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Spectrogram_AS_Frontiers

This codes correspond to the paper called "A Learning-Based Image Processing Approach for Pulse Wave Velocity Estimation using spectrogram from Peripheral Pulse Wave Signals: An in-silico Study", and have the following uses:

  • Spectrogram_selection.m : This file containts the code to select the parameters for the histogram computing the Q-metrics.

  • SCSA_tune.m and SCSA_noise_tune.m : This files contains the codes to selects the SCSA parameters for the noisy-free cases and the noisy cases.

  • Laws_features.m, SCSA_features.m and Statistical_features.m : This files contains the codes to compute the different features used in this project.

  • ML_hyptun.py : This files contains the code to train the different machine learning models and obtain the prediction.

  • Functions : This folder contains the different functions used in the project

  • Data: The dataset used is this project can be downloaded in : https://peterhcharlton.github.io/pwdb/index.html

  • Results: Every time you run the algorithms the result will be saved in this folder