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Toward improving person identification using the ElectroCardioGram (ECG) signal based on non-fiducial features

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Abstract

In the last years, the ElectroCardioGram (ECG) signal identification system presents an important technology due to the expanding domain of applications. The ECG signal of each person is composed of P-wave, T-wave, and QRS-complex. In fact, any state of the person (e.g. the stress, the physical activity) the shape of the heartbeat is not easy to change but can increase the heart rate. The paper presents an efficient person identification system based on ECG signal. Our identification system is realized in different steps such as preprocessing, detection of R peaks, segmentation, features extraction, and classification. After the processing step and the detection of R peaks step, we realized the segmentation step. In this work, we used two cases of the segmentation, namely, fragments with single R peak and fragments with two R peaks. Then, in the step of the features extraction, we interested in the non-fiducial features. We proposed to use an integration of non-fiducial parameters like cepstral coefficients, Zero Crossing Rate (ZCR), and entropy. In addition, the Support Vector Machines (SVM) is use for the classification system. The integration of the different characteristics are evaluated using two benchmarks databases namely Massachusetts Institute of Technology-Boston’s Beth Israel Hospital (MIT-BIH) Arrhythmia and ECG-ID database obtained from the Physionet database. The Experimental results present that our characteristics can receive high person, an accuracy equal to 100%, 100%, 100%, and 99.01% that are from the MIT-BIH database (47 subjects), ECG-ID (12 subjects) (Five-recording), ECG-ID (90 subjects) (Two-recording), and ECG-ID (90 subjects) (All-recording), respectively.

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  1. https://archive.physionet.org/cgi-bin/atm/ATM

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Authors and Affiliations

  1. Multimedia InfoRmation systems and Advanced Computing Laboratory, MIRACL University of Sfax, Sfax, Tunisia

    Sihem Hamza & Yassine Ben Ayed

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  1. Sihem Hamza
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  2. Yassine Ben Ayed
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Correspondence to Sihem Hamza.

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Hamza, S., Ayed, Y.B. Toward improving person identification using the ElectroCardioGram (ECG) signal based on non-fiducial features. Multimed Tools Appl 81, 18543–18561 (2022). https://doi.org/10.1007/s11042-022-12244-0

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