Document Type
Event
Start Date
23-4-2023 5:00 PM
Description
Heart diseases are the leading cause of mortality worldwide, emphasizing the need for early detection and intervention. Traditional heart sound analysis using a stethoscope is subjective and prone to variability, necessitating a more objective and reliable approach. In this study, we present a deep learning model designed for heart sound analysis to enable the early detection of heart diseases. The model's architecture combines convolutional and fully connected layers with max-pooling and dropout operations, effectively capturing intricate patterns in heart sounds. We trained and validated our model on the Physionet 2016 challenge dataset, consisting of 3240 labeled heart sound recordings. Our deep learning model achieved an accuracy of 91.9%, surpassing the current state-of-the-art performance of 89.7%. This result demonstrates the model's potential to significantly reduce diagnostic errors and facilitate timely interventions, ultimately improving patient outcomes and reducing healthcare costs.
GR-325 Early Heart Disease Detection Using Mel-Spectrograms and Deep Learning
Heart diseases are the leading cause of mortality worldwide, emphasizing the need for early detection and intervention. Traditional heart sound analysis using a stethoscope is subjective and prone to variability, necessitating a more objective and reliable approach. In this study, we present a deep learning model designed for heart sound analysis to enable the early detection of heart diseases. The model's architecture combines convolutional and fully connected layers with max-pooling and dropout operations, effectively capturing intricate patterns in heart sounds. We trained and validated our model on the Physionet 2016 challenge dataset, consisting of 3240 labeled heart sound recordings. Our deep learning model achieved an accuracy of 91.9%, surpassing the current state-of-the-art performance of 89.7%. This result demonstrates the model's potential to significantly reduce diagnostic errors and facilitate timely interventions, ultimately improving patient outcomes and reducing healthcare costs.