Sampling rate and heart rate variability: On metrics and health outcomes

Article Computer Science, Interdisciplinary Applications

Insufficient sampling frequencies skew heart rate variability estimates: Implications for extracting heart rate metrics from neuroimaging and physiological data

Joel S. Burma et al.

Summary: This study investigated the impact of sampling frequency on the validity and reliability of deriving cardiac metrics. Results showed that downsampling frequencies of >= 50 and >= 90 Hz produced highly robust measures, while lower frequencies resulted in wider variability and less accuracy. Further research is needed to determine the minimum sampling frequency required for obtaining valid heart rate and HRV metrics from pulsatile waveforms.

JOURNAL OF BIOMEDICAL INFORMATICS (2021)

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Article Pediatrics

Fetal Cardiovascular Decompensation During Labor Predicted From the Individual Heart Rate Tracing: A Machine Learning Approach in Near-Term Fetal Sheep Model

Nathan Gold et al.

Summary: This study aimed to predict the individual timing of cardiovascular decompensation in fetuses using RMSSD data. Results showed that RMSSD values were highly non-stationary and could accurately identify individual time points of cardiovascular decompensation, despite some false alarms at a 4 Hz sampling rate.

FRONTIERS IN PEDIATRICS (2021)

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Article Pediatrics

Distance to Healthy Metabolic and Cardiovascular Dynamics From Fetal Heart Rate Scale-Dependent Features in Pregnant Sheep Model of Human Labor Predicts the Evolution of Acidemia and Cardiovascular Decompensation

Stephane G. Roux et al.

Summary: This study aims to investigate the relationship between fetal heart rate (FHR) temporal dynamics and fetal health, using sheep fetuses as an animal model. The results show that FHR variability features are sensitive for early detection of cardiovascular decompensation, correlated with metabolites and pH, helpful for early acidosis detection. The importance of coarse time scales (2.5-8 s) in FHR dynamics and the introduction of a new self-referencing metric for predicting the occurrence of clinically significant degradation of fetal health reserve are highlighted.

FRONTIERS IN PEDIATRICS (2021)

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Article Psychology, Mathematical

NeuroKit2: A Python toolbox for neurophysiological signal processing

Dominique Makowski et al.

Summary: NeuroKit2 is an open-source Python package designed for neurophysiological signal processing, offering a comprehensive suite of processing routines for various bodily signals. It combines high-level functions with validated pipelines for efficient data processing, focusing on transparency, reproducibility, innovation, and accessibility for users of all levels.

BEHAVIOR RESEARCH METHODS (2021)

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Letter Physiology