In this paper we present a contribution to create a VHDL-AMS
radio-frequency component library. Currently, integrated circuit
technology tends to integrate in a sole chip not only mixed signal but
also mixed technology systems, going to a more general definition of
the so called Systems On Chip. A library of RF models would be useful
to model, in a same framework, circuits and systems of different
physical domains, including RF, which will certainly optimise design
process of such systems.
Generally, VHDL-AMS, the analogue and mixed signal extension to IEEE
standard VHDL does not include specific formulation for RF devices or
systems modeling, as it does not support distributed parameters for
simulation or description purposes. However, RF devices can be
modeled by means of more general VHDL-AMS resources, like sentences
including algebraic and trigonometric relations.
Proc. SPIE. 5839, Bioengineered and Bioinspired Systems II
KEYWORDS: Human-machine interfaces, Detection and tracking algorithms, Pathology, Heart, Linear filtering, Data acquisition, Signal processing, Algorithm development, Signal analyzers, Signal detection
The auscultation of the heart is still the first basic analysis tool used to evaluate the functional state of the heart, as well as the first indicator used to submit the patient to a cardiologist. In order to improve the diagnosis capabilities of auscultation, signal processing algorithms are currently being developed to assist the physician at primary care centers for adult and pediatric population. A basic task for the diagnosis from the phonocardiogram is to detect the events (main and additional sounds, murmurs and clicks) present in the cardiac cycle. This is usually made by applying a threshold and detecting the events that are bigger than the threshold. However, this method usually does not allow the detection of the main sounds when additional sounds and murmurs exist, or it may join several events into a unique one. In this paper we present a reliable method to detect the events present in the phonocardiogram, even in the presence of heart murmurs or additional sounds. The method detects relative maxima peaks in the amplitude envelope of the phonocardiogram, and computes a set of parameters associated with each event. Finally, a set of characteristics is extracted from each event to aid in the identification of the events. Besides, the morphology of the murmurs is also detected, which aids in the differentiation of different diseases that can occur in the same temporal localization. The algorithms have been applied to real normal heart sounds and murmurs, achieving satisfactory results.