Medical Device Integration, Informatics & Heart Rate Variability (HRV) Analysis
The purpose of the attached white paper and the analysis is to study the use of signal processing analysis on heart rate variability data collected via medical device integration to assess the value of this method for advanced informatics & analytics. The selection of the LSP was made because the ability of this algorithm to operate on data that contains gaps is quite important when considering physiologic and other real-world data.
A white paper describing the development of an algorithm written in Visual Basic within MS Excel is contained here: Lomb-Scargle Periodogram
The use of the Lomb-Scargle Periodogram (LSP) for the analysis of biological signal rhythms has been well-documented. [1,2]
“The analysis of time-series of biological data often require special statistical procedures to test for the presence or absence of rhythmic components in noisy data, and to determine the period length of rhythms.” 
“In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill in gaps. An alternative is the Lomb-Scargle method (or least-squares spectral analysis, LSSA), which estimates a frequency spectrum based on a least squares fit of sinusoid.” 
 T. Ruf, “The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series.” Biological Rhythm Research, 1999, Vol. 30, No. 2, pp. 178-201.
 Jozef Púčik, “Heart Rate Variability Spectrum: Physiologic Aliasing and Nonstationarity Considerations.” Trends in Biomedical Engineering. Bratislava, September 16-18, 2009.
 T. Ruf, “The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series”. Biological Rhythm Research, 1999, Vol. 30, No. 2, pp. 178-201.
 Marc in the box, “Lomb-Scargle periodogram for unevenly sampled time series.” Link: http://www.r-bloggers.com/lomb-scargle-periodogram-for-unevenly-sampled-time-series/. Published January 10th, 2013. Accessed 20-April-2015.
This Paper’s Contribution
This paper focuses on the use of the Lomb-Scargle Periodogram to survey Heart Rate Variabililty (HRV). In a preceding analysis, our focus was on the use of signal processing methods, such as the Lomb-Scargle Periodogram, detect power spectral density versus frequency in time-domain signals, such as heart rate variability. The purpose of that analysis was to illustrate the identification of power spectral density associated with time domain signals using a signal processing method known as the Lomb-Scargle Periodogram (LSP). The LSP is deemed a better method for evaluating power spectral density in time-varying signals where there may be missing or data gaps, or irregular measurements. For this reason, it is deemed superior to the discrete Fourier transform for power spectral analysis related to signals processing involving unevenly sample data, which is frequently the case in biology and medicine.
Download the White Paper Here
A copy of the white paper on HRV analysis using Lomb-Scargle Periodogram can be downloaded here. The mathematics of the Lomb-Scargle Periodogram are such that the method can be applied to time signals with gaps or missing data, thereby improving its utility in real-world settings where such gaps may be expected to occur.