Heart Sound Biometric System Based on Marginal Spectrum Analysis

Zhao, Zhidong; Shen, Qinqin; Ren, Fangqin

doi:10.3390/s130202530

Cited by 29 publications

(23 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors claim to make use of EMD in its basic form to detect coronary artery disease based on the instantaneous frequency calculated from diastolic murmurs using EMD and SVM. In another study [85] by the same authors, signal was denoised using db5 and, ensemble EMD (E-EMD) was then applied. EEMD removes the mode mixing problem in traditional EMD.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

“…have been employed in most cases. Specific measures like mean prediction power and mean accuracy [92], correct recognition rate (CRR) [85], correct and incorrect diagnosis [84], and SNR and ratio R [88] have also been reported. EMD algorithm is compared with other time-frequency algorithms like wavelet and DWT in [87,88,101].…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

See 1 more Smart Citation

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Ismail

Siddiqi

Akram

2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together.

show abstract

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Ismail

Siddiqi

Akram

2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Amplitude variation and instantaneous frequency not only improve the effectiveness of decomposition significantly, but also make HT based on EEMD suitable for non-stationary signals. The transformations of amplitude and frequency can be clearly separated by using each IMF component’s expansion, which mitigates Fourier transform’s limitation in terms of invariable amplitude and frequency [ 23 ]. The time-frequency-amplitude distribution is designated as the signal’s Hilbert spectrum H (ω, t ), which can accurately describe amplitude changes with time and frequency and further reflect the signal’s inherent time-varying characteristics.…”

Section: Basic Theory Of Eemd and Marginal Spectrummentioning

confidence: 99%

Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD

Cai

Lin

Zhang

et al. 2015

Sensors

View full text Add to dashboard Cite

As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs’ confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault.

show abstract

“…These cepstrum coefficients are MFCC and LPCC, which are applied to 100 heart sounds of 50 people to test the algorithm. Zhao et al [ 11 ] proposed a heart sound system based on marginal spectrum analysis and the classifier is based on VQ. Babiker et al [ 12 ] present the design of a system for access control using a heart sound biometric signature based on energy percentage in each wavelet coefficients and MFCC feature.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Wang

She

2020

Entropy

View full text Add to dashboard Cite

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.

show abstract

Heart Sound Biometric System Based on Marginal Spectrum Analysis

Cited by 29 publications

References 16 publications

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD

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

Contact Info

Product

Resources

About