Figure 1.

Potential applications of acoustic cardiography in heart failure. Multiple approaches exist that can harness the information encompassed by acoustic cardiography recordings. Features (e.g. amplitudes, frequencies, and time intervals) can be extracted manually and analysed using conventional statistics or supplied to classical machine learning or deep learning models. Recordings can also be analysed without manual feature extraction using deep learning. Of note, only a few examples are provided in the figure, and acoustic cardiography might be used for several other tasks within or even outside the realm of heart failure. ECG, electrocardiogram; HF, heart failure; ML, machine learning.