Design of a microcontroller-based real-time heart rate variability measurement system using a low-complexity r-peak detection algorithm

Ying Chieh Wei, Ying Yu Wei, Kai Hsiung Chang, Ling-Sheng Jang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The development of a real-time heart rate variability (HRV) measurement system is reported based on microcontrollers. Previous studies have analyzed autonomic nervous system changes using an offline method to obtain HRV parameters; therefore, researchers cannot monitor the changes as they occur and cannot perform biofeedback. To calculate real-time HRV parameters, we first developed a low-complexity R-peak detection algorithm to determine the RR intervals. This algorithm only highlights the R peak position; thus, it can quickly calculate RR intervals and easily be implemented in a microcontroller. Experimental results show that the rate of heartbeat error of our algorithm is less than 1 bpm. In addition, a high correlation coefficient (r > 0.99) exists between the measured and actual heart rates in the tested range of 20-200 bpm. The HRV parameters of each subject were calculated using Bland-Altman statistical analysis and had a narrow LoA, and all parameters exhibited good correlation (r > 0.91). Thus, the results provide evidence that the system can generate adequately reliable HRV parameters. Importantly, the system can generate real-time HRV parameters; thereby facilitating autonomic nervous system research to elucidate the modulation of and changes in sympathetic and parasympathetic neural activities.

Original languageEnglish
Pages (from-to)274-289
Number of pages16
JournalInstrumentation Science and Technology
Volume41
Issue number3
DOIs
Publication statusPublished - 2013 May 1

Fingerprint

heart rate
Microcontrollers
autonomic nervous system
Neurology
nervous system
biofeedback
Biofeedback
intervals
detection
rate
correlation coefficients
statistical analysis
Statistical methods
parameter
Modulation
modulation

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Chemical Engineering(all)
  • Environmental Science(all)

Cite this

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abstract = "The development of a real-time heart rate variability (HRV) measurement system is reported based on microcontrollers. Previous studies have analyzed autonomic nervous system changes using an offline method to obtain HRV parameters; therefore, researchers cannot monitor the changes as they occur and cannot perform biofeedback. To calculate real-time HRV parameters, we first developed a low-complexity R-peak detection algorithm to determine the RR intervals. This algorithm only highlights the R peak position; thus, it can quickly calculate RR intervals and easily be implemented in a microcontroller. Experimental results show that the rate of heartbeat error of our algorithm is less than 1 bpm. In addition, a high correlation coefficient (r > 0.99) exists between the measured and actual heart rates in the tested range of 20-200 bpm. The HRV parameters of each subject were calculated using Bland-Altman statistical analysis and had a narrow LoA, and all parameters exhibited good correlation (r > 0.91). Thus, the results provide evidence that the system can generate adequately reliable HRV parameters. Importantly, the system can generate real-time HRV parameters; thereby facilitating autonomic nervous system research to elucidate the modulation of and changes in sympathetic and parasympathetic neural activities.",
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Design of a microcontroller-based real-time heart rate variability measurement system using a low-complexity r-peak detection algorithm. / Wei, Ying Chieh; Wei, Ying Yu; Chang, Kai Hsiung; Jang, Ling-Sheng.

In: Instrumentation Science and Technology, Vol. 41, No. 3, 01.05.2013, p. 274-289.

Research output: Contribution to journalArticle

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