Person identification in irregular cardiac conditions using electrocardiogram signals

This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challenge database (AFPDB), MIT-BIH Supraventricular Arrthymia database (SVDB) and T-Wave Alternans Challenge database (TWADB). Cross correlation (CC) was used as the biometric matching algorithm with defined threshold values to evaluate the performance. In order to measure the eficiency of this simple yet effective matching algorithm, two biometric performance metrics were used which are false acceptance rate (FAR) and false reject rate (FRR). Our experimentation results suggest that ECG based biometric identification with irregular cardiac condition gives a higher recognition rate of different ECG signals when tested for three different abnormal cardiac databases yielding false acceptance rate (FAR) of 2%, 3% and 2% and false reject rate (FRR) of 1%, 2% and 0% for AFPDB, SVDB and TWADB respectively. These results also indicate the existence of salient biometric characteristics in the ECG morphology within the QRS complex that tends to differentiate individuals.