Design of tree structured matched wavelet for HRV signals of menstrual cycle

Abstract

An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method. The proposed filter banks are used for the decomposition of the HRV signal. For reconstructing the original signal, the tree structure method is used. In this approach, decomposed sub-bands are selected based upon their energy in each sub-band. Thus, instead of using all sub-bands for reconstruction, sub-bands having high energy content are used for the reconstruction of signal. Thus, a lower number of sub-bands are required for reconstruction of the original signal which shows the effectiveness of newly created filter coefficients. Results show that proposed wavelets are able to differentiate HRV variations between phases of the menstrual cycle accurately than standard wavelets.

Keywords:

• Filter coefficients
• heart rate variability
• least mean square
• matched wavelet
• menstrual cycle

Acknowledgements

The authors would like to acknowledge the contribution of subjects involved in this research work. There is no funding source for this research work.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.