An Enhanced Method to Estimate Heart Rate from Seismocardiography via Ensemble Averaging of Body Movements at Six Degrees of Freedom

Abstract

Continuous cardiac monitoring has been developed to evaluate cardiac activity outside of clinical environments due to the advancement of novel instruments. Seismocardiography (SCG) is one of the vital components that could develop such a monitoring system. Although SCG has been presented with a lower accuracy, this novel cardiac indicator has been steadily proposed over traditional methods such as electrocardiography (ECG). Thus, it is necessary to develop an enhanced method by combining the significant cardiac indicators. In this study, the six-axis signals of accelerometer and gyroscope were measured and integrated by the L2 normalization and multi-dimensional kineticardiography (MKCG) approaches, respectively. The waveforms of accelerometer and gyroscope were standardized and combined via ensemble averaging, and the heart rate was calculated from the dominant frequency. Thirty participants (15 females) were asked to stand or sit in relaxed and aroused conditions. Their SCG was measured during the task. As a result, proposed method showed higher accuracy than traditional SCG methods in all measurement conditions. The three main contributions are as follows: (1) the ensemble averaging enhanced heart rate estimation with the benefits of the six-axis signals; (2) the proposed method was compared with the previous SCG method that employs fewer-axis; and (3) the method was tested in various measurement conditions for a more practical application.

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

“…Sensors 2020, 20, x 3 of 14 ( ) ( ) ( ) n d n y n    (4) The adaptive control unit updates the coefficients using the input vector and the prior estimation error. The detailed information can be described in Equation (5): (5) where ( ) n k is the gain vector that is described in Equation (6): (6) where  is the forgetting factor and ( ) n P is a covariance matrix of the noise which can be updated by Equation (7):…”

“…Motion artifact is usually irregular and it is mixed with the heartbeat signals in the time and frequency domains. The mixture makes it difficult to separate the heartbeat signal from the mixed signal [3][4][5]. Some researchers tried to use multiple sensors to remove the motion artifact from the recorded signals [6][7][8][9].…”

“…The adaptive control unit updates the coefficients using the input vector and the prior estimation error. The detailed information can be described in Equation (5): w(n) =ŵ(n − 1) + k(n)ξ(n) (5) where k(n) is the gain vector that is described in Equation (6): k(n) = λ −1 P(n − 1)u(n) 1 + λ −1 u H P(n − 1)u(n) (6) Sensors 2020, 20, 1596 3 of 14 where λ is the forgetting factor and P(n) is a covariance matrix of the noise which can be updated by Equation (7): P(n) = λ −1 P(n − 1) − λ −1 k(n)u(n)P(n − 1) (7) this paper.…”

Recent Advances in Seismocardiography

“…More studies are needed that compare different filtering methods in clinical and ambulatory settings. [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93] Adaptive filtering Motion artefact removal [88,95] Averaging theory Motion artefact removal [101] Comb filtering Removing respiration noise from radar signal [50] Empirical mode decomposition Baseline wandering, breathing and body movement artefact removal [76,94,95] Independent component analysis Motion artefact removal [102] Median filtering [96] Morphological filtering [95] Polynomial smoothing Motion artefact removal [103] Savitzky-Golay filtering Motion artefact removal [83,103] Wavelet denoising Segmentation of HSs and SCG [64,95,96] Wiener filtering [94] 2.…”

Detection and Analysis of Heartbeats in Seismocardiogram Signals

“…Reliable markers for cardiac activity were also found in gyroscope traces [14], a technique named GyroCardioGraphy (GCG). By combining the six degrees of freedom provided by a gyroscope and an accelerometer, the authors in [15,16] detect different systolic events for HR estimation. Both works, however, leveraged ensemble averaging techniques to improve signal-to-noise ratio.…”