Abstract

Currently, an experienced physician can detect the worsening of chronic heart failure (CHF) by examining the patient and by characteristic changes in the patient’s heart failure biomarkers, which are determined from the patient’s blood. Unfortunately, clinical worsening of a CHF patient likely means that we are already dealing with a fully developed CHF episode that will most likely require a hospital admission. Additionally, in some patients, characteristic changes in heart sounds can accompany heart failure worsening and can be heard using phonocardiography. Therefore, with the usage of recent advancement in machine learning and deep learning models, this project implements the detection of chronic heart failure from phonocardiography (PCG) data using end-to-end average aggregate recording model built with extracted features from both machine learning and deep learning. The proposed ChronicNet model results also compared with individual ML, and DL model.

Description