signalytica 0.1.18

Feature extraction for time series and EEG signals

SignaLytica

A Python package for feature extraction in time series and EEG signals.

Installation

.. code-block:: bash

pip install signalytica

Dependencies

• NumPy <https://numpy.org/>_
• SciPy <https://scipy.org/>_

Utilization

Import modules

.. code-block:: python

import numpy as np
import matplotlib.pyplot as plt
from signalytica import SignaLytica

Example time series data

.. code-block:: python

np.random.seed(1234)
x = np.random.normal(size=128)
plt.figure(figsize=(6,2))
plt.plot(x)
plt.title('Example time serie'); plt.xlabel('Samples'); plt.ylabel('Amplitude')
plt.tight_layout()
plt.savefig('example_time_serie.png')

Output:

.. image:: https://github.com/Edgar-La/signalytica/blob/main/signalytica/example_time_serie.png :alt: Example time series plot

List all the available metrics in the package

.. code-block:: python

signa = SignaLytica()
available_metrics = signa.list_metrics()
print(available_metrics)

Output:

::

['mean', 'std', 'coeff_var', 'median', 'mode', 'max', 'min', 'first_quartile', 'third_quartile', 'inter_quartile_range', 'kurtosis', 'skewness', 'activity_hjorth_param', 'mobility_hjorth_param', 'complexity_hjorth_param', 'total_power_spectral_density', 'centroid_power_spectral_density', 'relative_delta_power', 'relative_theta_power', 'relative_alpha_power', 'relative_beta_power', 'relative_gamma_power', 'determinism', 'trapping_time', 'diagonal_line_entropy', 'average_diagonal_line_length', 'compute_recurrence_rate', 'spectral_edge_frequency_25', 'spectral_edge_frequency_50', 'spectral_edge_frequency_75', 'delta_amplitude', 'theta_amplitude', 'beta_amplitude', 'alpha_amplitude', 'gamma_amplitude', 'hurst_exponent']

Extract specific features from time series/EEG signal

.. code-block:: python

features = ['coeff_var', 'inter_quartile_range', 'kurtosis', 'total_power_spectral_density', 'trapping_time']
signa.extract_features(x, features)

Output:

::

{
  'coeff_var': 0.9296511098623816,
  'inter_quartile_range': 1.1642928949004765,
  'kurtosis': 0.9355744131054928,
  'total_power_spectral_density': 1.03503685376608,
  'trapping_time': 2.2205882352941178
}

Calculate individual features

Activity Hjorth Parameter

.. code-block:: python

activity = signa.activity_hjorth_param(x)
print(activity)

Output:

::

0.9296511098623816

Total power spectral density

.. code-block:: python

tpsd = signa.total_power_spectral_density(x)
print(tpsd)

Output:

::

1.03503685376608

Determinism

.. code-block:: python

determ = signa.determinism(x)
print(determ)

Output:

::

4.315972222222222

Alpha amplitude

.. code-block:: python

alpha_amp = signa.alpha_amplitude(x)
print(alpha_amp)

Output:

::

0.19518190403498442

Hurst Exponent

.. code-block:: python

hurst_exp = signa.hurst_exponent(x)
print(hurst_exp)

Output:

::

0.5074992385199263

Extract all features

Instead of using a list to indicate the features, you can use the parameter all to calculate all the features.

.. code-block:: python

all_features_calculated = signa.extract_features(x, 'all')
all_features_calculated

Output:

::

{'mean': 0.006880339575229891,
 'std': 0.9641841680210175,
 'coeff_var': 0.9296511098623816,
 'median': 0.07802095042293272,
 'mode': 0.47143516373249306,
 'max': 2.390960515463033,
 'min': -3.5635166606247353,
 'first_quartile': -0.4874633299633267,
 'third_quartile': 0.6768295649371497,
 'inter_quartile_range': 1.1642928949004765,
 'kurtosis': 0.9355744131054928,
 'skewness': -0.5940081255524681,
 'activity_hjorth_param': 0.9296511098623816,
 'mobility_hjorth_param': 1.5772426970445614,
 'complexity_hjorth_param': 1.1381489369369338,
 'total_power_spectral_density': 1.03503685376608,
 'centroid_power_spectral_density': 39.785545239633095,
 'relative_delta_power': 0.014199533696614332,
 'relative_theta_power': 0.024773922507739236,
 'relative_alpha_power': 0.06670507211034518,
 'relative_beta_power': 0.16222741368578988,
 'relative_gamma_power': 0.1944461143387118,
 'determinism': 4.315972222222222,
 'trapping_time': 2.2205882352941178,
 'diagonal_line_entropy': 2.794104878439014,
 'average_diagonal_line_length': 26.28700906344411,
 'compute_recurrence_rate': 0.063720703125,
 'spectral_edge_frequency_25': 27.0,
 'spectral_edge_frequency_50': 46.0,
 'spectral_edge_frequency_75': 55.0,
 'delta_amplitude': 0.332883261842357,
 'theta_amplitude': 0.1334128596782051,
 'beta_amplitude': 0.44829663314331475,
 'alpha_amplitude': 0.19518190403498442,
 'gamma_amplitude': 0.36127348831240264,
 'hurst_exponent': 0.5074992385199263}

Convert the features into a feature vector

.. code-block:: python

feature_vector = list(all_features_calculated.values())
print('features:', feature_vector)
print('n_features:', len(feature_vector))

Output:

::

features: [0.006880339575229891, 0.9641841680210175, 0.9296511098623816, 0.07802095042293272, 0.47143516373249306, 2.390960515463033, -3.5635166606247353, -0.4874633299633267, 0.6768295649371497, 1.1642928949004765, 0.9355744131054928, -0.5940081255524681, 0.9296511098623816, 1.5772426970445614, 1.1381489369369338, 1.03503685376608, 39.785545239633095, 0.014199533696614332, 0.024773922507739236, 0.06670507211034518, 0.16222741368578988, 0.1944461143387118, 4.315972222222222, 2.2205882352941178, 2.794104878439014, 26.28700906344411, 0.063720703125, 27.0, 46.0, 55.0, 0.332883261842357, 0.1334128596782051, 0.44829663314331475, 0.19518190403498442, 0.36127348831240264, 0.5074992385199263]

n_features: 36

Development

SignaLytica was created and is maintained by Edgar Lara <https://accidental-bard-367.notion.site/Edgar-Lara-a8828a758e5242f4981b65a2fdc1d44f>_. Contributions are more than welcome, so feel free to contact me, open an issue, or submit a pull request!

To see the code or report a bug, please visit the GitHub repository <https://github.com/Edgar-La/signalytica>_.

Note that this program is provided with NO WARRANTY OF ANY KIND. Always double-check the results.

Acknowledgement

This package was inspired by the research work of Hernández Nava, G. (2023). *Predicción de eventos epilépticos mediantes técnicas de aprendizaje profundo usando señales de EEG.