Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

The work presented here applies deep learning to the task of automated cardiac
auscultation, ie recognizing abnormalities in heart sounds. We describe an automated heart …

During multimodal model training and testing, certain data modalities may be absent due to
sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting …

Listening to heart sounds is an important first step in evaluating the cardiovascular system
and is important in the early detection of cardiovascular disease. We present and evaluate a …

The cardiac auscultation using the classical stethoscope (PCG: phonological cardiogram) is
known as the most famous method to detect Cardiovascular Disease (CVD). However, this …

Cardiovascular disorders cause nearly one in three deaths in the United States. Short-and
long-term care for these disorders is often determined in short-term settings. However, these …

Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15
million deaths annually. Accurate diagnosis and routine monitoring of these diseases by …

Cardiovascular diseases (CVDs) are the leading global cause of death, which requires the
early and accurate detection of cardiac abnormalities. Abnormal heart sounds, indicative of …

Large language models (LLMs) for audio have excelled in recognizing and analyzing
human speech, music, and environmental sounds. However, their potential for …

Objective. Phonocardiography has recently gained popularity in low-cost and remote
monitoring, including passive fetal heart monitoring. The development of methods which …