Key result
An automatic person identification scheme using autoregressive Burg modeling of phonocardiogram beats and an ensemble bag trees classifier achieved satisfactory identification performance on 50 individuals.
Why the study?
With the rapid advancement of biosensors and increasing demand for more secure biometric authentication systems, phonocardiogram signals offer an easily acquired cardiac signal for person identification.
An automatic person identification scheme using autoregressive modeling of phonocardiogram beats achieved satisfactory performance for biometric authentication.
Answers are grounded in this paper and cite their sources.
Phonocardiogram biometrics show promise in small cohorts; leaves open scalability and security validation before clinical consideration.
With the rapid advancement of biosensors and increasing demand of more secured biometric authentication system, cardiac signals are getting special attention. Because of its very simple acquisition technique, phonocardiogram (PCG) signal is getting popularity in this field. This paper presents an automatic person identification scheme based on autoregressive modeling of PCG beats. On a given PCG recording first preprocesing and then wavelet denoising are applied. In order to perform beat by beat operation, a segmentation scheme is proposed using the Hilbert envelope which extracts the PCG beat containing the first and second heart sounds. Next reflection coefficients are extracted by employing the AR Burg modeling of the PCG beat. Finally the AR Burg reflection coefficients are used in ensemble bag trees classifier to identify a person. Performance of the proposed method is being tested on PCG signals of 50 different person taken from a publicly available PCG dataset and very satisfactory identification performance is achieved.
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Fahad et al. (2019) studied Biometric authentication (n=50). Automatic person identification scheme based on autoregressive modeling of PCG beats was evaluated on Person identification performance. An automatic person identification scheme using autoregressive Burg modeling of phonocardiogram beats and an ensemble bag trees classifier achieved satisfactory identification performance on 50 individuals.
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