Improved HRV characterization using OCDWT

In this paper an over-complete discrete wavelet transform (OCDWT) algorithm, obtained by blending two wavelet transform implementations that is redundant wavelet transform and the Mallat's multiresolution decomposition, has been proposed to retrieve the time-varying characteristics of HRV under two different postures, supine and standing. The OCDWT algorithm is critically sub-sampled to a given level of decomposition, below which it is then fully sampled. Five subjects were included to investigate posture-related HRV. The results showed that the high frequency fluctuations are larger in supine and get significantly reduced in standing in comparison to low frequency variations. Moreover, the very low frequency heart beat fluctuations during supine were greater than during standing. Further a comparative analysis has also been made between the Mallat's and OCDWT implementation in order to show the superiority of proposed algorithm.