A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

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.

Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables

“…ECG signals were also de-noised by a fast Fourier transform (FFT) filter and with the frequency bands of 0.5–45 Hz as described in ref. 41. In addition to the above sensors, in a pre-study examination we considered three other gyroscope sensors, namely Murata SCC1300d02, Bosch BMI 160, and the SONY Xperia Z3 compact smartphone with a built in IMU in order to evaluate the reproducibility of the GCG waveforms.…”

Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography

“…Here, we primarily take advantage of the time–frequency correspondence characteristic of the wavelet transform. Other time–frequency methods include the Hilbert transform, the short time Fourier transform and the quadratic time–frequency distribution [44–47]. These methods are also used to extract instantaneous characteristics of the signal.…”

Recent Advances in Seismocardiography

“…These signal patterns were also found to correlate with the strength of myocardial contractions [17,18] and contain detectable waveform changes during heart disease resolution [19]. After introducing SCG in early 1990s [13], recent studies suggest possible SCG utility for monitoring left-ventricle function, coronary blood flow during balloon angioplasty [9,10,20], heart and breathing rates [21][22][23][24][25][26], and ventricular filling, cardiac valve closure, and ventricular ejection preceding the onset of ischemic symptoms [10].…”