Heart rate variability estimation with joint accelerometer and gyroscope sensing

This paper describes a method for estimation of heart rate (HR) and heart rate variability (HRV) with accelerometers and gyroscopes. We denote this joint seismocardiography (SCG) and gyrocardiography (GCG) approach as SCG/GCG. In principle, SCG which is a well known method measures the linear mechanical movements of the heart and GCG is a new technique which measures angular motion due to the chest micro-vibrations caused by myocardial rotation. As electrocardiography (ECG), they can also be performed in non-invasive manner using a device in contact to subjects skin, for example. Our method extracts HRV parameters based on single-axis and multi-axes autocorrelation analysis (1-AC and 6-AC) of all simultaneously captured SCG/GCG axes. The results of each axes are combined to maintain reliable HR- and HRV. We validate our results with a comparison study between simultaneous ECG and SCG/GCG recordings using a study group of 29 healthy male volunteers. The study provides a promising approach for HRV estimation with modern wearable devices.

[1]  Ari Paasio,et al.  Seismocardiography: Toward heart rate variability (HRV) estimation , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

[2]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[3]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[4]  Shintaro Izumi,et al.  Instantaneous Heart Rate detection using short-time autocorrelation for wearable healthcare systems , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Eero Lehtonen,et al.  Gyrocardiography: A new non-invasive approach in the study of mechanical motions of the heart. Concept, method and initial observations , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  B S BOZHENKO,et al.  [Seismocardiography--a new method in the study of functional conditions of the heart]. , 1961, Terapevticheskii arkhiv.

[7]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[8]  Kouhyar Tavakolian,et al.  Seismocardiograms return valid heart rate variability indices , 2013, Computing in Cardiology 2013.

[9]  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).

[10]  Bozhenko Bs.  [Seismocardiography--a new method in the study of functional conditions of the heart]. , 1961 .

[11]  Steffen Leonhardt,et al.  How speech processing can help with beat-to-beat heart rate estimation in ballistocardiograms , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[12]  P. Ekman,et al.  Autonomic nervous system activity distinguishes among emotions. , 1983, Science.

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

[14]  Hajime Kobayashi,et al.  Weighted autocorrelation for pitch extraction of noisy speech , 2001, IEEE Trans. Speech Audio Process..

[15]  Mika P. Tarvainen,et al.  Kubios HRV - Heart rate variability analysis software , 2014, Comput. Methods Programs Biomed..

[16]  Daniel McDuff,et al.  BioInsights: Extracting personal data from “Still” wearable motion sensors , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[17]  J W Gordon,et al.  Certain Molar Movements of the Human Body produced by the Circulation of the Blood. , 1877, Journal of anatomy and physiology.