Comparison of HRV indices obtained from ECG and SCG signals from CEBS database
Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording a...
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| Published in | Biomedical engineering online Vol. 18; no. 1; pp. 69 - 15 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
01.06.2019
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1475-925X 1475-925X |
| DOI | 10.1186/s12938-019-0687-5 |
Cover
| Abstract | Background
Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals.
Methods
We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features.
Results
Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats (
Se
=
0.930
,
PPV
=
0.934
) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination (
R
2
) to determine goodness of fit to linear model. The highest
R
2
values were obtained for mean interbeat interval (
R
2
=
1.000
for reference algorithm,
R
2
=
0.9249
in the worst case),
PSD
LF
and
PSD
HF
(
R
2
=
1.000
for the best case,
R
2
=
0.9846
for the worst case) and the lowest were obtained for
PSD
VLF
(
R
2
=
0.0009
in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the
R
2
values of pNN50 values in signals p001–p020 and for all analyzed signals.
Conclusions
Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and
PSD
VLF
. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. |
|---|---|
| AbstractList | Abstract Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. Methods We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Results Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ($$\text {Se}=0.930$$ Se=0.930 , $$\text {PPV}=0.934$$ PPV=0.934 ) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ($$R^2$$ R2 ) to determine goodness of fit to linear model. The highest $$R^2$$ R2 values were obtained for mean interbeat interval ($$R^2 = 1.000$$ R2=1.000 for reference algorithm, $$R^2 = 0.9249$$ R2=0.9249 in the worst case), $${{\text{PSD}}}_{{\text{LF}}}$$ PSDLF and $${{\text{PSD}}}_{{\text{HF}}}$$ PSDHF ($$R^2 = 1.000$$ R2=1.000 for the best case, $$R^2 = 0.9846$$ R2=0.9846 for the worst case) and the lowest were obtained for $${{\text{PSD}}}_{{\text{VLF}}}$$ PSDVLF ($$R^2 = 0.0009$$ R2=0.0009 in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the $$R^2$$ R2 values of pNN50 values in signals p001–p020 and for all analyzed signals. Conclusions Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and $${{\text{PSD}}}_{{\text{VLF}}}$$ PSDVLF . Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats (Se=0.930 $$\text {Se}=0.930$$ , PPV=0.934 $$\text {PPV}=0.934$$ ) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination (R2 $$R^2$$ ) to determine goodness of fit to linear model. The highest R2 $$R^2$$ values were obtained for mean interbeat interval (R2=1.000 $$R^2 = 1.000$$ for reference algorithm, R2=0.9249 $$R^2 = 0.9249$$ in the worst case), PSDLF $${{\text{PSD}}}_{{\text{LF}}}$$ and PSDHF $${{\text{PSD}}}_{{\text{HF}}}$$ (R2=1.000 $$R^2 = 1.000$$ for the best case, R2=0.9846 $$R^2 = 0.9846$$ for the worst case) and the lowest were obtained for PSDVLF $${{\text{PSD}}}_{{\text{VLF}}}$$ (R2=0.0009 $$R^2 = 0.0009$$ in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the R2 $$R^2$$ values of pNN50 values in signals p001-p020 and for all analyzed signals. Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and PSDVLF $${{\text{PSD}}}_{{\text{VLF}}}$$ . Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. Methods We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Results Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats (Se=0.930 $$\text {Se}=0.930$$ , PPV=0.934 $$\text {PPV}=0.934$$ ) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination (R2 $$R^2$$ ) to determine goodness of fit to linear model. The highest R2 $$R^2$$ values were obtained for mean interbeat interval (R2=1.000 $$R^2 = 1.000$$ for reference algorithm, R2=0.9249 $$R^2 = 0.9249$$ in the worst case), PSDLF $${{\text{PSD}}}_{{\text{LF}}}$$ and PSDHF $${{\text{PSD}}}_{{\text{HF}}}$$ (R2=1.000 $$R^2 = 1.000$$ for the best case, R2=0.9846 $$R^2 = 0.9846$$ for the worst case) and the lowest were obtained for PSDVLF $${{\text{PSD}}}_{{\text{VLF}}}$$ (R2=0.0009 $$R^2 = 0.0009$$ in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the R2 $$R^2$$ values of pNN50 values in signals p001-p020 and for all analyzed signals. Conclusions Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and PSDVLF $${{\text{PSD}}}_{{\text{VLF}}}$$ . Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. Keywords: Seismocardiography, Heart rate variability, HRV analysis Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals.BACKGROUNDHeart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals.We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features.METHODSWe considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features.Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals.RESULTSBeat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals.Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.CONCLUSIONSCalculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. Methods We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Results Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ( Se = 0.930 , PPV = 0.934 ) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ( R 2 ) to determine goodness of fit to linear model. The highest R 2 values were obtained for mean interbeat interval ( R 2 = 1.000 for reference algorithm, R 2 = 0.9249 in the worst case), PSD LF and PSD HF ( R 2 = 1.000 for the best case, R 2 = 0.9846 for the worst case) and the lowest were obtained for PSD VLF ( R 2 = 0.0009 in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the R 2 values of pNN50 values in signals p001–p020 and for all analyzed signals. Conclusions Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and PSD VLF . Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. Methods We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Results Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats (\(\text {Se}=0.930\), \(\text {PPV}=0.934\)) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination (\(R^2\)) to determine goodness of fit to linear model. The highest \(R^2\) values were obtained for mean interbeat interval (\(R^2 = 1.000\) for reference algorithm, \(R^2 = 0.9249\) in the worst case), \({{\text{PSD}}}_{{\text{LF}}}\) and \({{\text{PSD}}}_{{\text{HF}}}\) (\(R^2 = 1.000\) for the best case, \(R^2 = 0.9846\) for the worst case) and the lowest were obtained for \({{\text{PSD}}}_{{\text{VLF}}}\) (\(R^2 = 0.0009\) in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the \(R^2\) values of pNN50 values in signals p001–p020 and for all analyzed signals. Conclusions Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and \({{\text{PSD}}}_{{\text{VLF}}}\). Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals. Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal. |
| ArticleNumber | 69 |
| Audience | Academic |
| Author | Kostka, Pawel S. Tkacz, Ewaryst J. Siecinski, Szymon |
| Author_xml | – sequence: 1 givenname: Szymon surname: Siecinski fullname: Siecinski, Szymon organization: Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology – sequence: 2 givenname: Ewaryst J. surname: Tkacz fullname: Tkacz, Ewaryst J. email: etkacz@polsl.pl organization: Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, Katowice School of Technology – sequence: 3 givenname: Pawel S. surname: Kostka fullname: Kostka, Pawel S. organization: Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31153383$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_s23031615 crossref_primary_10_3390_s20092663 crossref_primary_10_1080_10641963_2023_2238923 crossref_primary_10_1109_JBHI_2024_3370394 crossref_primary_10_1109_TIM_2023_3346511 crossref_primary_10_3390_math9182243 crossref_primary_10_1109_LSP_2022_3152448 crossref_primary_10_3390_s23042152 crossref_primary_10_3390_s23104684 crossref_primary_10_1016_j_bspc_2023_105484 crossref_primary_10_3390_s20164522 crossref_primary_10_3390_s23198114 |
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| Keywords | HRV analysis Heart rate variability Seismocardiography |
| Language | English |
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| Snippet | Background
Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent... Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there... Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent... Abstract Background Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the... |
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| SubjectTerms | Accelerometers Algorithms Angina pectoris Autonomic nervous system Bands Bioengineering Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Cardiac arrhythmia Cardiography Cardiology Cardiovascular research Comparative analysis Data bases Detectors Echocardiography EKG Electrocardiography Engineering Goodness of fit Heart rate Heart rate variability HRV analysis International conferences Measurement Medical electronics Musical recordings Noise Physiology Recording Seismocardiography Sensors Signal processing Smartphones Very Low Frequencies Vibrations |
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| Title | Comparison of HRV indices obtained from ECG and SCG signals from CEBS database |
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