Comparison of HRV indices obtained from ECG and SCG signals from CEBS database

• Main Authors: Szymon Siecinski, Ewaryst J. Tkacz, Pawel S. Kostka
• Format: Article
• Language: English
• Published: BMC 2019-06-01
• Series: BioMedical Engineering OnLine
• Subjects: Seismocardiography; Heart rate variability; HRV analysis
• Online Access: http://link.springer.com/article/10.1186/s12938-019-0687-5

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.