| Περίληψη: | The aging population in many countries, in combination with high
government deficits and financial resources limitations,
necessitates new methods for the home care of the elderly at
reasonable costs based on the exploitation of modern information
and communication technologies (ICT). This requires the
installation of assistive environments at the homes of elderly
people, which include various types of sensors, generating biosignals
of other types of signals, which are transferred through
networks to local health centers or hospitals in order to be
monitored. However, scaling up the application of such ICTbased
methods of elderly home care is going to increase
tremendously the workload of the medical staff of local health
centers or hospitals. Therefore it is of critical importance to
develop capabilities for an automated first screening of these
signals and identification of abnormal elements and diseases. In
this direction the present paper proposes a system for the
automatic identification of murmurs in heart sound signals, and
also for the classification of them as systolic or diastolic, using a
new generation of advanced Random Forests classification
algorithms, which are aggregating the prediction of multiple
classifiers (ensemble classification). The proposed system has
been applied and validated in a representative global dataset of
198 heart sound signals, which come both from healthy medical
cases and from cases having systolic and diastolic murmurs. Also,
some alternative classifiers have been applied to the same data for
comparison purposes. It has been concluded that the proposed
systems shows a good performance, which is higher than the
examined alternative classifiers. |
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English