Towards Real Time Implementation of Sparse Representation Classifier (SRC) Based Heartbeat Biometric System

Tan, Weng Chun; Yeap, H. M.; Chee, Kai Jye; Ramli, Dzati Athiar

doi:10.1007/978-3-319-03967-1_15

Cited by 10 publications

(14 citation statements)

References 10 publications

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“…The method used WT for feature extraction and back propagation multilayer perceptron artificial neural network (BP-MLP-ANN) for classification and the accuracy rate on 10 volunteers reached 90.52%, the EER reached 9.48%. Tan et al [ 34 ] demonstrated a new method for heart sound authentication. The pre-processing is based on low-pass filtering, and then the heart sounds are segmented using zero-crossing rate (ZCR) and short-term amplitude (STA) techniques to extract S1 and S2 sounds.…”

Section: Resultsmentioning

confidence: 99%

Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Cheng

Wang

She

2020

Entropy

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In this paper, a new method of biometric characterization of heart sounds based on multimodal multiscale dispersion entropy is proposed. Firstly, the heart sound is periodically segmented, and then each single-cycle heart sound is decomposed into a group of intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). These IMFs are then segmented to a series of frames, which is used to calculate the refine composite multiscale dispersion entropy (RCMDE) as the characteristic representation of heart sound. In the simulation experiments I, carried out on the open heart sounds database Michigan, Washington and Littman, the feature representation method was combined with the heart sound segmentation method based on logistic regression (LR) and hidden semi-Markov models (HSMM), and feature selection was performed through the Fisher ratio (FR). Finally, the Euclidean distance (ED) and the close principle are used for matching and identification, and the recognition accuracy rate was 96.08%. To improve the practical application value of this method, the proposed method was applied to 80 heart sounds database constructed by 40 volunteer heart sounds to discuss the effect of single-cycle heart sounds with different starting positions on performance in experiment II. The experimental results show that the single-cycle heart sound with the starting position of the start of the first heart sound (S1) has the highest recognition rate of 97.5%. In summary, the proposed method is effective for heart sound biometric recognition.

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Section: Resultsmentioning

confidence: 99%

Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Cheng

Wang

She

2020

Entropy

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“…The using of WT for feature extraction and MLP ANN for classification achieved an accuracy of 90.52% on 10 volunteers and an EER of 9.48%. Tan et al showed a new approach for cardiac sounds authentication based on low‐pass filtering for preprocessing. They were segmented to extract S1 and S2 sound using zero‐crossing rate (ZCR) and short‐Term amplitude (STA) techniques.…”

Section: Resultsmentioning

confidence: 99%

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time‐scale domains

El‐Dahshan

Bassiouni

2019

Computational Intelligence

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Biometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT‐11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de‐noising of cardiac sounds using multiresolution‐decomposition and multiresolution‐reconstruction (MDR‐MRR). The de‐noised signal is then segmented based on frame‐windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel‐frequency) and time‐scale (T‐S) domain (based on Wavelet Transform) features are extracted from the de‐noised signal after segmentation. The features, extracted from the Cp‐domain and the T‐S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K‐nearest neighbor (KNN). The performance of the classifications is assessed based on the k‐fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big‐O measurements. The T‐S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T‐S domain.

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“…In 2014, Tan et al . [14] presented a new approach for human authentication using sparse representation classifier (SRC) for heart sounds. For pre‐processing, heart sounds were low‐pass filtered, then, they were segmented to extract S 1 and S 2 sounds using zero‐crossing rate and short‐term amplitude techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, in [14], Tan et al . used HSCT‐11 database to evaluate their system in identification mode.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

“…Cepstral features, specifically mel‐frequency cepstral coefficient (MFCC) features, have proved to have a great performance in automatic speech and speaker recognition applications because the human ear resolves frequencies non‐linearly across the audio spectrum [7–9]. As PCG signal has acoustic nature, MFCC was adopted in many previously implemented PCG biometric authentication systems [10–14]. However, the main drawback with the mel‐scale is that it is approximately linear in the frequency range of PCG signal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heart‐ID: human identity recognition using heart sounds based on modifying mel‐frequency cepstral features

et al. 2016

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Towards Real Time Implementation of Sparse Representation Classifier (SRC) Based Heartbeat Biometric System

Cited by 10 publications

References 10 publications

Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time‐scale domains

Heart‐ID: human identity recognition using heart sounds based on modifying mel‐frequency cepstral features

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