Video 24: ECG Signals Classification using Continuous Wavelet Transform (CWT) & Deep Neural Network in MATLAB

Hello Viewers, in this video, ECG signals are classified using pretrained deep CNN such as AlexNet via transfer learning. As we know that AlextNet can accepts input as image only, therefore, it is not possible to give 1D ECG signals to AlexNet directly. 

To solve this problem, we utilize the strength of Continuous Wavelet Transform (CWT) to represent 1D ECG signals into image, so that it can be fed as input to deep CNN AlexNet. 

Using CWT, we obtain CWT coefficients of 1D ECG signal and these coefficients are arranged as scalogram to represent in form of image. The ECG database is taken from Physionet.

This video has following contents:

  • Types of ECG Signals for Classification.
  • ECG Signal Database.
  • Converting 1D ECG signals to Image using CWT Scalogram.
  • Transfer Learning via pretrained AlexNet deep CNN.
  • MATLAB Code for CWT Scalogram Image database creation.
  • MATLAB Code for AlexNet Training and Validation.

Important Links:

1. Continuous Wavelet Transform of 1D signals using Python and MATLAB: Click Here

2. How to create a deep neural network in MATLAB : Click Here

3. ECG signal database GitHub repository: