version number: 0.2.3
author: Rhenan Bartels
hrv is a simple Python module that brings the most widely used techniques to work with RRi series and Heart Rate Variability (HRV) analyses without losing the Power and Flexibility of a native Python object and numpy arrays.
To install use pip:
$ pip install hrv
Or clone the repo:
$ git clone https://github.com/rhenanbartels/hrv.git
$ python setup.py install
Once you create an RRi object you will have the power of a native Python iterable object. This means, that you can loop through it using a for loop, get a just a part of the series using native slicing and much more. Let us try it:
from hrv.rri import RRi
rri_list = [800, 810, 815, 750, 753, 905]
rri = RRi(rri_list)
print(rri)
RRi array([800., 810., 815., 750., 753., 905.])print(rri[0])
800.0
print(type(rri[0]))
numpy.float64
print(rri[::2])
RRi array([800., 815., 753.])from hrv.rri import RRi
rri = RRi([800, 810, 815, 750, 753, 905])
rri_ge = rri[rri >= 800]
rri_ge
RRi array([800., 810., 815., 905.])for rri_value in rri:
print(rri_value)
800.0
810.0
815.0
750.0
753.0
905.0When time information is not provided, time array will be created using the cumulative sum of successive RRi. After cumulative sum, the time array is subtracted from the value at t[0] to make it start from 0s
from hrv.rri import RRi
rri_list = [800, 810, 815, 750, 753, 905]
rri = RRi(rri_list)
print(rri.time)
array([0. , 0.81 , 1.625, 2.375, 3.128, 4.033]) # Cumsum of rri values minus t[0]
rri = RRi(rri_list, time=[0, 1, 2, 3, 4, 5])
print(rri.time)
[0. 1. 2. 3. 4. 5.]Some validations are made in the time list/array provided to the RRi class, for instance:
- RRi and time list/array must have the same length;
- Time list/array can not have negative values;
- Time list/array must be monotonic increasing.
With RRi objects you can make math operatins just like a numpy array:
rri
RRi array([800., 810., 815., 750., 753., 905.])
rri * 10
RRi array([8000., 8100., 8150., 7500., 7530., 9050.])
rri + 200
RRi array([1000., 1010., 1015., 950., 953., 1105.])import numpy as np
rri = RRi([800, 810, 815, 750, 753, 905])
sum_rri = np.sum(rri)
print(sum_rri)
4833.0
mean_rri = np.mean(rri)
print(mean_rri)
805.5
std_rri = np.std(rri)
print(std_rri)
51.44171459039833Text files contains a single column with all RRi values. Example of RRi text file
800
810
815
750
from hrv.io import read_from_text
rri = read_from_text('path/to/file.txt')
print(rri)
RRi array([800., 810., 815., 750.])The .hrm files contain the RRi acquired with Polar ®
A complete guide for .hrm files can be found here.
from hrv.io import read_from_hrm
rri = read_from_hrm('path/to/file.hrm')
print(rri)
RRi array([800., 810., 815., 750.])Example of csv file:
800,
810,
815,
750,
from hrv.io import read_from_csv
rri = read_from_csv('path/to/file.csv')
print(rri)
RRi array([800., 810., 815., 750.])When using read_from_csv you can also provide a column containing time information. Let's check it.
800,1
810,2
815,3
750,4
rri = read_from_csv('path/to/file.csv', time_col_index=1)
print(rri)
RRi array([800., 810., 815., 750.])
print(rri.time)
array([0., 1., 2., 3., 4.])The RRi object implements some basic statistics information about its values:
- mean
- median
- standard deviation
- variance
- minimum
- maximum
- amplitude
Some examples:
from hrv.rri import RRi
rri = RRi([800, 810, 815, 750, 753, 905])
# mean
rri.mean()
805.5
# median
rri.median()
805.0You can also have a complete overview of its statistical charactheristic
desc = rri.describe()
desc----------------------------------------
rri hr
----------------------------------------
min 750.00 66.30
max 905.00 80.00
mean 805.50 74.78
var 2646.25 20.85
std 51.44 4.57
median 805.00 74.54
amplitude 155.00 13.70
print(desc['std'])
{'rri': 51.44171459039833, 'hr': 4.5662272355549725}rri = RRi([800, 810, 815, 750, 753, 905])
rri.info()
N Points: 6
Duration: 4.03s
Interpolated: False
Detrended: False
Memory Usage: 0.05KbThe RRi class brings a very easy way to visualize your series:
from hrv.io import read_from_text
rri = read_from_text('path/to/file.txt')
fig, ax = rri.plot(color='k')
rri.hist()
rri.hist(hr=True)
It is also possible to slice RRi series with time range information (in seconds).
In the following example, we are taking a slice that starts at 100s and ends at 200s.
from hrv.io import read_from_text
rri = read_from_text('path/to/file.txt')
rri_range = rri.time_range(start=100, end=200)
fig, ax = rri_range.plot(marker='.')
Time offset can be reset from the RRi series range:
rri_range.reset_time(inplace=True)
from hrv.filters import moving_average
filt_rri = moving_average(rri, order=3)
fig, ax = rri.plot()
filt_rri.plot(ax=ax)
from hrv.filters import moving_median
filt_rri = moving_median(rri, order=3)
fig, ax = rri.plot()
filt_rri.plot(ax=ax)
from hrv.filters import quotient
filt_rri = quotient(rri)
fig, ax = rri.plot()
filt_rri.plot(ax=ax)
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}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}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}