Labélisation de signaux

Labélisation manuelle et automatisée des attributs, régions et points d’intérêt des signaux

Utilisez le Deep Learning pour accélérer et automatiser la labélisation des caractéristiques.

Applications

Label signal attributes, regions, and points of interest, and extract features

`edfheader`	Create header structure for EDF or EDF+ file (depuis R2021a)
`edfinfo`	Get information about EDF/EDF+ file (depuis R2020b)
`edfread`	Read data from EDF/EDF+ file (depuis R2020b)
`edfwrite`	Create or modify EDF or EDF+ file (depuis R2021a)
`paddata`	Pad data by adding elements (depuis R2023b)
`resize`	Resize data by adding or removing elements (depuis R2023b)
`trimdata`	Trim data by removing elements (depuis R2023b)
`signalDatastore`	Datastore for collection of signals (depuis R2020a)

`labeledSignalSet`	Create labeled signal set
`signalLabelDefinition`	Create signal label definition
`signalMask`	Modify and convert signal masks and extract signal regions of interest (depuis R2020b)
`sigrangebinmask`	Label signal samples with values within a specified range (depuis R2023a)
`countlabels`	Count number of unique labels (depuis R2021a)
`filenames2labels`	Get list of labels from filenames (depuis R2022b)
`folders2labels`	Get list of labels from folder names (depuis R2021a)
`splitlabels`	Find indices to split labels according to specified proportions (depuis R2021a)

Automate Signal Labeling with Custom Functions (Signal Processing Toolbox)
Use Signal Labeler to locate and label QRS complexes and R peaks of ECG signals.
Label Signal Attributes, Regions of Interest, and Points (Signal Processing Toolbox)
Use Signal Labeler to label attributes, regions, and points of interest in a set of whale songs.
Create Labeled Signal Sets Iteratively with Reduced Human Effort (Signal Processing Toolbox)
Use deep learning to decrease the human effort required to label signals.

Label signals and export data using Signal Labeler to train a deep learning classifier.