Analyzing seismocardiographic approach for heart rate variability measurement. (July 2021)

• Record Type: Journal Article
• Authors: Choudhary, Tilendra; Das, Mousumi; Sharma, L.N.; Bhuyan, M.K.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Seismocardiogram -- Variational mode decomposition -- AO–AO intervals -- Heart rate variability; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102793
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: For AO peak detection, the performance of the previously proposed MVMD technique is improved by adding a decision-rule-based post-processing scheme. Temporal and spectral HRV parameters are estimated from the tachograms created from consecutive AO–AO intervals. The performance of the proposed method is tested and validated with healthy subjects at different postures and physiological conditions. The performance validation is done with the heart cycles and HRV parameters derived from reference ECG signals. Various statistical measures are used for the validation purposes, including normalized cross correlation (NCC), absolute error, mean error, root mean square error (RMSE), and limits of agreement (LOA). Abstract: As a vital risk stratification tool, heart rate variability (HRV) has the ability to provide early warning signs for many life-threatening diseases. This paper presents a study on reliable cardiac cycle extraction and HRV measurement with a seismocardiographic (SCG) method. Like R-peaks in an ECG, the proposed method relies on peaks corresponding to aortic valve opening (AO) instants in an SCG signal. Due to better reliability and accessibility, the SCG signal is selected for the study. Initially, the prominent AO peaks in an SCG signal are estimated using our previously proposed modified variational mode decomposition (MVMD) based approach. In the present method, the detection performance of AO peaks is improved by employing a decision-rule-based post-processing scheme. Subsequently, tachogram of AO–AO intervals is used for the estimation of HRV parameters. A set of real-time signals collected in various physiological conditions and the SCG signals taken from a publicly available standard database are used to test and validate the proposed method. Experimental results clearly tell that the cardiac intervals obtained from the SCG signal using the proposed method can be used for HRV analysis. Also, the resulted parameters of HRV analysis on ECG and SCG exhibit strong correlation and agreement that shows the effectiveness of the proposed method.