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Estimation of a Menstrual Cycle by Covariance Stationary-Time Series Analysis on the Basal Body Temperatures

Jeong-Hwan Kim 1, Sang-Eun Park 1, Gyeo-Wun Jeung 1, Jeong-Whan Lee 1, Heejung-Choi 2, and Kyeong-Seop Kim 3
1. School of Biomedical Engineering, Konkuk University, Chungju, Korea
2. Department of Nursing, Konkuk University, Chungju, Korea
3. School of Biomedical Engineering, Konkuk University, Chungju, Korea
Research Institute of Biomedical Eng., Konkuk University, Chungju, Korea
Abstract—The daily recording of a woman’s basal body temperature is the most easy and convenient method for estimating ovulation period especially by resolving the biphasic trend in the temperature readings. For this study, a woman’s basal body temperature data are collected on a daily basis and interpreted as a time series-data to discern the periodicity in terms of a menstrual cycle. A new method of detecting the cycle is proposed with estimating the sample autocorrelation function as a time-domain decisive feature and with computing its power spectral density by periodogram in a frequency-domain aspect.

Index Terms—basal body temperature, menstrual cycle, ovulation, discrete fourier transform, Autocorrelation, Power Spectrum, Time series, periodogram

Cite: Jeong-Hwan Kim, Sang-Eun Park, Gyeo-Wun Jeung, Jeong-Whan Lee, Heejung-Choi, and Kyeong-Seop Kim, "Estimation of a Menstrual Cycle by Covariance Stationary-Time Series Analysis on the Basal Body Temperatures," Journal of Medical and Bioengineering, Vol. 5, No. 1, pp. 63-66, February 2016. Doi: 10.12720/jomb.5.1.63-66
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