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Heart Rate Detection on Single-Arm ECG by Using Dual-Median Approach

  • Research Article-Electrical Engineering
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Abstract

This study introduces a system to obtain correct heart rate values on single-arm ECG signals. For preparing a dataset, single-arm and dual-arm measurements were taken from the healthy volunteers; then, to increase the diversity of the data and test the limits of the method, some artificial noises are added to the unfiltered raw signals. The signals in the dataset were cleaned with the IIR filter, and then, their significant frequency sub-bands were selected using wavelet transform. The heart rate (HR) determined by the proposed algorithm from the single arm and compared with the ones of the dual-arm signals. The median function, which is used twice in the algorithm, did not only help to overcome the artifacts, but also facilitate to count peaks. According to the results, if the algorithm used in real-time applications, it is enough to wait only for 5 s from the moment that the system first started to find the HR with an MAE around one bpm, and 1 min for an MAE around 0.6 bpm. The most important advantage of this study is the superior performance of the algorithm on finding HR from noisy single-arm ECG signals with the lowest error in the literature and using dual-arm ECG measurement ground truth and as a reference to optimize the parameters of the algorithm.

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References

  1. Baig, M.M.; Gholamhosseini, H.; Connolly, M.J.: A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med. Biol. Eng. Comput. 51, 485–495 (2013)

    Google Scholar 

  2. McAdams, H.P.; Samei, E.; Dobbins, J.; Tourassi, G.D.; Ravin, C.E.: Recent advances in chest radiography. Radiology 241(3), 663–683 (2006)

    Google Scholar 

  3. Gurcan, M.N.; Boucheron, L.E.; Can, A.; Madabhushi, A.; Rajpoot, N.M.; Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)

    Google Scholar 

  4. Fisher, R.S.; van Emde Boas, W.; Blume, W.; Elger, C.; Genton, P.; Lee, P.; Engel Jr., J.: Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46(4), 470–472 (2005)

    Google Scholar 

  5. World Health Organization (WHO): Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. WHO Press, Geneva (2009)

    Google Scholar 

  6. Hunt, S.A.: ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in the adult: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 46(6), e1–e82 (2005)

    MathSciNet  Google Scholar 

  7. Kreatsoulas, C.; Anand, S.S.: The impact of social determinants on cardiovascular disease. Can. J. Cardiol. 26(C), 8C–13C (2010)

    Google Scholar 

  8. Oresko, J.J.; Jin, Z.; Cheng, J.; Huang, S.; Sun, Y.; Duschl, H.; Cheng, A.C.: A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Trans. Inf. Technol. Biomed. 14(3), 734–740 (2010)

    Google Scholar 

  9. Gupta, C.N.; Palaniappan, R.; Swaminathan, S.; Krishnan, S.M.: Neural network classification of homomorphic segmented heart sounds. Appl. Soft Comput. 7(1), 286–297 (2007)

    Google Scholar 

  10. Acharya, U.R.; Joseph, K.P.; Kannathal, N.; Lim, C.M.; Suri, J.S.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44(12), 1031–1051 (2006)

    Google Scholar 

  11. Wang, Y.; Agrafioti, F.; Hatzinakos, D.; Plataniotis, K.N.: Analysis of human electrocardiogram for biometric recognition. EURASIP J. Adv. Signal Process. 19, 1–11 (2008)

    MATH  Google Scholar 

  12. Israel, S.A.; Irvine, J.M.; Cheng, A.; Wiederhold, M.D.; Wiederhold, B.K.: ECG to identify individuals. Pattern Recognit. 38(1), 133–142 (2005)

    Google Scholar 

  13. Lim, C.L.P.; Woo, W.L.; Dlay, S.S.; Wu, D.; Gao, B.: Deep multiview heartwave authentication. IEEE Trans. Ind. Inform. 15(2), 777–786 (2019)

    Google Scholar 

  14. Lim, C.L.P.; Woo, W.L.; Dlay, S.S.; Gao, B.: Heartrate-dependent heartwave biometric identification with thresholding-based GMM–HMM methodology. IEEE Trans. Ind. Inform. 15(1), 45–53 (2019)

    Google Scholar 

  15. Dong, J.G.: The role of heart rate variability in sports physiology. Exp. Ther. Med. 11(5), 1531–1536 (2016)

    Google Scholar 

  16. Mourot, L.; Bouhaddi, M.; Perrey, S.; Cappelle, S.; Henriet, M.T.; Wolf, J.P.; Regnard, J.: Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis. Clin. Physiol. Funct. Imaging 24(1), 10–18 (2004)

    Google Scholar 

  17. Mosenia, A.; Sur-Kolay, S.; Raghunathan, A.; Jha, N.: Wearable medical sensor-based system design: a survey. IEEE Trans. Multi-Scale Comput. Syst. 3(2), 124–138 (2017)

    MATH  Google Scholar 

  18. Lobodzinski, S.S.; Laks, M.M.: New devices for very long-term ECG monitoring. Cardiol. J. 19(2), 210–214 (2012)

    Google Scholar 

  19. Altini, M.; Polito, S.; Penders, J.; Kim, H.; Van Helleputte, N.; Kim, S.; Yazicioglu, F.: An ECG patch combining a customized ultra-low-power ECG SoC with Bluetooth low energy for long term ambulatory monitoring. In: Proceedings of the 2nd Conference on Wireless Health, p. 15 (2011)

  20. Zhang, Q.; Zhou, D.; Zeng, X.: Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 16(1), 23 (2017)

    Google Scholar 

  21. Raj, P.S.; Hatzinakos, D.: Feasibility of single-arm single-lead EKG biometrics. In: 2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), pp. 2525–2529

  22. Yang, H.C.; Chien, T.F.; Liu, S.H.; Chiang, H.H.: Study of Single-Arm Electrode for EKG Measurement Using Flexible Print Circuit. Department of Electrical Engineering, Southern Taiwan University, Taiwan (2011)

    Google Scholar 

  23. Rachim, V.P.; Chung, W.Y.: Wearable noncontact armband for mobile EKG monitoring system. IEEE Trans. Biomed. Circuits Syst. 10(6), 1112–1118 (2016)

    Google Scholar 

  24. Zhang, Q.; Zeng, X.; Hu, W.; Zhou, D.: A machine learning-empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with ear-ECG/PPG. IEEE Access 5, 10547–10561 (2017)

    Google Scholar 

  25. Mahmud, M.S.; Wang, H.; Esfar-E-Alam, A.M.; Fang, H.: A wireless health monitoring system using mobile phone accessories. IEEE Internet Things J. 4(6), 2009–2018 (2017)

    Google Scholar 

  26. Preejith, S.P.; Dhinesh, R.; Joseph, J.; Sivaprakasam, M.: Wearable EKG platform for continuous cardiac monitoring. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 623–626 (2016)

  27. Iskandar, A.A.; Kolla, R.; Schilling, K.; Voelker, W.: A wearable 1-lead necklace EKG for continuous heart rate monitoring. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, pp. 1–4 (2016)

  28. Caldara, M.; Comotti, D.; Gaioni, L.; Pedrana, A.; Pezzoli, M.; Re, V.; Traversi, G.: Wearable sensor system for multi-lead EKG measurement. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, pp. 137–140 (2017)

  29. Okada, M.: A digital filter for the ors complex detection. IEEE Trans. Biomed. Eng. 12, 700–703 (1979)

    Google Scholar 

  30. Pahlm, O.; Sörnmo, L.: Software QRS detection in ambulatory monitoring—a review. Med. Biol. Eng. Comput. 22(4), 289–297 (1984)

    Google Scholar 

  31. Pan, J.; Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)

    Google Scholar 

  32. Kohler, B.U.; Hennig, C.; Orglmeister, R.: The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)

    Google Scholar 

  33. Portet, F.; Hernández, A.I.; Carrault, G.: Evaluation of real-time QRS detection algorithms in variable contexts. Med. Biol. Eng. Comput. 43(3), 379–385 (2005)

    Google Scholar 

  34. Li, C.; Zheng, C.; Tai, C.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)

    Google Scholar 

  35. Martinez, J.P.; Almeida, R.; Olmos, S.; Rocha, A.P.; Laguna, P.: A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)

    Google Scholar 

  36. Lim, C.L.P.; Woo, W.L.; Dlay, S.S.: Enhanced wavelet transformation for feature extraction in highly variated ECG signal. In: Proceedings of 2nd IET International Conference on Intelligent Signal Processing, 1–2 Dec 2015, pp. 1–6. London (2015)

  37. Rajpurkar, P.; Hannun, A.Y.; Haghpanahi, M.; Bourn, C.; Ng, A.Y.: Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint (2017)

  38. Mirvis, D.M.; Goldberger, A.L.: Electrocardiography. In: Bonow, R.O. et al. (eds.) Braunwald’s Heart Disease : A Textbook of Cardiovascular Medicine, 9th edn, pp. 126–167. Elsevier Saunders (2012)

  39. Rodrigues, J.; Belo, D.; Gamboa, H.: Noise detection on ECG based on agglomerative clustering of morphological features. Comput. Biol. Med. 87, 322–334 (2017)

    Google Scholar 

  40. Blanco-Velascoa, M.; Weng, B.; Barner, K.E.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38(1), 1–13 (2008)

    Google Scholar 

  41. Mahesh, S.C.; Agarwala, R.A.; Uplane, M.D.: Suppression of baseline wander and power line interference in ECG using digital IIR filter. Int. J. Circuits Syst. Signal Process. 2, 356–365 (2008)

    Google Scholar 

  42. Sorensen, J.S.; Johannesen, L.; Grove, U.S.L.; Lundhus, K.; Couderc J.P.; Graff, C.: A comparison of IIR and wavelet filtering for noise reduction of the ECG. In: Computing in Cardiology, pp. 489–492. Belfast (2010)

  43. Karthikeyan, P.; Murugappan, M.; Yaacob, S.: ECG signal denoising using wavelet thresholding techniques in human stress assessment. Int. J. Electr. Eng. Inform. 4(2), 306–319 (2012)

    Google Scholar 

  44. Slim, Y.; Raoof, K.: Removal of ECG interference from surface respiratory electromyography. IRBM 31(4), 209–220 (2010)

    Google Scholar 

  45. Moran, R.J.; Campo, P.; Maestu, F.; Reilly, R.B.; Dolan, R.J.; Strange, B.A.: Peak frequency in the theta and alpha bands correlates with human working memory capacity. Front. Hum. Neurosci. 4(200), 1–12 (2010)

    Google Scholar 

  46. Iscan, Z.; Dokur, Z.; Demiralp, T.: Classification of electroencephalogram signals with combined time and frequency features. Expert Syst. Appl. 38(8), 10499–10505 (2011)

    Google Scholar 

  47. Chang, K.M.: Arrhythmia ECG noise reduction by ensemble empirical mode decomposition. Sensors 10(6), 6063–6080 (2010)

    Google Scholar 

  48. Pa, S.; Mitra, M.: Empirical mode decomposition-based ECG enhancement and QRS detection. Comput. Biol. Med. 42(1), 83–92 (2012)

    Google Scholar 

  49. Liu, F.; Lee, D.H.; Lagoa, R.; Kumar, S.: Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med. Mater. Eng. 26(1), S1283–S1290 (2015)

    Google Scholar 

  50. Christlein, V.; Riess, C.; Jordan, J.; Riess, C.; Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)

    Google Scholar 

  51. Krommweh, J.: Tetrolet transform: a new adaptive Haar wavelet algorithm for sparse image representation. J. Vis. Commun. Image Represent. 21(4), 364–374 (2010)

    Google Scholar 

  52. Xiaokui, X.; Wang, G.; Gehrke, J.: Differential privacy via wavelet transforms. IEEE Trans. Knowl. Data Eng. 23(8), 1200–1214 (2011)

    Google Scholar 

  53. Zidelmal, Z.; Amirou, A.; Adnane, M.; Belouchrani, A.: QRS detection based on wavelet coefficients. Comput. Methods Programs Biomed. 107(3), 490–496 (2012)

    Google Scholar 

  54. Subasi, A.; Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010)

    Google Scholar 

  55. Orhan, U.; Hekim, M.; Ozer, M.: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011)

    Google Scholar 

  56. Phinyomark, A.; Nuidod, A.; Phukpattaranont, P.; Limsakul, C.: Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. Elektron. Elektrotech. 122(6), 27–32 (2012)

    Google Scholar 

  57. Khushaba, R.N.; Kodagoda, S.; Lal, S.; Dissanayake, G.: Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans. Biomed. Eng. 58(1), 121–131 (2011)

    Google Scholar 

  58. Johnson, A.E.W.; Behar, J.; Andreotti, F.; Clifford, G.D.; Oster, J.: R-peak estimation using multimodal lead switching. Comput. Cardiol. 41, 281–284 (2014)

    Google Scholar 

  59. Singh, O.; Sunkaria, R.K.: A new approach for identification of heartbeats in multimodal physiological signals. J. Med. Eng. Technol. 42(3), 182–186 (2018)

    Google Scholar 

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Acknowledgements

This study is a part of the research program with Project Number FBA-2015-5346, which is supported by the Cukurova University BAPKOM.

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Authors and Affiliations

  1. Department of Computer Engineering, Çukurova University, Balcalı, 01330, Sarıçam, Adana, Turkey

    Umut Orhan

  2. Department of Biomedical Engineering, Çukurova University, Balcalı, 01330, Sarıçam, Adana, Turkey

    Ahmet Aydin

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  1. Umut Orhan
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  2. Ahmet Aydin
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Correspondence to Ahmet Aydin.

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Orhan, U., Aydin, A. Heart Rate Detection on Single-Arm ECG by Using Dual-Median Approach. Arab J Sci Eng 45, 6573–6581 (2020). https://doi.org/10.1007/s13369-020-04574-8

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  • DOI: https://doi.org/10.1007/s13369-020-04574-8

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