[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

Real-time smart monitoring system for atrial fibrillation pathology

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Agostinelli A, Giuliani C, Burattini L (2014) Extracting a clean ecg from a noisy recording: a new method based on segmented-beat modulation. Comput. Cardiol. 41:49–52

    Google Scholar 

  • Agostinelli A, Sbrollini A, Giuliani C, Fioretti S, Di Nardo F, Burattini L (2016) Segmented beat modulation method for electrocardiogram estimation from noisy recordings. Med Eng Phys 38(6):560–568. https://doi.org/10.1016/j.medengphy.2016.03.011

    Article  Google Scholar 

  • Agostinelli A, Morettini M, Sbrollini A, Maranesi E, Migliorelli L, Di Nardo F, Fioretti S, Burattini L (2017) Carisma 1.0: cardiac risk self-monitoring assessment. Open Sports Sci J 10(1):179–190. https://doi.org/10.2174/1875399x01710010179

  • Aranda JAS, Dias LPS, Barbosa JLV, de Carvalho JV, da Rosa Tavares JE, Tavares MC (2019) Collection and analysis of physiological data in smart environments: a systematic mapping. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01409-9

    Article  Google Scholar 

  • Awolusi I, Marks E, Hallowell M (2018) Wearable technology for personalized construction safety monitoring and trending: review of applicable devices. Autom Constr 85:96–106. https://doi.org/10.1016/j.autcon.2017.10.010

    Article  Google Scholar 

  • Camm A, Malik M, Bigger J, Breithardt G, Cerutti S, Cohen R, Coumel P, Fallen E, Kennedy H, Kleiger R et al (1996) Heart rate variability: standards of measurement, physiological interpretation and clinical use. task force of the european society of cardiology and the north american society of pacing and electrophysiology. Circulation 93(5):1043–1065

    Article  Google Scholar 

  • Chong JW, Esa N, McManus DD, Chon KH (2015) Arrhythmia discrimination using a smart phone. IEEE J Biomed Health Inform 19(3):815–824. https://doi.org/10.1109/JBHI.2015.2418195

    Article  Google Scholar 

  • Desteghe L, Germeys J, Vijgen J, Koopman P, Dilling-Boer D, Schurmans J, Delesie M, Dendale P, Heidbuchel H (2018) Effectiveness and usability of an online tailored education platform for atrial fibrillation patients undergoing a direct current cardioversion or pulmonary vein isolation. Int J Cardiol 272:123–129. https://doi.org/10.1016/j.ijcard.2018.07.065

    Article  Google Scholar 

  • Gambi E, Agostinelli A, Belli A, Burattini L, Cippitelli E, Fioretti S, Pierleoni P, Ricciuti M, Sbrollini A, Spinsante S (2017) Heart rate detection using microsoft kinect: validation and comparison to wearable devices. Sensors (Switzerland) 17(8):1776–1794. https://doi.org/10.3390/s17081776

  • Kotecha D, Chua WW, Fabritz L, Hendriks J, Casadei B, Schotten U, Vardas P, Heidbuchel H, Dean V, Kirchhof P et al (2017) European society of cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers. Ep Euro 20(2):225–233. https://doi.org/10.1093/europace/eux299

    Article  Google Scholar 

  • Kulmala J, Hynynen E (2011) Heart rate variability in chronic and acute stress. University of Jyvaskyla, pp 1–74

  • Lahdenoja O, Hurnanen T, Iftikhar Z, Nieminen S, Knuutila T, Saraste A, Kiviniemi T, Vasankari T, Airaksinen J, Pänkäälä M et al (2017) Atrial fibrillation detection via accelerometer and gyroscope of a smartphone. IEEE J Biomed Health Inform 22(1):108–118. https://doi.org/10.1109/JBHI.2017.2688473

    Article  Google Scholar 

  • Lamiche I, Bin G, Jing Y, Yu Z, Hadid A (2018) A continuous smartphone authentication method based on gait patterns and keystroke dynamics. J Ambient Intell Hum Comput 10(11):4417–4430. https://doi.org/10.1007/s12652-018-1123-6

    Article  Google Scholar 

  • Lee J, Reyes BA, McManus DD, Maitas O, Chon KH (2012) Atrial fibrillation detection using an iphone 4 s. IEEE Trans Biomed Eng 60(1):203–206. https://doi.org/10.1109/TBME.2012.2208112

    Article  Google Scholar 

  • Licht CM, de Geus EJ, Zitman FG, Hoogendijk WJ, van Dyck R, Penninx BW (2008) Association between major depressive disorder and heart rate variability in the netherlands study of depression and anxiety (nesda). Arch Gen Psychiatry 65(12):1358. https://doi.org/10.1001/archpsyc.65.12.1358

    Article  Google Scholar 

  • Lupton D (2013) Quantifying the body: monitoring and measuring health in the age of mhealth technologies. Crit Public Health 23(4):393–403. https://doi.org/10.1080/09581596.2013.794931

    Article  Google Scholar 

  • Malasinghe LP, Ramzan N, Dahal K (2017) Remote patient monitoring: a comprehensive study. J Ambient Intell Hum Comput 10(1):57–76. https://doi.org/10.1007/s12652-017-0598-x

    Article  Google Scholar 

  • Mohebbi M, Ghassemian H (2008) Detection of atrial fibrillation episodes using svm. In: Engineering in medicine and biology Society, 2008. EMBS 2008. 30th annual international conference of the IEEE, IEEE, pp 177–180, https://doi.org/10.1109/iembs.2008.4649119

  • Moody GB, Mark RG (1983) A new method for detecting atrial fibrillation using r-r intervals. Comput Cardiol 10:227–230

    Google Scholar 

  • Moody GB, Mark RG, Goldberger AL (2001) Physionet: a web-based resource for the study of physiologic signals. IEEE Eng Med Biol Mag 20(3):70–75. https://doi.org/10.1109/51.932728

    Article  Google Scholar 

  • Mortelmans C (2016) Validation of a new smartphone application (“FibriCheck”) for the diagnosis of atrial fibrillation in primary care. KU Leuven, Leuven, Belgium, pp 1–20

  • Najarian K, Splinter R (2012) Biomedical signal and image processing, 2nd edn. CRC Press, Taylor and Francis Group, Abingdon

    Google Scholar 

  • Nepi D, Sbrollini A, Agostinelli A, Maranesi E, Morettini M, Di Nardo F, Fioretti S, Pierleoni P, Pernini L, Valenti S, et al. (2016) Validation of the heart-rate signal provided by the zephyr bioharness 3.0. In: Computing in cardiology conference (CinC), 2016, IEEE, pp 361–364, https://doi.org/10.22489/cinc.2016.106-358

  • O’Rourke RA, Fuster V, Alexander R (2003) Hurst. Il Cuore, il manuale. McGraw Hill, New York

    Google Scholar 

  • Oster J, Behar J, Colloca R, Li Q, Li Q, Clifford GD (2013) Open source java-based ecg analysis software and android app for atrial fibrillation screening. In: Computing in cardiology 2013, IEEE, pp 731–734

  • Park J, Lee S, Jeon M (2009) Atrial fibrillation detection by heart rate variability in poincare plot. Biomed Eng Online 38:1–12. https://doi.org/10.1186/1475-925x-8-38

    Article  Google Scholar 

  • Pierleoni P, Pernini L, Belli A, Palma L (2014a) An android-based heart monitoring system for the elderly and for patients with heart disease. Int J Telemed Appl 2014:10. https://doi.org/10.1155/2014/625156

    Article  Google Scholar 

  • Pierleoni P, Pernini L, Belli A, Palma L (2014b) Real-time apnea detection using pressure sensor and tri-axial accelerometer. In: IEEE-EMBS international conference on biomedical and health informatics (BHI), IEEE, pp 513–516, https://doi.org/10.1109/bhi.2014.6864415

  • Pierleoni P, Pernini L, Belli A, Palma L, Valenti S, Paniccia M (2015) Svm-based fall detection method for elderly people using android low-cost smartphones. In: 2015 IEEE sensors applications symposium (SAS), pp 1–5, https://doi.org/10.1109/SAS.2015.7133642

  • Pierleoni P, Pernini L, Palma L, Belli A, Valenti S, Maurizi L, Sabbatini L, Marroni A (2016) An innovative webrtc solution for e-health services. In: e-Health networking, applications and services (Healthcom), 2016 IEEE 18th international conference on, IEEE, pp 1–6, https://doi.org/10.1109/healthcom.2016.7749444

  • Pierleoni P, Belli A, Gentili A, Incipini L, Palma L, Valenti S, Raggiunto S (2018) A eHealth system for atrial fibrillation monitoring. In: Leone A, Caroppo A, Rescio G, Diraco G, Siciliano P (eds) Ambient assisted living. ForItAAL 2018. Lecture notes in electrical engineering, vol 544. Springer, Cham, pp 229–241. https://doi.org/10.1007/978-3-030-05921-7_19

  • Pierleoni P, Belli A, Concetti R, Palma L, Pinti F, Raggiunto S, Sabbatini L, Valenti S, Monteriùs A (2019) Biological age estimation using an ehealth system based on wearable sensors. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01593-8

  • Rozen G, Vaid J, Hosseini SM, Kaadan MI, Rafael A, Roka A, Poh YC, Poh MZ, Heist EK, Ruskin JN (2018) Diagnostic accuracy of a novel mobile phone application for the detection and monitoring of atrial fibrillation. Am J Cardiol 121(10):1187–1191. https://doi.org/10.1016/j.amjcard.2018.01.035

    Article  Google Scholar 

  • Sbrollini A, Mercanti S, Agostinelli A, Morettini M, Di Nardo F, Fioretti S, Burattini L (2017) Athria: a new adaptive threshold identification algorithm for electrocardiographic p waves. In: 2017 Computing in cardiology (CinC), vol 44. IEEE, pp 1–4, https://doi.org/10.22489/cinc.2017.237-179

  • Sbrollini A, Cicchetti K, De Martinis A, Marcantoni I, Morettini M, Burattini L (2018) Automatic identification of atrial fibrillation by spectral analysis of fibrillatory waves. In: 2018 computing in cardiology conference (CinC), vol 45. IEEE, pp 1–4. https://doi.org/10.22489/cinc.2018.066

  • Shaffer F, Ginsberg JP (2017) An overview of heart rate variability metrics and norms. Front Public Health 5:258. https://doi.org/10.3389/fpubh.2017.00258

    Article  Google Scholar 

  • Stewart S, Hart C, Hole D, McMurray J (2001) Population prevalence, incidence, and predictors of atrial fibrillation in the renfrew/paisley study. Heart 86(5):516–521. https://doi.org/10.1136/heart.86.5.516

    Article  Google Scholar 

  • Tsipouras MG, Fotiadis DI, Sideris D (2005) An arrhythmia classification system based on the rr-interval signal. Artifi Intell Med 33(3):237–250. https://doi.org/10.1016/j.artmed.2004.03.007

    Article  Google Scholar 

  • Yang H, Lee J, Lee K, Lee Y, Kim K, Choi H, Kim D (2008) Application for the wearable heart activity monitoring system: analysis of the autonomic function of hrv. In: 30th annual international IEEE EMBS conference, https://doi.org/10.1109/iembs.2008.4649392

  • Yoshimoto M, Izumi S (2019) Recent progress of biomedical processor soc for wearable healthcare application: a review. IEICE Trans Electron 102(4):245–259. https://doi.org/10.1587/transele.2018cdi0001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lorenzo Incipini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pierleoni, P., Belli, A., Gentili, A. et al. Real-time smart monitoring system for atrial fibrillation pathology. J Ambient Intell Human Comput 12, 4461–4469 (2021). https://doi.org/10.1007/s12652-019-01602-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01602-w

Keywords

Navigation