Abstract:
Auscultation is a technique, in which Physicians used the stethoscope to
listen to patient’s heart sounds in order to make a diagnosis. However, the
determination of heart conditions by heart auscultation is a difficult task and
it requires special training of medical staff. On the other hand, in primary or
home health care, when deciding who requires special care, auscultation
plays a very important role; and for these situations, an ‘‘intelligent
stethoscope’’ with decision support abilities is highly needed and it would
be a great added value.
In this study a reliable Real Time Heart sounds recognition system has been,
introduced, designed, implemented and successfully tested.
The system algorithm has been realized in two phases, offline data phase and
real data phase. For offline data phase, 30 cases of Heart Sounds (HSs) files
were collected from medical students and doctor's world website, and then
the background noise is minimized using wavelet transform. After that,
graphical and statistics features vector elements are formed for both time and
frequency domain. Finally, classification process was accomplished using
look-up table. The implementation of the proposed algorithm produced
accuracy of 90%, and sensitivity of 87.5%.
In experimental phase (real time data), electronic stethoscope has been
designed and recorded HSs directly from 30 volunteers with 17 normal case
and 13 various pathologies cases. In preprocessing stage, an adaptive filter
was used to filter heart sounds from lung sounds, due to lung sound
overlapped with heart sound in sub frequency band. Then, wavelet was
applied to minimized background noise and features are formed for
classification process, as well as offline data phase. The implementation of
the proposed algorithm produced accuracy of 80%, and sensitivity of 82.4%.
The advanced steps for implementing a portable module by embedded DSP
have been successfully achieved. Firstly, System SIMULINK model was
built, and then real time workshop was used to generate embedded coder,
finally the code files linked to Code Composer Studio Software and running
the project successfully.
