AUTHOR=Sides Krystal , Kilungeja Grentina , Tapia Matthew , Kreidl Patrick , Brinkmann Benjamin H. , Nasseri Mona 

TITLE=Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics

JOURNAL=Frontiers in Network Physiology

VOLUME=3

YEAR=2023

URL=https://www.frontiersin.org/journals/network-physiology/articles/10.3389/fnetp.2023.1227228

DOI=10.3389/fnetp.2023.1227228

ISSN=2674-0109

ABSTRACT=<p>This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p<inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&lt;</mml:mo></mml:math></inline-formula>0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p<inline-formula id="inf2"><mml:math id="m2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&gt;</mml:mo></mml:math></inline-formula>0.05). There was a significant difference between ovulating and non-ovulating cycles (p<inline-formula id="inf3"><mml:math id="m3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>&lt;</mml:mo></mml:math></inline-formula>0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (<italic>μ</italic>S), respectively.</p>