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

ECG signals are classified using pre-trained deep CNN such as AlexNet via transfer learning. As we know that AlextNet can accept 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 the 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 the form of an image. The ECG database is taken from Physionet.

This video has the following contents:

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

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