Abstract

The ability of an experienced physician to detect the progression of chronic heart failure (CHF) primarily relies on patient examination and changes in heart failure biomarkers, determined from blood tests. Unfortunately, the clinical deterioration of a CHF patient often signals a fully developed CHF episode that may necessitate hospitalization. In some cases, distinctive changes in heart sounds can accompany heart failure progression and are detectable through phonocardiography. Leveraging recent advancements in machine learning and deep learning, this project introduces an early detection system for chronic heart failure using phonocardiography (PCG) data. The system utilizes an end-to-end average aggregate recording model that incorporates features extracted from both machine learning and deep learning techniques. The proposed ChronicNet model is compared with individual machine learning and deep learning models, demonstrating its effectiveness in early CHF detection.

Description