Literature DB >> 21743126

Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise.

Jae Hyuk Shin1, Su Hwan Hwang, Min Hye Chang, Kwang Suk Park.   

Abstract

We introduced a novel non-constrained technique for estimating heart rate variability (HRV) using a ballistocardiogram (BCG). To assess whether the BCG signal can be used to analyse the cardiac autonomic modulation, HRV parameters derived from the BCG signal (ballistocardiographic HRV, B-HRV) were statistically compared with the HRV parameters from the ECG signal during rest and under two different experimental conditions that induce cardiac autonomic rhythm changes: the Valsalva manoeuvre and static exercise. Time domain, frequency domain and nonlinear analyses were individually performed on 15 healthy subjects to assess whether the BCG can be used to analyse the cardiac autonomic modulation under each condition. For all subjects, the proposed method had averages of relative errors of 5.01 ± 4.72, 5.64 ± 4.83 and 5.98 ± 5.80% for resting, Valsalva and post-exercise sessions, respectively, and the correlation coefficients between the reference (ECG) and proposed (BCG) methods are 0.97, 0.98 and 0.98, for resting, Valsalva and post-exercise sessions, respectively. During cardiac autonomic changes, the B-HRV parameters changed in a pattern that is very similar to the variations in the HRV parameters based on Student's t-test results. In addition, some of the B-HRV parameters changed according to cardiac autonomic rhythms controlled by sympathetic and parasympathetic activities during the experiments. These findings indicate that BCG can provide an accurate and reliable means to evaluate autonomic system activation by HRV in its unconstrained way.

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Year:  2011        PMID: 21743126     DOI: 10.1088/0967-3334/32/8/015

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  7 in total

1.  Non-invasive human vital signs monitoring based on twin-core optical fiber sensors.

Authors:  Fengze Tan; Shuyang Chen; Weimin Lyu; Zhengyong Liu; Changyuan Yu; Chao Lu; Hwa-Yaw Tam
Journal:  Biomed Opt Express       Date:  2019-10-29       Impact factor: 3.732

2.  Relationships between Heart Rate Variability, Sleep Duration, Cortisol and Physical Training in Young Athletes.

Authors:  Christina Mishica; Heikki Kyröläinen; Esa Hynynen; Ari Nummela; Hans-Christer Holmberg; Vesa Linnamo
Journal:  J Sports Sci Med       Date:  2021-10-01       Impact factor: 2.988

3.  Extraction of heart rate variability from smartphone photoplethysmograms.

Authors:  Rong-Chao Peng; Xiao-Lin Zhou; Wan-Hua Lin; Yuan-Ting Zhang
Journal:  Comput Math Methods Med       Date:  2015-01-12       Impact factor: 2.238

4.  Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications.

Authors:  Ghufran Shafiq; Kalyana Chakravarthy Veluvolu
Journal:  Sci Data       Date:  2017-04-25       Impact factor: 6.444

Review 5.  Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise-Basics and Options for Wearable Devices.

Authors:  Melanie Ludwig; Katrin Hoffmann; Stefan Endler; Alexander Asteroth; Josef Wiemeyer
Journal:  Front Physiol       Date:  2018-06-25       Impact factor: 4.566

Review 6.  Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review.

Authors:  Michaela Sidikova; Radek Martinek; Aleksandra Kawala-Sterniuk; Martina Ladrova; Rene Jaros; Lukas Danys; Petr Simonik
Journal:  Sensors (Basel)       Date:  2020-10-06       Impact factor: 3.576

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

Authors:  Huiying Cui; Zhongyi Wang; Bin Yu; Fangfang Jiang; Ning Geng; Yongchun Li; Lisheng Xu; Dingchang Zheng; Biyong Zhang; Peilin Lu; Stephen E Greenwald
Journal:  Sensors (Basel)       Date:  2022-03-21       Impact factor: 3.576

  7 in total

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