1. Computing physiological data metrics

The phys2denoise package provides a set of functions to compute physiological data metrics. The set of supported metrics includes:

  • Cardiac metrics

    • Cardiac phase

    • Heart rate

    • Heart rate variability

    • Heart beat interval

  • Respiratory metrics

    • Respiratory phase

    • Respiratory variance

    • Respiratory pattern variability

    • Envelope

  • Multimodal metrics

    • RETROICOR

All of the metrics computation function definitions, descriptions and references can be found in API.

1.1. Using a Physio object

Physiological data metrics can be easily computed using Physio objects, from the physutils module, on which the physiological data will be loaded.

The following example shows how to compute the respiratory variance time using a Physio object.

from physutils import io
from phys2denoise.metrics.chest_belt import respiratory_variance_time
# peakdet is an example package that provides peak/trough detection for the respiratory signal
from peakdet import operations

# Load the physiological data
sample_rate = 1000
physio = io.load_physio('path/to/physiological/data', fs=sample_rate)

# Peak/trough detection for the respiratory signal, using the peakdet package
physio = operations.peakfind_physio(physio)

# Compute RVT
physio, rvt = respiratory_variance_time(physio)

respiratory_variance_time() returns a tuple with the updated Physio object and the computed respiratory variance time.

peakdet is used in this example as it is also compatible with the Physio object. However, any other peak/trough detection package can be used. In this case, the peak and trough values should be stored in the Physio object manually as follows:

# Store the peak and trough values in the Physio object
physio._metadata["peaks"] = peaks
physio._metadata["troughs"] = troughs

The benefit of using a Physio object other than the encapsulation of all the desired parameters in a single object is the fact that the object retains a history of all the operations performed on it. This allows for easy debugging and reproducibility of the results. For further information refer to the physutils documentation.

1.2. Without using a Physio object

However, if the use of the Physio object from the physutils module is not preferred, the metrics can be also computed without it. The following example shows how to compute the heart rate and the heart rate variability using the phys2denoise package.

from phys2denoise.metrics.chest_belt import respiratory_variance_time

# Given that the respiratory signal is stored in `data`, the peaks in `peaks`, the troughs in `troughs`
# and the sample rate in `sample_rate`
_, rvt = respiratory_variance_time(data, peaks, troughs, sample_rate)