23,288 research outputs found
Cardiovascular function and ballistocardiogram: a relationship interpreted via mathematical modeling
Objective: to develop quantitative methods for the clinical interpretation of
the ballistocardiogram (BCG). Methods: a closed-loop mathematical model of the
cardiovascular system is proposed to theoretically simulate the mechanisms
generating the BCG signal, which is then compared with the signal acquired via
accelerometry on a suspended bed. Results: simulated arterial pressure
waveforms and ventricular functions are in good qualitative and quantitative
agreement with those reported in the clinical literature. Simulated BCG signals
exhibit the typical I, J, K, L, M and N peaks and show good qualitative and
quantitative agreement with experimental measurements. Simulated BCG signals
associated with reduced contractility and increased stiffness of the left
ventricle exhibit different changes that are characteristic of the specific
pathological condition. Conclusion: the proposed closed-loop model captures the
predominant features of BCG signals and can predict pathological changes on the
basis of fundamental mechanisms in cardiovascular physiology. Significance:
this work provides a quantitative framework for the clinical interpretation of
BCG signals and the optimization of BCG sensing devices. The present study
considers an average human body and can potentially be extended to include
variability among individuals
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
- …