# Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial Intelligence

## TL;DR

This paper presents an AI model for detecting heart murmurs in phonocardiogram data, using data augmentation and advanced machine learning techniques to improve diagnostic accuracy and speed.

## Contribution

The novel contribution is an AI model combining data augmentation and a pre-trained Vision Transformer with MiniROCKET for efficient heart murmur detection.

## Key findings

- The model achieved improved metrics like Weighted Accuracy, Sensitivity, and F-Score compared to existing methods.
- The model processed each patient's data in just 0.02 seconds, enabling fast diagnosis.

## Abstract

Background/Objectives: With a 17.9 million annual mortality rate, cardiovascular disease is the leading global cause of death. As such, early detection and disease diagnosis are critical for effective treatment and symptom management. Cardiac auscultation, the process of listening to the heartbeat, often provides the first indication of underlying cardiac conditions. This practice allows for the identification of heart murmurs caused by turbulent blood flow. In this exploratory research paper, we propose an AI model to streamline this process to improve diagnostic accuracy and efficiency. Methods: We utilized data from the 2022 George Moody PhysioNet Heart Sound Classification Challenge, comprising phonocardiogram recordings of individuals under 21 years of age in Northeast Brazil. Only patients who had recordings from all four heart valves were included in our dataset. Audio files were synchronized across all recordings and converted to Mel spectrograms before being passed into a pre-trained Vision Transformer, and finally a MiniROCKET model. Additionally, data augmentation was conducted on audio files and spectrograms to generate new data, extending our total sample size from 928 spectrograms to 14,848. Results: Compared to the existing methods in the literature, our model yielded significantly enhanced quality assessment metrics, including Weighted Accuracy, Sensitivity, and F-Score, and resulted in a fast evaluation speed of 0.02 s per patient. Conclusions: The implementation of our method for the detection of heart murmurs can supplement physician diagnosis and contribute to earlier detection of underlying cardiovascular conditions, fast diagnosis times, increased scalability, and enhanced adaptability.