282 H. Y. ZHOU ET AL.
Copyright © 2009 SciRes. WSN
In this AED algorithm, the QRS detector adopts linear
time-domain statistical analysis and syntactic analysis
methods to locate QRS complex from AECG signals.
The signal preprocessing and conditioning procedure,
adopting adaptive filter and band-pass filter, remove or
reduce various interferences caused by physical and
technical factors. The most serious noisy, such as motion
artifacts, has been effectively eliminated by the adaptive
filter. According to the statistical feature and morphol-
ogic features of QRS complex, i.e. heart rate, steep edges
and sharp amplitude, the QRS complex is located to
mark heart beat by applying SAT method and STR pro-
cedure on sub-segment diagnosis window.
The rhythm classifier classifies rhythms and interprets
cardiac arrhythmias basing upon the diagnostic rules
which are obtained from the experiences of cardiologists
and the training results of pre-learning phase. The initial
ECG signals with the length of 10 seconds are used to
estimate the type of QRS complex and to extract th e fea-
tures of normal rhythm template (the means of LQRS,
RR, etc.). According to the origination of heart beat, the
rhythms are categorized into two classes: sinus rhythm
(atria) and ventricular rhythm (ventricle). According to
the changes of heart rate, cardiac arrhythmias are catego-
rized into two classes: bradycardia and tachycardia. The
cardiac arrhythmias interpretation procedure is adopted
to classify cardiac arrhythmias into various types of bra-
dycardia and tachycardia, based on the features extracted
in the detection algorithm.
Currently, this algorithm has been applied on the
STAR system. The performance evaluations results show
that this algorithm was effective for the QRS detection
and the rhythm classification, and was thus suitable for
PCC services. The simple, fast and efficient features of
this algorithm enable it to be embedded into microproc-
essor system or be implemented on chip.
6. Acknowledgement
This project is supported by OSEO (French research
agency) and the Conseil Régional d’Auvergne (France).
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