Browsing by Subject "Polynomial fittings"

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Open Access
Quantifying the effects of blood pressure changes on ballistocardiogram signals
(IEEE, 2017) Javaid, A. Q.; Töreyin, Hakan; İnan, Ö. T.
Quantifying the effects of blood pressure (BP) changes on the ballistocardiogram (BCG) signal shape and features can potentially improve the understanding of this mechanical modality of cardiovascular sensing. BCG, a measure of body movements caused by ejection of blood into the vasculature, has recently re-emerged as a promising method for trending cardiac output and myocardial contractility. Although recent research has shown that the BCG waveform has the potential to be used as a viable proximal timing reference for measuring pulse transit time (PTT) and indirectly BP, it has not been deeply explored for direct estimation of BP. In this paper, we posit that the BCG signal contains features corresponding to changes in BP. To further investigate this hypothesis, BCG waveforms were measured using a modified-weighing scale from 14 subjects performing an isometric handgrip challenge in a seated position. The energy in the latter half of the BCG heartbeat was estimated using polynomial fitting and interpolation methods. The results indicate that an increase in mean arterial pressure (MAP) or a decrease in PTT manifests itself in the form of high amplitude oscillations following the main peak (J-peak) in a BCG heartbeat, thus elucidating the mechanisms behind these oscillations and also potentially improving the breadth of data that can be sensed using BCG signals.
Open Access
Signal denoising by piecewise continuous polynomial fitting
(IEEE, 2010) Yıldız, Aykut; Arıkan, Orhan
Piecewise smooth signal denoising is cast as a non-linear optimization problem in terms of transition boundaries and a parametric smooth signal family. Optimal transition boundaries for a given number of transitions are obtained by using particle swarm optimization. The piecewise smooth section parameters are obtained as the maximum likelihood estimates conditioned on the optimal transition boundaries. The proposed algorithm is extended to the case where the number of transition boundaries are unknown by sequentially increasing number of sections until the residual error is at the level of noise standard deviation. Performance comparison with the state of the art techniques reveals the important advantages of the proposed technique. ©2010 IEEE.