Title: Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine,
Abstract: Computer based recognition and detection of abnormalities in ECG signals is
proposed. For this purpose, the Support Vector Machines (SVM) are combined with
the advantages of Hermite transform representation. SVM represent a special
type of classification techniques commonly used in medical applications.
Automatic classification of ECG could make the work of cardiologic departments
faster and more efficient. It would also reduce the number of false diagnosis
and, as a result, save lives. The working principle of the SVM is based on
translating the data into a high dimensional feature space and separating it
using a linear classificator. In order to provide an optimal representation for
SVM application, the Hermite transform domain is used. This domain is proved to
be suitable because of the similarity of the QRS complex with Hermite basis
functions. The maximal signal information is obtained using a small set of
features that are used for detection of irregular QRS complexes. The aim of the
paper is to show that these features can be employed for automatic ECG signal
analysis.