A1 Journal article – refereed
A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

List of Authors: Tadi MJ, Lehtonen E, Hurnanen T, Koskinen J, Eriksson J, Pankaala M, Teras M, Koivisto T
Publication year: 2016
Journal: Physiological Measurement
Journal acronym: PHYSIOL MEAS
Volume number: 37
Issue number: 11
Number of pages: 25
ISSN: 0967-3334

Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismo-cardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and similar or equal to 0.6%), left (99.3%, 98.8% and similar or equal to 0.001%) and right (99.53%, 99.3% and similar or equal to 0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r > 0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.

Last updated on 2019-21-08 at 23:20