Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability

Terukazu Akiyama, Tatsuya Miyazaki, Hiroki Ito, Hirofumi Nogami, Renshi Sawada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We investigated the possibility of applying a MEMS blood flowmeter to heart rate variability (HRV) analysis. We conducted simultaneous measurements of HRV by electrocardiogram and MEMS blood flowmeter. TPP for the MEMS blood flowmeter was defined as the interval between peaks, which were designated as where the first-order differential of the signal changes from negative to positive. TRR (i.e., the R-R interval of the electrocardiogram) and TPP were compared by regression analysis. Autonomic indices transformed by power spectrum analysis were also compared by regression analysis. Fast Fourier transform (FFT) and maximum entropy method (MEM) were employed in the frequency analysis. By FFT analysis, the coefficient of determination for the regression between LF%, HF%, and LF/HF derived by TRR versus TPP was 0.8781, 0.8781, and 0.8946, respectively. By MEM analysis, the coefficient of determination for the regression between LF%, HF%, and LF/HF derived by TRR versus TPP was 0.9649, 0.8026, and 0.9181, respectively. These high correlations suggest that the TPP of the MEMS blood flowmeter is a reliable metric that can be utilized in applications of HRV analysis.

Original languageEnglish
Title of host publicationBIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015
PublisherSciTePress
Pages211-218
Number of pages8
ISBN (Electronic)9789897580697
Publication statusPublished - 2015
Event8th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2015 - Lisbon, Portugal
Duration: Jan 12 2015Jan 15 2015

Other

Other8th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2015
CountryPortugal
CityLisbon
Period1/12/151/15/15

Fingerprint

Flowmeters
Power spectrum
Spectrum analysis
MEMS
Blood
Maximum entropy methods
Electrocardiography
Regression analysis
Fast Fourier transforms

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering

Cite this

Akiyama, T., Miyazaki, T., Ito, H., Nogami, H., & Sawada, R. (2015). Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability. In BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015 (pp. 211-218). SciTePress.

Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability. / Akiyama, Terukazu; Miyazaki, Tatsuya; Ito, Hiroki; Nogami, Hirofumi; Sawada, Renshi.

BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015. SciTePress, 2015. p. 211-218.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Akiyama, T, Miyazaki, T, Ito, H, Nogami, H & Sawada, R 2015, Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability. in BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015. SciTePress, pp. 211-218, 8th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2015, Lisbon, Portugal, 1/12/15.
Akiyama T, Miyazaki T, Ito H, Nogami H, Sawada R. Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability. In BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015. SciTePress. 2015. p. 211-218
Akiyama, Terukazu ; Miyazaki, Tatsuya ; Ito, Hiroki ; Nogami, Hirofumi ; Sawada, Renshi. / Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability. BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015. SciTePress, 2015. pp. 211-218
@inproceedings{da2c2a56534348c49a301b7e94bc9710,
title = "Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability",
abstract = "We investigated the possibility of applying a MEMS blood flowmeter to heart rate variability (HRV) analysis. We conducted simultaneous measurements of HRV by electrocardiogram and MEMS blood flowmeter. TPP for the MEMS blood flowmeter was defined as the interval between peaks, which were designated as where the first-order differential of the signal changes from negative to positive. TRR (i.e., the R-R interval of the electrocardiogram) and TPP were compared by regression analysis. Autonomic indices transformed by power spectrum analysis were also compared by regression analysis. Fast Fourier transform (FFT) and maximum entropy method (MEM) were employed in the frequency analysis. By FFT analysis, the coefficient of determination for the regression between LF{\%}, HF{\%}, and LF/HF derived by TRR versus TPP was 0.8781, 0.8781, and 0.8946, respectively. By MEM analysis, the coefficient of determination for the regression between LF{\%}, HF{\%}, and LF/HF derived by TRR versus TPP was 0.9649, 0.8026, and 0.9181, respectively. These high correlations suggest that the TPP of the MEMS blood flowmeter is a reliable metric that can be utilized in applications of HRV analysis.",
author = "Terukazu Akiyama and Tatsuya Miyazaki and Hiroki Ito and Hirofumi Nogami and Renshi Sawada",
year = "2015",
language = "English",
pages = "211--218",
booktitle = "BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015",
publisher = "SciTePress",

}

TY - GEN

T1 - Application of a MEMS blood flowmeter for power spectrum analysis of heart rate variability

AU - Akiyama, Terukazu

AU - Miyazaki, Tatsuya

AU - Ito, Hiroki

AU - Nogami, Hirofumi

AU - Sawada, Renshi

PY - 2015

Y1 - 2015

N2 - We investigated the possibility of applying a MEMS blood flowmeter to heart rate variability (HRV) analysis. We conducted simultaneous measurements of HRV by electrocardiogram and MEMS blood flowmeter. TPP for the MEMS blood flowmeter was defined as the interval between peaks, which were designated as where the first-order differential of the signal changes from negative to positive. TRR (i.e., the R-R interval of the electrocardiogram) and TPP were compared by regression analysis. Autonomic indices transformed by power spectrum analysis were also compared by regression analysis. Fast Fourier transform (FFT) and maximum entropy method (MEM) were employed in the frequency analysis. By FFT analysis, the coefficient of determination for the regression between LF%, HF%, and LF/HF derived by TRR versus TPP was 0.8781, 0.8781, and 0.8946, respectively. By MEM analysis, the coefficient of determination for the regression between LF%, HF%, and LF/HF derived by TRR versus TPP was 0.9649, 0.8026, and 0.9181, respectively. These high correlations suggest that the TPP of the MEMS blood flowmeter is a reliable metric that can be utilized in applications of HRV analysis.

AB - We investigated the possibility of applying a MEMS blood flowmeter to heart rate variability (HRV) analysis. We conducted simultaneous measurements of HRV by electrocardiogram and MEMS blood flowmeter. TPP for the MEMS blood flowmeter was defined as the interval between peaks, which were designated as where the first-order differential of the signal changes from negative to positive. TRR (i.e., the R-R interval of the electrocardiogram) and TPP were compared by regression analysis. Autonomic indices transformed by power spectrum analysis were also compared by regression analysis. Fast Fourier transform (FFT) and maximum entropy method (MEM) were employed in the frequency analysis. By FFT analysis, the coefficient of determination for the regression between LF%, HF%, and LF/HF derived by TRR versus TPP was 0.8781, 0.8781, and 0.8946, respectively. By MEM analysis, the coefficient of determination for the regression between LF%, HF%, and LF/HF derived by TRR versus TPP was 0.9649, 0.8026, and 0.9181, respectively. These high correlations suggest that the TPP of the MEMS blood flowmeter is a reliable metric that can be utilized in applications of HRV analysis.

UR - http://www.scopus.com/inward/record.url?scp=84938880391&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938880391&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84938880391

SP - 211

EP - 218

BT - BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015

PB - SciTePress

ER -