(2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes.  Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups.  (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation.  Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.
CVT-Trans
Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers.The major cause of death worldwide is due to cardiovascular disorders (CVDs). 
Convolutional Vision Transformers
Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers.The major cause of death worldwide is due to cardiovascular disorders (CVDs).  For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. 
Phonocardiogram
The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. 
Continuous Wavelet Transform-based Spectrogram
(1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. 
Deep Learning