A study on a bio-signal biometric algorithm on the ubiquitous environments

Abstract

This paper is about the personal identification algorithm for adapting ubiquitous environment using electrocardiogram (ECG) that has been studied by a few researchers recently. The main characteristic of proposed algorithm uses together features analysis and morphological analysis method. The Principle Component Analysis (PCA) algorithm was applied for morphological analysis method and the features analysis method adapting to Support Vector Machine (SVM) classifier algorithm. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 93.89% heartbeat recognition rate and 100% ECG recognition rate. © Springer-Verlag Berlin Heidelberg 2014.