Detection of Atrial Fibrillation in Short-Lead Electrocardiogram Recordings Obtained using a Smart Scale


Atrial fibrillation (AF) is the type of arrhythmia that raises possibility of severe health problems such as heart failure and stroke and it is known that a major risk factor of AF includes overweight and obesity. Based on this association between such health-related indicators, we propose a smart scale that is capable of measuring weight and electrocardiography (ECG) simultaneously. The scale was developed using Arduino Uno, a Wheatstone bridge load cell, and ECG sensors. The ECG signals were processed to compute heart rate (in other words, RR interval). The smart scale was evaluated with four healthy volunteers in terms of reliability showing high agreement with a commercial device for ECG monitoring. In addition, it implements Atrial Fibrillation (AF) detection using machine-learning classifiers including a k-Nearest Neighbor (kNN) method, a Decision Tree (DT), and a Neural Network (NN) on relatively short recordings of ECG obtained while using the scale. The root mean square of the successive differences between heart beats (RMSSD) and the Shannon entropy of the RR interval (ECG features) were extracted from ECG signals for AF detection. Performance of AF detection was tested with patients who were treated at a Cardiology Center after balancing data by applying over- and under-sampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and the Tomek Link (T-Link) algorithm. After addressing the data imbalance, the AF detection performance of each classifier (kNN, DT, and NNs) was 98.9%, 97.8%, and 98.9% respectively. This work has successfully demonstrated weight and cardio activity monitoring features while using a scale that may help keep the records of sensitive health related indexes on a daily basis.

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  1. 1.

    January CT et al (2014) 2014 AHA/ACC/HRS Guideline for the management of patients with atrial fibrillation: executive summary. A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society 64(21):2246–2280

    Google Scholar 

  2. 2.

    Hajjar I, Kotchen TA (2003) Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988–2000. JAMA 290(2):199–206, 7

  3. 3.

    PA Wolf, RD Abbott, WB Kannel (1991) Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke 22(8):983–988, 8.

  4. 4.

    Middeldorp ME et al (2018) PREVEntion and regReSsive effect of weight-loss and risk factor modification on atrial fibrillation: the REVERSE-AF study. EP Europace 20(12):1929–1935

    Article  Google Scholar 

  5. 5.

    Nalliah CJ, Sanders P, Kottkamp H, Kalman JM (2015) The role of obesity in atrial fibrillation. Eur Heart J 37(20):1565–1572

    Article  Google Scholar 

  6. 6.

    Abed HS et al (2013) Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation: a randomized clinical trial. JAMA 310(19):2050–2060

    Article  Google Scholar 

  7. 7.

    Altmann D, Andler K, Bruland K, Nakicenovic N, Nordmann A (2004) Converging technologies—shaping the future of european societies

  8. 8.

    Ramkumar S, Nerlekar N, D’Souza D, Pol DJ, Kalman JM, Marwick TH (2018) Atrial fibrillation detection using single lead portable electrocardiographic monitoring: a systematic review and meta-analysis. BMJ Open 8(9):9

    Article  Google Scholar 

  9. 9.

    Freedman B (2016) Screening for Atrial Fibrillation Using a Smartphone: Is There an App for That? J Am Heart Assoc 5(7):e004000

    Article  Google Scholar 

  10. 10.

    Lahdenoja O, Hurnanen T, Iftikhar Z, Nieminen S, Jnuutila T, Saraste A, Kiviniemi T, Vasankari T, Airaksinen J, Pankaala M, Koivisto T Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone. IEEE Journal of Biomedical and Health Informatics 22(1):108–118, 1 2018.

  11. 11.

    Taji B, Chan ADC, Shirmohammadi S False alarm reduction in atrial fibrillation detection using deep belief networks. IEEE Transactions on Instrumentation and Measurement 67(5):1124–1131, 5 2018.

  12. 12.

    Gotlibovych I, Crawfold S, Goyal D, Kerem Y, Benaron D, Yilmaz D, Marcus G, Li Y End-to-end deep learning from raw sensor data: atrial fibrillation detection using wearables. arXiv:1807.10707 [cs, stat], 7 2018.

  13. 13.

    Aliamiri A, Shen Y Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor. IEEE EMBS International Conference on Biomedical Health Informatics (BHI 2018), pp 442–445, 2018.

  14. 14.

    Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

    Article  Google Scholar 

  15. 15.

    Breiman L, Friedman J, Stone CJ, Olshen RA, Classification and regression trees. CRC press, 1984.

  16. 16.

    Wang SC Artificial Neural Network, in Interdisciplinary Computing in Java Programming. Boston, MA: Springer US, 2003, pp. 81–100.

  17. 17.

    Lucena SEd, Sampaio DJBS, Mall B, Meyer M, Burkart MA, Keller FV ECG monitoring using Android mobile phone and Bluetooth. In: 2015 IEEE international instrumentation and measurement technology conference (I2MTC) Proceedings, Pisa, 2015.

  18. 18.

    Mena LJ, Félix VG, Ochoa A, Ostos R, González E, Aspuru J, Velarde P, Maestre GE (2018) mobile personal health monitoring for automated classification of electrocardiogram signals in elderly, Comput Mathematical Methods in Medicine

  19. 19.

    K. Lee, S. Kim, H. O. Choi, J. Lee, and Y. Nam, "Analyzing electrocardiogram signals obtained from a nymi band to detect atrial fibrillation," Multimedia Tools and Applications, 2018/12/17 2018.

  20. 20.

    Blood pressure control is at the heart of your health," Withings, [Online]. Available: [Accessed February 2020].

  21. 21.

    "Move ECG," Withings, [Online]. Available: [Accessed February 2020].

  22. 22.

    "Apple Watch Series 5," Apple, [Online]. Available: [Accessed 2020].

  23. 23.

    "Heath on Apple Watch," Apple, [Online]. Available: [Accessed 2020].

  24. 24.

    "Galaxy Watch Active2," SAMSUNG, [online]. Available: [Accessed 2020]

  25. 25.

    "KardiaMobile," AliveCor, [online]. Available: [Accessed 2020]

  26. 26.

    Gonzalez-Landaeta R, Casas O, Pallas-Areny R Heart Rate Detection from an Electronic Weighing Scale. In: 2007 29th annual international conference of the IEEE engineering in medicine and biology society, Lyon, 2007, pp 6282-6285

  27. 27.

    Bujnowski A et al. (2018) Smart weighing scale with feet-sampled ECG. In: IECON 2018—44th annual conference of the IEEE industrial electronics society, Washington, DC, 2018, pp 3286-3291

  28. 28.

    "Body Cardio - Wi-Fi Smart Scale with Body Composition & Heart Rate," Withings, [Online]. Available: [Accessed February 2020].

  29. 29.

    Ayers B, Beshaw C, Serrano-Finetti E, Casas O, Pallas-Areny R, Couderc J (2016) Enabling atrial fibrillation detection using a weight scale. In: 2016 computing in cardiology conference (CinC), Vancouver, BC, pp. 969-972

  30. 30.

    Lobodzinski SS, Laks MM (2012) New devices for very long-term ECG monitoring, (in eng). Cardiol J 19(2):210–214

    Article  Google Scholar 

  31. 31.

    Hendrikx T, Rosenqvist M, Wester P, Sandström H, Hörnsten R (2014) Intermittent short ECG recording is more effective than 24-h Holter ECG in detection of arrhythmias, (in eng), BMC Cardiovasc Disord 14:41.

  32. 32.

    S. Ekelof (2001)The genesis of the Wheatstone bridge. Eng Sci Educ J 10(1):37–40, 2.

  33. 33.

    Carbone V, Marafioti V, Oreto G (2014) Changes in QRS morphology during atrial fibrillation: What is the mechanism?, Heart Rhythm 11(5):901–903

  34. 34.

    Jensen MSA, Thomsen JL, Jensen SE, Lauritzen T, Engberg M (2005) Electrocardiogram interpretation in general practice, Fam Pract 22(1):109–113, 2

  35. 35.

    Ho KK et al (1997) Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics, (in eng). Circulation 96(3):842–848

    Article  Google Scholar 

  36. 36.

    Wolf A, Swift JB, Swinney HL, Vastano J (1985)A Determining Lyapunov exponents from a time series, Physica D 16(3):285–317, 7.

  37. 37.

    Larose DT (2005) K-nearest neighbor algorithm. Discovering knowledge in data: an introduction to data mining 90.

  38. 38.

    Barros RC, Basgalupp MP, de Carvalho ACPLF, Freitas AA (2012) A survey of evolutionary algorithms for decision-tree induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(3):291–312, 5.

  39. 39.

    Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP ( 2002) SMOTE: synthetic minority over-sampling technique, 1, 16:321–357, 6.

  40. 40.

    Tomek I (1976) An experiment with the edited nearest-neighbor rule. IEEE Trans Syst Man Cybernet 6:448–452

    MathSciNet  MATH  Google Scholar 

  41. 41.

    Colak C, Karaaslan E, Colak C, Arslan AK, Erdil N, Handling imbalanced class problem for the prediction of atrial fibrillation in obese patient, 2017.

  42. 42.

    Rincón F, Grassi PR, Khaled N, Atienza D, Sciuto D, Automated real-time atrial fibrillation detection on a wearable wireless sensor platform. In: 2012 Annual international conference of the IEEE engineering in medicine and biology society, 28 Aug.-1 Sept. 2012 2012, pp. 2472–2475.

  43. 43.

    Moody G (1983) A new method for detecting atrial fibrillation using RR intervals, Comput Cardiol pp 227–230

  44. 44.

    Shashikumar SP, Shah AJ, Li Q, Clifford GD, Nemati S (2017) A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. In: 2017 IEEE EMBS international conference on biomedical & health informatics (BHI), 16–19 Feb. 2017 2017.

  45. 45.

    Gotlibovych I et al. (2018) End-to-end deep learning from raw sensor data: Atrial fibrillation detection using wearables, arXiv preprint arXiv:1807.10707

  46. 46.

    Xia Y, Wulan N, Wang K, Zhang H (2018) Detecting atrial fibrillation by deep convolutional neural networks. Comput Biol Med 93:84–92

    Article  Google Scholar 

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This research was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government(MOTIE) (P0012724, The Competency Development Program for Industry Specialist) and the Soonchunhyang University Research Fund.

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Correspondence to Yunyoung Nam.

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Lee, K., Kim, JY., Choi, H.O. et al. Detection of Atrial Fibrillation in Short-Lead Electrocardiogram Recordings Obtained using a Smart Scale. J. Electr. Eng. Technol. 16, 1109–1118 (2021).

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