Evaluation of Wearable Acoustic Sensors and Machine Learning Algorithms for Automated Measurement of Left Ventricular Ejection Fraction.

Left ventricular ejection fraction (EF) is a predictor of mortality and guides clinical decisions. Although transthoracic echocardiography (TTE) is commonly used for measuring EF, it has limitations, such as subjectivity and requires expert personnel. Advancements in biosensor technology and artificial intelligence are allowing systems capable of determining left ventricular function and providing automated measurement of EF.

Assessment of Left Ventricular Diastolic Function using Phonocardiogram Signals: A Comparison with Echocardiography.

This paper presents a fully-automated end-to-end phonocardiogram(PCG)-based wearable system capable of providing echocardiography-like metrics for left ventricular (LV) diastolic function assessment. Proxy metrics for five echocardiographic parameters were calculated based on physiologically-motivated features extracted from PCG signals using noise-subtraction, heartbeat-segmentation, and quality-assurance algorithms. The clinical value of these proxy metrics was evaluated using the latest American Society of Echocardiography/European Association of Cardiovascular Imaging guidelines for evaluation of LV diastolic function.

Fully-Automated Diagnosis of Aortic Stenosis Using Phonocardiogram-Based Features.

The irreversible damage and eventual heart failure caused by untreated aortic stenosis (AS) can be prevented by early detection and timely intervention. Prior work in the field of phonocardiogram (PCG) signal analysis has provided proof of concept for using heart-sound data in AS diagnosis. However, such systems either require operation by trained technicians, fail to address a diverse subject set, or involve unwieldy configuration procedures that challenge real-world application.