Notebooks Grouped by Signal Type

Each Notebook content is summarized in his header through a quantitative scale ("Difficulty" between 1 and 5 stars) and some keywords (Group of "tags").

Grouping Notebooks by difficulty level, by signal type to which it applies or by tags is an extremelly important task, in order to ensure that the biosignalsnotebooks user could navigate efficiently in this learning environment.

ECG
	Generation of Poincaré Plot from ECG Analysis
	Computing SNR for ECG Signals
	Computing SNR for Slow Signals
	Generation of Tachogram from ECG
	ECG Sensor - Unit Conversion
	Event Detection - R Peaks (ECG)
	ECG Analysis - Heart Rate Variability Parameters
	Signal Classifier - Distinguish between EMG and ECG

EMG
	Fatigue Evaluation - Evolution of Median Power Frequency
	EMG Sensor - Unit Conversion
	Event Detection - Muscular Activations (EMG)
	EMG Analysis - Time and Frequency Parameters
	Signal Classifier - Distinguish between EMG and ECG
	Train a model for detecting the fist activity using Naive Bayes
	EMG - Overview

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☌ Project Presentation ☌ GitHub Repository ☌ How to install biosignalsnotebooks Python package ? ☌ Signal Library

☌ Notebook Categories ☌ Notebooks by Difficulty ☌ Notebooks by Signal Type ☌ Notebooks by Tag

Auxiliary Code Segment (should not be replicated by the user)

from biosignalsnotebooks.__notebook_support__ import css_style_apply
css_style_apply()

.................... CSS Style Applied to Jupyter Notebook .........................