Annotation of seismocardiogram using gyroscopic recordings

This paper introduces a novel setup and algorithm for the automatic annotation of seismocardiographic (SCG) recordings from a MEMS accelerometer. The setup utilizes gyroscopic recordings as a reference for the detection of isovolumic moment (IM) and aortic valve closure (AC) peaks. A method for deriving the rotational kinetic energy waveform is proposed and the coefficients are generated using singular vector decomposition (SVD). Experimental results on 5 subjects at rest indicate an IM detection rate of 96.9% and AC detection rate of 95.6% without envelope filtering. It is suggested that this algorithm is feasible as an ECG-free automatic peak annotation method of SCG recordings from subjects at rest.

[1]  Bozena Kaminska,et al.  Estimation of hemodynamic parameters from seismocardiogram , 2010, 2010 Computing in Cardiology.

[2]  Kouhyar Tavakolian,et al.  Precordial Vibrations Provide Noninvasive Detection of Early-Stage Hemorrhage , 2014, Shock.

[3]  Carlo Menon,et al.  Moving toward automatic and standalone delineation of seismocardiogram signal , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Kouhyar Tavakolian,et al.  Accurate and consistent automatic seismocardiogram annotation without concurrent ECG , 2015, 2015 Computing in Cardiology Conference (CinC).

[5]  Chenxi Yang,et al.  Motion Artifact Cancellation of Seismocardiographic Recording From Moving Subjects , 2016, IEEE Sensors Journal.

[6]  Kouhyar Tavakolian,et al.  Seismocardiography: Past, present and future , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Marco Di Rienzo,et al.  Use of seismocardiogram for the beat-to-beat assessment of the Pulse Transit Time: A pilot study , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Francesco Rizzo,et al.  A textile-based wearable system for the prolonged assessment of cardiac mechanics in daily life , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Kouhyar Tavakolian,et al.  Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations , 2015, IEEE Journal of Biomedical and Health Informatics.

[10]  Gene H. Golub,et al.  Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.

[11]  Zhi-Hong Mao,et al.  Estimation of heart rate from a chest-worn inertial measurement unit , 2015, 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB).

[12]  Pierre-François Migeotte,et al.  Multi-dimensional Kineticardiography a New Approach for Wearable Cardiac Monitoring Through Body Acceleration Recordings , 2016 .

[13]  Kouhyar Tavakolian,et al.  Ballistocardiography and Seismocardiography: A Review of Recent Advances , 2015, IEEE Journal of Biomedical and Health Informatics.

[14]  Nathan Halko,et al.  Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..

[15]  Ari Paasio,et al.  A new algorithm for segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiography , 2015, International Conference on Graphic and Image Processing.