hrv 0.2.0

A python package for heart rate variability analysis

Pythonic package for Heart Rate Variability Analysis
===============================

version number: 0.2.0
author: Rhenan Bartels

Overview
--------

**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](http://www.numpy.org/) arrays.

Installation / Usage
--------------------

To install use pip:

$ pip install hrv


Or clone the repo:

$ git clone https://github.com/rhenanbartels/hrv.git
$ python setup.py install

BASIC USAGE
-------
## Create a RRi instance
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:

```python
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.])
```
#### Slicing
```python
print(rri[0])
800.0

print(type(rri[0]))
numpy.float64

print(rri[::1])
array([800., 815., 753.]) # Future versions will return a RRi object
```
#### Logical Indexing

```python
from hrv.rri import RRi

rri = RRi([800, 810, 815, 750, 753, 905])
rri_ge = rri[rri >= 800)

rri_ge
array([800., 810., 815., 905.])

```

#### Loop

```python
for rri_value in rri:
print(rri_value)

800.0
810.0
815.0
750.0
753.0
905.0
```
#### Note:
When 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`

#### RRi object and time information
```python
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.]
```
#### Note:
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.

### Basic math operations
With RRi objects you can make math operatins just like a numpy array:

```python
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.])
```

## Read RRi files

### From text (.txt) file

Text files contains a single column with all RRi values.
Example of RRi text file

```
800
810
815
750
```

```python
from hrv.io import read_from_text

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

print(rri)
RRi array([800., 810., 815., 750.])
```

### From Polar ^® (.hrm) file

The .hrm files contain the RRi acquired with Polar ^®

A complete guide for .hrm files can be found [here](https://www.polar.com/files/Polar_HRM_file%20format.pdf).

```python
from hrv.io import read_from_hrm

rri = read_from_hrm('path/to/file.hrm')

print(rri)
RRi array([800., 810., 815., 750.])
```

### From comma separated (.csv) file file
Example of csv file:

```
800,
810,
815,
750,
```

```python
from hrv.io import read_from_csv

rri = read_from_csv('path/to/file.csv')

print(rri)
RRi array([800., 810., 815., 750.])
```

#### Note:
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
```

```python
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.])
```

### RRi statistics
The RRi object implements some basic statistics information about its values:

- mean
- median
- standard deviation
- variance
- minimum
- maximum
- amplitude

Some examples:

```python
from hrv.rri import RRi

rri = RRi([800, 810, 815, 750, 753, 905])

# mean
rri.mean()
805.5

# median
rri.median()
805.0
```
You can also have a complete overview of its statistical charactheristic

```python
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
```

```python
print(desc['std'])
{'rri': 51.44171459039833, 'hr': 4.5662272355549725}
```

## RRi Visualization

The RRi class brings a very easy way to visualize your series:

### RRi Series

```python
from hrv.io import read_from_text

rri = read_from_text('path/to/file.txt')
fig, ax = rri.plot(color='k')

```
<img src="docs/figures/rri_fig.png" alt="RRi Image" width=600px;>

### RRi histogram and Heart Rate Histogram

```python
rri.hist()

rri.hist(hr=True)
```

<img src="docs/figures/rri_hist.png" alt="Moving Average Image" width=600px;>


<img src="docs/figures/hr_hist.png" alt="Moving Average Image" width=600px;>

## Time Slicing
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`.

```python
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='.')
```
<img src="docs/figures/rri_range.png" alt="Moving Average Image" width=600px;>

Time offset can be reset from the RRi series range:

```
rri_range.reset_time(inplace=True)
```
<img src="docs/figures/rri_range_reset.png" alt="Moving Average Image" width=600px;>

## Filters

### Moving Average

```python
from hrv.filters import moving_average
filt_rri = moving_average(rri, order=3)

fig, ax = rri.plot()
filt_rri.plot(ax=ax)
```

<img src="docs/figures/mov_avg.png" alt="Moving Average Image" width=600px;>

### Moving Median

```python
from hrv.filters import moving_median
filt_rri = moving_median(rri, order=3)

fig, ax = rri.plot()
filt_rri.plot(ax=ax)
```

<img src="docs/figures/mov_median.png" alt="Moving Median Image" width=600px;>

### Quotient
[Read more.](https://www.ncbi.nlm.nih.gov/pubmed/17322593)

```python
from hrv.filters import quotient
filt_rri = quotient(rri)

fig, ax = rri.plot()
filt_rri.plot(ax=ax)
```

<img src="docs/figures/quotient.png" alt="Quotient Filter Image" width=600px;>


## Time Domain Analysis
```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}
```

## Frequency Domain Analysis
```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
```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}
```