Abstract

Chronic heart failure (CHF) is a chronic, progressive condition underscored by the heart’s inability to supply enough perfusion to target tissues and organs at the physiological filling pressures to meet their metabolic demands. CHF has reached epidemic proportions in the population, as its incidence is increasing by 2% annually. In the developed world, CHF affects 1-2% of the total population and 10% of people older than 65 years. Currently, the diagnosis and treatment of CHF uses approximately 2% of the annual healthcare budget. In absolute terms, the USA spent approximately 35 billion USD to treat CHF in 2018 alone, and the costs are expected to double in the next 10 years. Currently, an experienced physician can detect the worsening of HF 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