Authors - Chetana Shravage, Shubhangi Vairagar, Priya Metri, Rohit Rajendra Kalaburgi, Harsh Anil Shah, Abhinandan Vaibhav Sharma, Shubham Shatrughun Godge Abstract - Heart sound categorization is critical for the early identification and detection of cardiovascular illness. Recently deep learning methods have resulted in promising improvements in the correctness of heart sound classification systems. This work introduces a unique transformer-based model for heart sound classification that uses powerful attention mechanism to capture both local as well as global dependencies in heart sound data. Transformers, in contrast to traditional models that rely on handmade features or recurrent networks, can dynamically focus on the most important characteristics in time-series data, making them perfect for dealing with the complexity and variability of phonocardiogram (PCG) signals
