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Basal body temperature curves and fitting with periodic smoothing splines

(2019) In Master's Theses in Mathematica Sciences FMAM05 20182
Mathematics (Faculty of Engineering)
Abstract (Swedish)
Fertility tracking phone apps are widely used by women today, in order to both achieve or prevent conception. Due to the health risks of hormone based contraceptions, many women opt for non-invasive methods, such as fertility tracking. Most of the algorithms for tracking fertility use parameters such as the basal body temperature (BBT), cycle length and days of menstruation.
The widely used "coverline" method is based on daily charting of the BBT in order to identify a sudden temperature increase following ovulation. This method can only find ovulation after it has occurred and has been used as a contraception method for centuries. Our goal is to use curve fitting of the basal body temperature in order to model fertility during the whole... (More)
Fertility tracking phone apps are widely used by women today, in order to both achieve or prevent conception. Due to the health risks of hormone based contraceptions, many women opt for non-invasive methods, such as fertility tracking. Most of the algorithms for tracking fertility use parameters such as the basal body temperature (BBT), cycle length and days of menstruation.
The widely used "coverline" method is based on daily charting of the BBT in order to identify a sudden temperature increase following ovulation. This method can only find ovulation after it has occurred and has been used as a contraception method for centuries. Our goal is to use curve fitting of the basal body temperature in order to model fertility during the whole cycle, and hence predict future ovulation. The method uses cubic periodic smoothing splines on a data set consisting of daily temperatures from 1151 women. The data is anonymized and was provided by the fertility tracking company Natural Cycles.
We optimize the parameters of the smoothing spline algorithm through cross validation and statistical analysis with the given data. We then correlate the periodicity of the fitted curve with the day of ovulation, through the use of data containing results from ovulation tests. We finally use the splines to model future cycles and predict ovulation.
We conclude that this method could be used for increasing accuracy in existing fertility tracking algorithms. (Less)
Please use this url to cite or link to this publication:
author
supervisor
organization
course
FMAM05 20182
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Basal Body Temperature, Ovulation, Fertility Prediction, Natural Cycles, Fertility Tracking, Smoothing Splines, Cubic, Periodic Smoothing Splines, Curve Fitting, BBT
publication/series
Master's Theses in Mathematica Sciences
report number
LUTFMA-3374-2019
ISSN
1404-6342
other publication id
2019:E2
language
English
id
8973067
date added to LUP
2019-06-05 13:30:14
date last changed
2019-06-12 10:58:31
```@misc{8973067,
abstract     = {Fertility tracking phone apps are widely used by women today, in order to both achieve or prevent conception. Due to the health risks of hormone based contraceptions, many women opt for non-invasive methods, such as fertility tracking. Most of the algorithms for tracking fertility use parameters such as the basal body temperature (BBT), cycle length and days of menstruation.
The widely used "coverline" method is based on daily charting of the BBT in order to identify a sudden temperature increase following ovulation. This method can only find ovulation after it has occurred and has been used as a contraception method for centuries. Our goal is to use curve fitting of the basal body temperature in order to model fertility during the whole cycle, and hence predict future ovulation. The method uses cubic periodic smoothing splines on a data set consisting of daily temperatures from 1151 women. The data is anonymized and was provided by the fertility tracking company Natural Cycles.
We optimize the parameters of the smoothing spline algorithm through cross validation and statistical analysis with the given data. We then correlate the periodicity of the fitted curve with the day of ovulation, through the use of data containing results from ovulation tests. We finally use the splines to model future cycles and predict ovulation.
We conclude that this method could be used for increasing accuracy in existing fertility tracking algorithms.},
author       = {Odlen, Ingrid},
issn         = {1404-6342},
keyword      = {Basal Body Temperature,Ovulation,Fertility Prediction,Natural Cycles,Fertility Tracking,Smoothing Splines,Cubic,Periodic Smoothing Splines,Curve Fitting,BBT},
language     = {eng},
note         = {Student Paper},
series       = {Master's Theses in Mathematica Sciences},
title        = {Basal body temperature curves and fitting with periodic smoothing splines},
year         = {2019},
}

```