Streamlining heart failure patient care with machine learning of thoracic cavity sound data.

Abstract

Together, the heart and lung sound comprise the thoracic cavity sound, which provides informative details that reflect patient conditions, particularly heart failure (HF) patients. However, due to the limitations of human hearing, a limited amount of information can be auscultated from thoracic cavity sounds. With the aid of artificial intelligence-machine learning, these features can be analyzed and aid in the care of HF patients. Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising, resampling, segmentation, and normalization. Afterwards, the most crucial step is feature extraction and selection where relevant features are selected to train the model. The next step is classification and model performance evaluation. This review summarizes the currently available studies that utilized different machine learning models, different feature extraction and selection methods, and different classifiers to generate the desired output. Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients. Additionally, some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety, while others have focused on risk stratification and prognostic evaluation of HF patients using thoracic cavity sounds. Overall, the results from these studies demonstrate a promisingly high level of accuracy. Therefore, future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.