Beat-to-beat alterations of acoustic intensity and frequency at the maximum power of heart sounds are associated with NT-proBNP levels.

Background: Auscultatory features of heart sounds (HS) in patients with heart failure (HF) have been studied intensively. Recent developments in digital and electrical devices for auscultation provided easy listening chances to recognize peculiar sounds related to diastolic HS such as S or S. This study aimed to quantitatively assess HS by acoustic measures of intensity (dB) and audio frequency (Hz).

Variational autoencoder-based neural electrocardiogram synthesis trained by FEM-based heart simulator.

Background: For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation.

Objective: The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters.

Unsupervised Heart Sound Decomposition and State Estimation with Generative Oscillation Models.

This paper proposes a new generative probabilistic model for phonocardiograms (PCGs) that can simultaneously capture oscillatory factors and state transitions in cardiac cycles. Conventionally, PCGs have been modeled in two main aspects. One is a state space model that represents recurrent and frequently appearing state transitions.

Modular representation of layered neural networks.

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood.