1 research outputs found
SVM based on personal identification system using Electrocardiograms
This paper presents a new algorithm for personal identification from their
Electrocardiograms (ECG) which is based on morphological descriptors and
Hermite Polynomials Expansion coefficients (HPEc). After preprocessing, we
extracted ten morphological descriptors which were divided into homogeneous
groups (amplitude, surface interval and slope) and we extracted sixty Hermite
Polynomials Expansion coefficients(HPEc) from each heartbeat. For the
classification, we employed a binary Support Vector Machines with Gaussian
kernel and we adopted a particular strategy: we first classified groups of
morphological descriptors separately then we combined them in one system. On
the other hand, we classified the Hermite Polynomials Expansion coefficients
apart and we associated them with all groups of morphological descriptors in a
single system in order to improve overall performance. We tested our algorithm
on 18 different healthy signals of the MIT_BIH database. The analysis of
different groups separately showed that the best recognition performance is
96.45% for all morphological descriptors and the results of experiments showed
that the proposed hybrid approach has led to an overall maximum of 98.97%.Comment: Conferenc