Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Huiying Cui; Zhongyi Wang; Bin Yu; Fangfang Jiang; Ning Geng; Yongchun Li; Lisheng Xu; Dingchang Zheng; Biyong Zhang; Peilin Lu; Stephen E. Greenwald

doi:10.3390/s22062423

Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Huiying Cui, Zhongyi Wang, Bin Yu, Fangfang Jiang, Ning Geng, Yongchun Li, Lisheng Xu, Dingchang Zheng, Biyong Zhang, Peilin Lu, Stephen E. Greenwald

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Abstract

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

Original language	English
Article number	2423
Number of pages	21
Journal	Sensors
Volume	22
Issue number	6
DOIs	https://doi.org/10.3390/s22062423
Publication status	Published - 21 Mar 2022

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Funder

This research was funded by the National Natural Science Foundation of China (No. 61773110), the Natural Science Foundation of Liaoning Province (No. 20170540312 and No. 2021-YGJC-14), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), the Fundamental Research Funds for the Central Universities (No. N2119008), and the National Key Research and Development Program of China (No. 2017YFC1307600). This research is also supported by the Shenyang Science and Technology Plan Fund (No. 21-104-1-24, No. 20-201-4-10, and No. 201375), the Member Program on Neusoft Research of Intelligent Healthcare Technology, Co., Ltd. (No. MCMP062002). This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant (No. LY20H090001).

Funding

This research was funded by the National Natural Science Foundation of China (No. 61773110), the Natural Science Foundation of Liaoning Province (No. 20170540312 and No. 2021-YGJC-14), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), the Fundamental Research Funds for the Central Universities (No. N2119008), and the National Key Research and Development Program of China (No. 2017YFC1307600). This research is also supported by the Shenyang Science and Technology Plan Fund (No. 21-104-1-24, No. 20-201-4-10, and No. 201375), the Member Program on Neusoft Research of Intelligent Healthcare Technology, Co., Ltd. (No. MCMP062002). This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant (No. LY20H090001).

Funders	Funder number
Intelligent Healthcare Technology, Co., Ltd.	MCMP062002
Shenyang Science and Technology Plan Fund	201375, 21-104-1-24, 20-201-4-10
National Natural Science Foundation of China	61773110
Natural Science Foundation of Zhejiang Province	LY20H090001
Natural Science Foundation of Liaoning Province	20170540312, 2021-YGJC-14
Department of Education of Liaoning Province	LJKZ00042021
National Key Research and Development Program of China	2017YFC1307600
Fundamental Research Funds for the Central Universities	N2119008

Keywords

Ballistocardiography
Electrocardiography
Heartrate variability
Hrv analysis
Pulse wave

ASJC Scopus subject areas

Analytical Chemistry
Information Systems
Instrumentation
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering
Biochemistry

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PublishedFinal published version, 7.82 MBLicence: CC BY
sensors-22-02423-v3Final published version, 7.44 MB

Cite this

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title = "Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals",

abstract = "Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin{\textquoteright}s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.",

keywords = "Ballistocardiography, Electrocardiography, Heartrate variability, Hrv analysis, Pulse wave",

author = "Huiying Cui and Zhongyi Wang and Bin Yu and Fangfang Jiang and Ning Geng and Yongchun Li and Lisheng Xu and Dingchang Zheng and Biyong Zhang and Peilin Lu and Greenwald, {Stephen E.}",

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AU - Cui, Huiying

AU - Wang, Zhongyi

AU - Yu, Bin

AU - Jiang, Fangfang

AU - Geng, Ning

AU - Li, Yongchun

AU - Xu, Lisheng

AU - Zheng, Dingchang

AU - Zhang, Biyong

AU - Lu, Peilin

AU - Greenwald, Stephen E.

N1 - This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2022/3/21

Y1 - 2022/3/21

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AB - Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

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KW - Pulse wave

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ER -

Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Abstract

Bibliographical note

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Funding

Keywords

ASJC Scopus subject areas

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