A proposed biometric sample extraction technique for ECG signals with abnormal cardiac conditions achieved high person identification accuracy of 96.7% for MITDB, 96.4% for SVDB, and 99.3% for DiSciRi.
Does the proposed ECG biometric extraction technique improve person identification accuracy in subjects with abnormal cardiac conditions compared to existing methods?
A novel ECG biometric extraction technique achieves high person identification accuracy (96.4%-99.3%) even in the presence of abnormal cardiac conditions.
This paper presents a person identification mechanism using electrocardiogram (ECG) signals with abnormal cardiac conditions in network environments. A total of 164 subjects were used in this paper using three different databases containing various irregular heart states from MIT-BIH arrhythmia database (MITDB), MIT-BIH supraventricular arrhythmia database (SVDB), and Charles Sturt diabetes complication screening initiative (DiSciRi) database. We proposed a simple yet effective biometric sample extraction technique for ECG samples with abnormal cardiac conditions to improve the person identification process. These sample points were then applied to four classifiers to verify the robustness of identification. Varying numbers of enrollment and recognition QRS complexes were used to validate the stability of the proposed method. Our experimentation results show that the biometric technique outperforms existing methods lacking the ability to efficiently extract features for biometric matching. This is evident by obtaining high accuracy results of 96.7% for MITDB, 96.4% for SVDB, and 99.3% for DiSciRi. Moreover, high sensitivity, specificity, positive predictive value, and Youden Index's values further verifies the reliability of the proposed method. This technique also suggests the possibility of improving the classification performance using ECG recordings with low sampling frequency and increased number of ECG samples.
Sidek et al. (Tue,) conducted a other in Abnormal cardiac conditions (n=164). Biometric sample extraction technique for ECG samples vs. Existing methods was evaluated on Person identification accuracy. A proposed biometric sample extraction technique for ECG signals with abnormal cardiac conditions achieved high person identification accuracy of 96.7% for MITDB, 96.4% for SVDB, and 99.3% for DiSciRi.