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Found 4 papers, 1 papers with code
A persistent homology approach to heart rate variability analysis with an application to sleep-wake classification
The first step is capturing the shapes of time series from two different aspects -- {the PH's and hence persistence diagrams of its} sub-level set and Taken's lag map.
Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data
However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose.
Sleep-wake classification via quantifying heart rate variability by convolutional neural network
This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring.
Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment
We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects.
