Analysis
========

Time Domain Analysis
####################

.. code-block:: python

    from hrv.classical import time_domain
    from hrv.io import read_from_text

    rri = read_from_text('path/to/file.txt')
    results = time_domain(rri)
    print(results)

    {'mhr': 66.528130159638053,
     'mrri': 912.50302419354841,
     'nn50': 337,
     'pnn50': 33.971774193548384,
     'rmssd': 72.849900286450023,
     'sdnn': 96.990569261440797,
     'sdsd': 46.233829821038042}

Frequency Domain Analysis
#########################
.. code-block:: python

    from hrv.classical import frequency_domain
    from hrv.io import read_from_text

    rri = read_from_text('path/to/file.txt')
    results = frequency_domain(
        rri=rri,
        fs=4.0,
        method='welch',
        interp_method='cubic',
        detrend='linear'
    )
    print(results)

    {'hf': 1874.6342520920668,
     'hfnu': 27.692517001462079,
     'lf': 4894.8271587038234,
     'lf_hf': 2.6110838171452708,
     'lfnu': 72.307482998537921,
     'total_power': 7396.0879278950533,
     'vlf': 626.62651709916258}

Non-linear Analysis
###################

.. code-block:: python

    from hrv.classical import non_linear
    from hrv.io import read_from_text

    rri = read_from_text('path/to/file.txt')
    results = non_linear(rri)
    print(results)

    {'sd1': 51.538501037146382,
     'sd2': 127.11460955437322}

It is also possible to depict the Poincaré Plot, from which SD1 and SD2 are derived:

.. code-block:: python

    rri.poincare_plot()

.. image:: ../figures/poincare.png
   :width: 500 px