TY - JOUR
T1 - Exploring the Hidden Complexity
T2 - Entropy Analysis in Pulse Oximetry of Female Athletes
AU - Cabanas, Ana M.
AU - Fuentes-Guajardo, Macarena
AU - Sáez, Nicolas
AU - Catalán, Davidson D.
AU - Collao-Caiconte, Patricio O.
AU - Martín-Escudero, Pilar
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/1
Y1 - 2024/1
N2 - This study examines the relationship between physiological complexity, as measured by Approximate Entropy (ApEn) and Sample Entropy (SampEn), and fitness levels in female athletes. Our focus is on their association with maximal oxygen consumption ((Formula presented.)). Our findings reveal a complex relationship between entropy metrics and fitness levels, indicating that higher fitness typically, though not invariably, correlates with greater entropy in physiological time series data; however, this is not consistent for all individuals. For Heart Rate (HR), entropy measures suggest stable patterns across fitness categories, while pulse oximetry ((Formula presented.)) data shows greater variability. For instance, the medium fitness group displayed an ApEn(HR) = (Formula presented.) with a coefficient of variation (CV) of 22.17 and ApEn((Formula presented.)) = (Formula presented.) with a CV of 46.08%, compared to the excellent fitness group with ApEn(HR) = (Formula presented.) with a CV of 15.19% and ApEn((Formula presented.)) = (Formula presented.) with a CV of 49.46%, suggesting broader physiological responses among more fit individuals. The larger standard deviations and CVs for (Formula presented.) entropy may indicate the body’s proficient oxygen utilization at higher levels of physical demand. Our findings advocate for combining entropy metrics with wearable sensor technology for improved biomedical analysis and personalized healthcare.
AB - This study examines the relationship between physiological complexity, as measured by Approximate Entropy (ApEn) and Sample Entropy (SampEn), and fitness levels in female athletes. Our focus is on their association with maximal oxygen consumption ((Formula presented.)). Our findings reveal a complex relationship between entropy metrics and fitness levels, indicating that higher fitness typically, though not invariably, correlates with greater entropy in physiological time series data; however, this is not consistent for all individuals. For Heart Rate (HR), entropy measures suggest stable patterns across fitness categories, while pulse oximetry ((Formula presented.)) data shows greater variability. For instance, the medium fitness group displayed an ApEn(HR) = (Formula presented.) with a coefficient of variation (CV) of 22.17 and ApEn((Formula presented.)) = (Formula presented.) with a CV of 46.08%, compared to the excellent fitness group with ApEn(HR) = (Formula presented.) with a CV of 15.19% and ApEn((Formula presented.)) = (Formula presented.) with a CV of 49.46%, suggesting broader physiological responses among more fit individuals. The larger standard deviations and CVs for (Formula presented.) entropy may indicate the body’s proficient oxygen utilization at higher levels of physical demand. Our findings advocate for combining entropy metrics with wearable sensor technology for improved biomedical analysis and personalized healthcare.
KW - VO
KW - approximate entropy
KW - pulse oximeter
KW - sample entropy
KW - women’s response to exercise
UR - https://www.scopus.com/pages/publications/85183107771
U2 - 10.3390/bios14010052
DO - 10.3390/bios14010052
M3 - Article
C2 - 38275305
AN - SCOPUS:85183107771
SN - 2079-6374
VL - 14
JO - Biosensors
JF - Biosensors
IS - 1
M1 - 52
ER -