A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms. (28th September 2016)

• Record Type: Journal Article
• Authors: Jafari Tadi, Mojtaba; Lehtonen, Eero; Hurnanen, Tero; Koskinen, Juho; Eriksson, Jonas; Pänkäälä, Mikko; Teräs, Mika; Koivisto, Tero
• Is Part Of: Physiological measurement. Volume 37:Number 11(2016:Nov.)
• Page Start: 1885
• Page End: 1909
• Publication Date: 2016-09-28
• Subjects: seismocardiography -- Hilbert transform -- adaptive thresholding -- heartbeat detection -- biomedical signal processing -- heart monitoring; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/0967-3334/37/11/1885
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: 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. Seismocardiography (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 ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃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.