Segmented ECG Bio Identification using Fréchet Mean Distance and Feature Matrices of Fiducial QRS Features

Abdullah Biran, Aleksandar Jeremic

Abstract

In this paper, we present a new segmented based method for human identification using Fréchet distances and the characteristics of the lag-feature matrices of six fiducial based QRS features. We examined the applicability of our methodology on 124 ECG records of 62 subjects from the publicly available ECG ID data base. Our experiments show that the Fréchet distance can identify majority of the subjects (44 individuals) using the feature matrix of QRS segment lagged by one beat with an identification accuracy ranging from 80% to 100%. Our preliminary results indicate that identifying humans using segmented approaches can be potentially useful.

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Paper Citation


in Harvard Style

Biran A. and Jeremic A. (2021). Segmented ECG Bio Identification using Fréchet Mean Distance and Feature Matrices of Fiducial QRS Features.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS, ISBN 978-989-758-490-9, pages 223-227. DOI: 10.5220/0010262302230227


in Bibtex Style

@conference{biosignals21,
author={Abdullah Biran and Aleksandar Jeremic},
title={Segmented ECG Bio Identification using Fréchet Mean Distance and Feature Matrices of Fiducial QRS Features},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,},
year={2021},
pages={223-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010262302230227},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - Segmented ECG Bio Identification using Fréchet Mean Distance and Feature Matrices of Fiducial QRS Features
SN - 978-989-758-490-9
AU - Biran A.
AU - Jeremic A.
PY - 2021
SP - 223
EP - 227
DO - 10.5220/0010262302230227