SVM based on personal identification system using Electrocardiograms

TL;DR

This paper introduces a novel ECG-based personal identification method combining morphological descriptors and Hermite Polynomial Expansion coefficients, utilizing SVM classifiers to achieve high recognition accuracy.

Contribution

The paper proposes a hybrid classification approach that integrates morphological descriptors and Hermite Polynomial Expansion coefficients for improved ECG-based identification.

Findings

Achieved up to 98.97% recognition accuracy

Morphological descriptors alone reached 96.45% accuracy

Hybrid approach outperforms individual descriptor classifications

Abstract

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%.