Skip to main content

Advertisement

Springer Nature Link
Log in

Interactive system for optimal position selection of a patch-type R–R interval telemeter

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Heart rate variability (HRV) is an indicator of changes in the interval between successive R-waves on the electrocardiogram (ECG), known as R–R intervals (RRI), caused by autonomic nervous system activity. Measurement of RRI is useful in detecting diseases related to autonomic nervous system activity and predicting seizures. This study proposes an improved heart-rate measurement system that combines a highly accurate, compact, and inexpensive patch-type RRI telemeter with a smartphone application that automatically selects the appropriate measurement position without the need of an expert. To evaluate the measurement accuracy, the RRIs of 10 healthy men and 10 healthy women in supine, sitting, standing, and walking (3 km/h) postures were simultaneously measured using the proposed system and a reference ECG measurement system, and the obtained results were compared. Furthermore, the R-wave detection rate was measured, and Bland–Altman analysis was conducted to analyze the measurement accuracy of the proposed system. The results show that the R-wave detection rate and limit-of-agreement were sufficiently accurate for HRV analysis for 68 and 67 out of the total of 80 epochs, respectively. The fabricated system is expected to enhance the ability of non-experts to conduct ECG measurements and will contribute to improve the quality of healthcare through continuous monitoring at home.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Roche F, Gaspoz JM, Minini P, Pichot V, Duverney D, Costes F, Lacour J-R, Barthélémy J-C (1999) Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation 100:1411–1415. https://doi.org/10.1161/01.CIR.100.13.1411

    Article  Google Scholar 

  2. Abe E, Fujiwara K, Hiraoka T, Yamakawa T, Kano M (2016) Drowsiness detection method by integrating heart rate variability analysis and multivariate statistical process control. SICE J Control Meas Syst Integr 9(1):10–17. https://doi.org/10.9746/jcmsi.9.10

    Article  Google Scholar 

  3. Toshitaka Y, Miho M, Fujiwara K et al (2020) Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability. Sensors 20(14):3987–4003. https://doi.org/10.3390/s20143987

    Article  Google Scholar 

  4. Lou Z, Wang L, Jiang K, et al. (2020) Reviews of wearable healthcare systems: materials, devices and system integration. Mater Sci Eng R: Rep 120:100523, 10.106/j.mser.2019.100523

  5. Indra HM, Patrique F, Roland E et al (2021) Pareto optimization for electrodes placement: compromises between electrophysiological and practical aspects. Med Biol Eng Comput 59:431–447. https://doi.org/10.1007/S11517-021—02319-9

    Article  Google Scholar 

  6. Isabel GT, Jose MS, Rui M et al (2016) Design and evaluation of novel textile wearable systems for the surveillance of vital signals. Sensors 16:1573. https://doi.org/10.3390/s16101573

    Article  Google Scholar 

  7. Yasunori T, Yusaku A, Tomonobu S et al (2015) A smart shirt made with conductive ink and conductive foam for the measurement of electrocardiogram signals with unipolar precordial leads. Fibers 3:463–447. https://doi.org/10.3390/fib3040463

    Article  Google Scholar 

  8. Mohamed M, Goran V, Lazar S et al (2017) Multi-purpose ECG telemetry system. BioMed Eng OnLine. https://doi.org/10.1186/S12938-017-0371-6

    Article  Google Scholar 

  9. Ligtenberg A, Kunt M (1983) A robust-digital QRS-detection algorithm for arrhythmia monitoring. Comput Biomed Res 16:273–286. https://doi.org/10.1016/0010-4809(83)90027-7

    Article  Google Scholar 

  10. Salo MA, Huikuri HV, Seppanen T (2001) Ectopic beats in heart rate variability analysis: effects of editing on time and frequency domain measures. Ann Noninvasive Electrocardiol 6(1):5–17. https://doi.org/10.1111/j.1542-474X.2001.tb00080.x

    Article  Google Scholar 

  11. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology (1996) Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93(5):1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043

    Article  Google Scholar 

  12. Shigeki O, Hiroshi H, Kyouichi M et al (1987) Changes in thoracic potential distribution with respiratory variation (in Japanese). ECG 7(3):295–303. https://doi.org/10.5105/jse.7.295

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by JSPS KAKENHI Grant Number 21H03855 and by the research project for medical engineering collaboration and implementation of artificial intelligence from the Japanese Agency for Medical Research and Development (AMED) Grant Number 21445838.

Author information

Authors and Affiliations

  1. Department of Computer Science and Electronical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan

    Aoi Noguchi & Tomoyuki Takano

  2. Graduate School of Engineering, Nagoya University, Nagoya, Japan

    Koichi Fujiwara

  3. Section of Liaison Psychiatry and Palliative Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan

    Miho Miyajima

  4. Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

    Toshitaka Yamakawa

Authors
  1. Aoi Noguchi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Tomoyuki Takano
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Koichi Fujiwara
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Miho Miyajima
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Toshitaka Yamakawa
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Aoi Noguchi.

Additional information

Publisher's Note

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

This work was presented in part at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25-27, 2022).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noguchi, A., Takano, T., Fujiwara, K. et al. Interactive system for optimal position selection of a patch-type R–R interval telemeter. Artif Life Robotics 28, 226–235 (2023). https://doi.org/10.1007/s10015-022-00815-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-022-00815-1

Keywords