Skip to main content

A biological signal simulation and evaluation package

Project description

Tests Publish Issues License

Med-Signal package for biological signals processing and simulation

This module focuses on the simulation and specific processing of known biological signals, such as EEG and ECG. This module is aimed for the study and processing of biological signals by the usage of known methods.

Contents

EEG module

This module contains the necesary actions for the processing and evaluation of EEG signals contained in a numpy array. The main functionality of this module is to let the user process analyze and generate simulations of EEG signals in a single package. This module implements actions such as:

  • Read and store the signal found from a valid .txt, .csv file containing the signal.
  • Calculate the main frequencies and relevant information from the signal.
  • Retrieve the relevant information and calculate the wave type based on Polar Statistics.

Izhikevich simulation module

This module focuses on generating a model in which the user can use the Izhikevich neurons and visualize their behavior either as a single unit or as a network of multiple units. The simulation includes settings such as input value, neuron positions and Field Voltage Response, as well as Single Voltage Response. The functionality of this module is based in three data-classes functioning as models to describe the neural response based on the Izhikevich Equations for neural response. The classes are listed as follow:

  • NeuronTypes: Provides the neural constants which determine the behavior of the neuron model.
  • Neuron: The actual neural model descrved by Izhikevich. An object with this type will represent a neuron model with constants provided by the the NeuronTypes class. This is the unit that will provide the Single Voltage Response with a given time and input.
  • Network: Represents a group of Neurons interconnected with each other. An object with this type provides a Field Voltage Response with its given neurons and connections.

Installation

In order to install and make use of this package, just enter python -m pip install med-signal and you will be good to go! This package uses various dependencies for its optimal functions. These requirements are listed in the section below. In order to successfuly use this package, a set of requirements are needed for its usage. These requirements will be automatically installed or upgraded (if you already have one of the dependencies) as the package is installed. The minimum python version needed to correctly use this package is python 3.10.

The following list provides the required packages with their minimal required version needed to install MedSig.

  • Pandas - 1.4.1
  • Matplotlib - 3.5.1
  • Scipy - 1.8.0
  • Numpy - 1.22.3

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

med-signal-0.0.5.tar.gz (16.0 kB view details)

Uploaded Source

Built Distribution

med_signal-0.0.5-py3-none-any.whl (15.3 kB view details)

Uploaded Python 3

File details

Details for the file med-signal-0.0.5.tar.gz.

File metadata

  • Download URL: med-signal-0.0.5.tar.gz
  • Upload date:
  • Size: 16.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for med-signal-0.0.5.tar.gz
Algorithm Hash digest
SHA256 3a7230800934482e65ac50ec2a241742bcc2c5b4934315dc6901ad364461b1f2
MD5 6b4a3b422f9d3ca0f303cbc183170f7c
BLAKE2b-256 4d2c6f0ef1b62218d738f3a634190c941bce7125be70c8d6b918264c78a696e7

See more details on using hashes here.

File details

Details for the file med_signal-0.0.5-py3-none-any.whl.

File metadata

  • Download URL: med_signal-0.0.5-py3-none-any.whl
  • Upload date:
  • Size: 15.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for med_signal-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 12382d5b19cc9e819ac6269db89909c8e1e32d2d1e206270a11a883200b5f3b1
MD5 6a0016a55e82f329ada91dbd0b96c8a8
BLAKE2b-256 9a44be419da307d47f9b159fcec138fdac8426b4a559d541907068e1dabe3165

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page