Human authentication implemented for mobile applications based on ECG-data acquired from sensorized garments

In recent years biometric systems gain more and more importance. Studies showed, that authentication with a clinical electrocardiogram (ECG) is principally possible and hence could be used as a biometric feature. In this work an algorithm was implemented. which is capable of segmenting single heartbeats of a mobile recorded single-channel-ECG. Based on these heartbeats, fiducial features, features from the combination of autocorrelation and discrete cosine transform, and wavelet features were extracted and considered for the classification process. They were evaluated concerning distinctiveness and stability over time. In order to reduce the feature space, sequential forward selection was used to eliminate unstable and non-distinctive features. A sensorized garment was used to derive ECG-signals from ten persons in order to evaluate the performance of the proposed methods. The wavelet-transform provides the best features since it is focusing on the characteristics of the QRS-complex of a human heartbeat, which provides the most stable information over time. Using the wavelet coefficients as features the developed authentication algorithm produced an equal error rate of 12.53 %.