ECG Biometric with Abnormal Cardiac Conditions in Remote Monitoring System

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 44; no. 11; pp. 1498 - 1509
• Main Authors: Sidek, Khairul A., Khalil, Ibrahim, Jelinek, Herbert F.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormal cardiac condition; Accuracy; Arrhythmia; Bayes network; biomedical signal processing; biometric; Biometrics; Cardiac arrhythmia; Electrocardiography; Extraction; Feature extraction; Heart; kNN; Matching; multilayer perceptron (MLP); Networks; normalization; pattern classification; radial basis function (RBF); Recognition; Recording; Robustness; Sampling; multilayer perceptron (MLP); pattern classification; normalization; Abnormal cardiac condition; Bayes network; biomedical signal processing; kNN; radial basis function (RBF); biometric; electrocardiography
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2014.2336842

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.