TY - JOUR
T1 - Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics
AU - Sides, Krystal
AU - Kilungeja, Grentina
AU - Tapia, Matthew
AU - Kreidl, Patrick
AU - Brinkmann, Benjamin H.
AU - Nasseri, Mona
N1 - Publisher Copyright:
Copyright © 2023 Sides, Kilungeja, Tapia, Kreidl, Brinkmann and Nasseri.
PY - 2023
Y1 - 2023
N2 - 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 (Formula presented.) 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 (Formula presented.) 0.05). There was a significant difference between ovulating and non-ovulating cycles (p (Formula presented.) 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 (μS), respectively.
AB - 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 (Formula presented.) 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 (Formula presented.) 0.05). There was a significant difference between ovulating and non-ovulating cycles (p (Formula presented.) 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 (μS), respectively.
KW - autoregressive integrated moving average
KW - circular statistical analysis
KW - follicular phase
KW - luteal phase
KW - menstrual cycles
KW - ovulating/non-ovulating
KW - physiological signal processing
KW - wearable sensor
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U2 - 10.3389/fnetp.2023.1227228
DO - 10.3389/fnetp.2023.1227228
M3 - Article
AN - SCOPUS:85178074848
SN - 2674-0109
VL - 3
JO - Frontiers in Network Physiology
JF - Frontiers in Network Physiology
M1 - 1227228
ER -