using independent components analysis. Neurocomputing 1998;22(1):173–186.
    doi: 10.1016/S0925-2312(98)00056-3google scholar: lookup
  40. Milanesi M, Martini N, Vanello N, Positano V, Santarelli MF, Paradiso R, De Rossi D, Landini L. Multichannel techniques for motion artifacts removal from electrocardiographic signals.. Conf Proc IEEE Eng Med Biol Soc 2006;2006:3391-4.
    pubmed: 17946178doi: 10.1109/iembs.2006.260464google scholar: lookup
  41. Milanesi M, Martini N, Vanello N, Positano V, Santarelli MF, Landini L. Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals.. Med Biol Eng Comput 2008 Mar;46(3):251-61.
    doi: 10.1007/s11517-007-0293-8pubmed: 18064502google scholar: lookup
  42. Chawla M. PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison. Applied Soft Computing 2011;11(2):2216–2226.
    doi: 10.1016/j.asoc.2010.08.001google scholar: lookup
  43. Lee J, McManus DD, Merchant S, Chon KH. Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical approaches.. IEEE Trans Biomed Eng 2012 Jun;59(6):1499-506.
    doi: 10.1109/TBME.2011.2175729pubmed: 22086485google scholar: lookup
  44. Chang KM. Arrhythmia ECG noise reduction by ensemble empirical mode decomposition.. Sensors (Basel) 2010;10(6):6063-80.
    doi: 10.3390/s100606063pmc: PMC3247747pubmed: 22219702google scholar: lookup
  45. Pesquet JC. Time-invariant orthonormal wavelet representations. Signal Processing, IEEE Transactions on 1996;44(8):1964–1970.
    doi: 10.1109/78.533717google scholar: lookup
  46. Strasser F. Motion artifact removal in ECG signals using multi-resolution thresholding. In: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European. IEEE; 2012. p. 899–903.
  47. Reef VB, Bonagura J, Buhl R, McGurrin MK, Schwarzwald CC, van Loon G, Young LE. Recommendations for management of equine athletes with cardiovascular abnormalities.. J Vet Intern Med 2014 May-Jun;28(3):749-61.
    doi: 10.1111/jvim.12340pmc: PMC4895474pubmed: 24628586google scholar: lookup
  48. Young L, van Loon G. Diseases of the heart and vessels. Equine sports medicine and surgery: basic and clinical sciences of equine athlete 2013;p. 695–744.
  49. Petersen E. Prevalence of arrhythmias during and immediately after racing in Standardbred trotters–is there an association between arrhythmias and myocardial hypertrophy. In: Proceedings of the BEVA Congress, Liverpool, United Kingdom; 2008. p. 138.
  50. Buhl R, Andersen LO, Karlshøj M, Kanters JK. Evaluation of clinical and electrocardiographic changes during the euthanasia of horses.. Vet J 2013 Jun;196(3):483-91.
    doi: 10.1016/j.tvjl.2012.11.016pubmed: 23290564google scholar: lookup
  51. Buhl R. Valvular regurgitations in the horse: the importance of an exercise ECG.. Vet J 2010 Feb;183(2):117-8.
    doi: 10.1016/j.tvjl.2009.06.018pubmed: 20036587google scholar: lookup
  52. Martin BB Jr, Reef VB, Parente EJ, Sage AD. Causes of poor performance of horses during training, racing, or showing: 348 cases (1992-1996).. J Am Vet Med Assoc 2000 Feb 15;216(4):554-8.
    doi: 10.2460/javma.2000.216.554pubmed: 10687012google scholar: lookup
  53. Marr C, Bowen M. Cardiology of the Horse. Elsevier Health Sciences; 2011.
  54. Vitale V. The effects of restriction of movement on the reliability of heart rate variability measurements in the horse (Equus caballus). Journal of Veterinary Behavior: Clinical Applications and Research 2013;8(5):400–403.
    doi: 10.1016/j.jveb.2013.02.003google scholar: lookup
  55. Scheffer CJ, Sloett van Oldruitenborgh-Oosterbaan MM. Computerized ECG recording in horses during a standardized exercise test.. Vet Q 1996 Mar;18(1):2-7.
    doi: 10.1080/01652176.1996.9694601pubmed: 8833603google scholar: lookup
  56. Verheyen T. Electrocardiography in horses, part 1: how to make a good recording. Vlaams Diergeneeskundig Tijdschrift 2010;79(5):331–336.
  57. Trachsel DS, Bitschnau C, Waldern N, Weishaupt MA, Schwarzwald CC. Observer agreement for detection of cardiac arrhythmias on telemetric ECG recordings obtained at rest, during and after exercise in 10 Warmblood horses.. Equine Vet J Suppl 2010 Nov;(38):208-15.
    doi: 10.1111/j.2042-3306.2010.00172.xpubmed: 21059008google scholar: lookup
  58. Buhl R, Meldgaard C, Barbesgaard L. Cardiac arrhythmias in clinically healthy showjumping horses.. Equine Vet J Suppl 2010 Nov;(38):196-201.
    doi: 10.1111/j.2042-3306.2010.00185.xpubmed: 21059006google scholar: lookup
  59. Cottin F, Barrey E, Lopes P, Billat V. Effect of repeated exercise and recovery on heart rate variability in elite trotting horses during high intensity interval training.. Equine Vet J Suppl 2006 Aug;(36):204-9.
    doi: 10.1111/j.2042-3306.2006.tb05540.xpubmed: 17402419google scholar: lookup
  60. Ryan N, Marr CM, McGladdery AJ. Survey of cardiac arrhythmias during submaximal and maximal exercise in Thoroughbred racehorses.. Equine Vet J 2005 May;37(3):265-8.
    doi: 10.2746/0425164054530713pubmed: 15892238google scholar: lookup
  61. Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases.. IEEE Trans Biomed Eng 2004 Apr;51(4):570-81.
    doi: 10.1109/TBME.2003.821031pubmed: 15072211google scholar: lookup
  62. Laguna P. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Computers in Cardiology 1997. IEEE; 1997. p. 673–676.
  63. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.. Circulation 2000 Jun 13;101(23):E215-20.
    doi: 10.1161/01.CIR.101.23.e215pubmed: 10851218google scholar: lookup
  64. Laguna P, Jané R, Caminal P. Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database.. Comput Biomed Res 1994 Feb;27(1):45-60.
    doi: 10.1006/cbmr.1994.1006pubmed: 8004942google scholar: lookup
  65. Maronna R. Robust statistics. John Wiley & Sons, Chichester: ISBN; 2006.
  66. Holschneider M. A real-time algorithm for signal analysis with the help of the wavelet transform. In: Wavelets. Springer; 1990. p. 286–297.
  67. Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1989;11(7):674–693.
    doi: 10.1109/34.192463google scholar: lookup
  68. Vetterli M. A theory of multirate filter banks. Acoustics, Speech and Signal Processing, IEEE Transactions on 1987;35(3):356–372.
    doi: 10.1109/TASSP.1987.1165137google scholar: lookup
  69. Li S, Lin J. The optimal de-noising algorithm for ECG using stationary wavelet transform. In: Computer Science and Information Engineering, 2009 WRI World Congress on. vol. 6. IEEE; 2009. p. 469–473.
  70. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement.. Lancet 1986 Feb 8;1(8476):307-10.
    doi: 10.1016/S0140-6736(86)90837-8pubmed: 2868172google scholar: lookup
  71. Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading.. Lancet 1995 Oct 21;346(8982):1085-7.
    doi: 10.1016/S0140-6736(95)91748-9pubmed: 7564793google scholar: lookup
  72. Lanatà A, Scilingo EP, Nardini E, Loriga G, Paradiso R, De-Rossi D. Comparative evaluation of susceptibility to motion artifact in different wearable systems for monitoring respiratory rate.. IEEE Trans Inf Technol Biomed 2010 Mar;14(2):378-86.
    doi: 10.1109/TITB.2009.2037614pubmed: 20007035google scholar: lookup
  73. Shah AP, Rubin SA. Errors in the computerized electrocardiogram interpretation of cardiac rhythm.. J Electrocardiol 2007 Sep-Oct;40(5):385-90.
    doi: 10.1016/j.jelectrocard.2007.03.008pubmed: 17531257google scholar: lookup

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  1. Xiao W, Sun C, Shen L, Feng Y, Liu M, Wu Y, Liu X, Wu T, Peng X, Guo H. A movable unshielded magnetocardiography system. Sci Adv 2023 Mar 29;9(13):eadg1746.
    doi: 10.1126/sciadv.adg1746pubmed: 36989361google scholar: lookup
  2. Kang Y, Choi S, Koo C, Joung Y. Development and Optimization of Silicon-Dioxide-Coated Capacitive Electrode for Ambulatory ECG Measurement System. Sensors (Basel) 2022 Nov 1;22(21).
    doi: 10.3390/s22218388pubmed: 36366085google scholar: lookup
  3. Nigusse AB, Mengistie DA, Malengier B, Tseghai GB, Langenhove LV. Wearable Smart Textiles for Long-Term Electrocardiography Monitoring-A Review. Sensors (Basel) 2021 Jun 17;21(12).
    doi: 10.3390/s21124174pubmed: 34204577google scholar: lookup
  4. Van Steenkiste G, van Loon G, Crevecoeur G. Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture. Sci Rep 2020 Jan 13;10(1):186.
    doi: 10.1038/s41598-019-57025-2pubmed: 31932667google scholar: lookup
  5. Redaelli V, Luzi F, Mazzola S, Bariffi GD, Zappaterra M, Nanni Costa L, Padalino B. The Use of Infrared Thermography (IRT) as Stress Indicator in Horses Trained for Endurance: A Pilot Study. Animals (Basel) 2019 Mar 7;9(3).
    doi: 10.3390/ani9030084pubmed: 30866503google scholar: lookup
  6. Ghaleb FA, Kamat MB, Salleh M, Rohani MF, Abd Razak S. Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter. PLoS One 2018;13(11):e0207176.
    doi: 10.1371/journal.pone.0207176pubmed: 30457996google scholar: lookup
  7. Baragli P, Vitale V, Sighieri C, Lanata A, Palagi E, Reddon AR. Consistency and flexibility in solving spatial tasks: different horses show different cognitive styles. Sci Rep 2017 Nov 29;7(1):16557.
    doi: 10.1038/s41598-017-16729-zpubmed: 29185468google scholar: lookup
  8. Galotti A, Eisersiö M, Yngvesson J, Lanatà A, Maglieri V, Palagi E, Baragli P. Rein tension and heart rate variability in horses: an experiment on experience. J Anim Sci 2025 Jan 4;103.
    doi: 10.1093/jas/skaf146pubmed: 40331242google scholar: lookup
  9. Veske-Lepp P, Van Steenkiste G, Thienpondt S, Cools J, De Pauw H, Bossuyt F. Development of 3D-Formed Textile-Based Electrodes with Flexible Interconnect Ribbon. Sensors (Basel) 2025 Jan 12;25(2).
    doi: 10.3390/s25020414pubmed: 39860784google scholar: lookup
  10. McCrae P, Spong H, Mahnam A, Bashura Y, Pearson W. The impact of skin preparation method on electrocardiogram quality in horses. Can Vet J 2024 Mar;65(3):245-249.
    pubmed: 38434162
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