Heartbeat Segment

Heartbeat segment analysis is a rapidly evolving field focused on extracting meaningful information from individual heartbeats for various applications, including disease diagnosis and emotion detection. Current research employs deep learning models, often incorporating convolutional neural networks and diffusion models, to analyze segmented ECG and phonocardiogram data, leveraging techniques like self-supervised learning to improve performance with limited labeled data. This work holds significant promise for improving the accuracy and efficiency of cardiac diagnostics, enabling earlier disease detection and personalized healthcare interventions, as well as offering novel approaches to non-invasive monitoring of physiological and emotional states.