Heart sound segmentation using fractal decomposition

Thomas, Rijil; Ling, Lieng H.; Boon, Soh Cheong; Gunawan, E.

doi:10.1109/embc.2016.7592153

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…The segmentation performance of the proposed hybrid method is compared with various benchmark methods and the results are tabulated in Table 6. The methods used for comparison are VMD-SEE [10], FD [13], improved EMD [8], wavelet [26], and adaptive wavelet [26]. It is clear from extracted Hilbert envelope from first mode, e detected boundaries after thresholding, f denoised PCG signal with starting and ending locations of fundamental heart sounds [8] Own data --99.74 -Adap.…”

Section: Evaluation Using Performance Matricesmentioning

confidence: 99%

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A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

et al. 2019

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Segmentation of fundamental heart sounds-S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This paper proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation.

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Section: Evaluation Using Performance Matricesmentioning

confidence: 99%

“…In this method, the detection is done based on VMD algorithm. Logistic-regression technique [12], multifractal approach [13], deep neural network with mel-frequency cepstral coefficient (MFCC) features [1], hidden Markov model (HMM) [14] are also proposed for fundamental heart sound segmentation.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

et al. 2019

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“…Many processing and classification methods about heart sounds have been proposed such as wavelet transform (WT), hidden semi-Markov model (HSMM), logistic regression (LR), Mel-Frequency Cepstral Coefficients (MFCC), ensemble empirical mode decomposition (EEMD), deep neural network (DNN), deep convolutional neural network (CNN), Multi-fractal decomposition, Shannon energy and SVM [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. The feature extraction methods were mainly based on these features, including short-time Fourier transform (STFT) features, kurtosis features, the wavelet features, deep structured features and the statistical features [ 3 , 5 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

2019

Entropy

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Heart is an important organ of human beings. As more and more heart diseases are caused by people’s living pressure or habits, the diagnosis and treatment of heart diseases also require technical improvement. In order to assist the heart diseases diagnosis, the heart sound signal is used to carry a large amount of cardiac state information, so that the heart sound signal processing can achieve the purpose of heart diseases diagnosis and treatment. In order to quickly and accurately judge the heart sound signal, the classification method based on Wavelet Fractal and twin support vector machine (TWSVM) is proposed in this paper. Firstly, the original heart sound signal is decomposed by wavelet transform, and the wavelet decomposition coefficients of the signal are extracted. Then the two-norm eigenvectors of the heart sound signal are obtained by solving the two-norm values of the decomposition coefficients. In order to express the feature information more abundantly, the energy entropy of the decomposed wavelet coefficients is calculated, and then the energy entropy characteristics of the signal are obtained. In addition, based on the fractal dimension, the complexity of the signal is quantitatively described. The box dimension of the heart sound signal is solved by the binary box dimension method. So its fractal dimension characteristics can be obtained. The above eigenvectors are synthesized as the eigenvectors of the heart sound signal. Finally, the twin support vector machine (TWSVM) is applied to classify the heart sound signals. The proposed algorithm is verified on the PhysioNet/CinC Challenge 2016 heart sound database. The experimental results show that this proposed algorithm based on twin support vector machine (TWSVM) is superior to the algorithm based on support vector machine (SVM) in classification accuracy and speed. The proposed algorithm achieves the best results with classification accuracy 90.4%, sensitivity 94.6%, specificity 85.5% and F1 Score 95.2%.

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“…Introduction: Accurate and reliable recognition of the fundamental heart sounds (FHSs) in a cardiac cycle of the phonocardiogram (PCG) signal plays a vital role in valvular split, cardiac stress, pulmonary artery pressure analysis, left ventricular pressure rise (LV dP/dt) measure, non-invasive blood pressure estimation and human identification [1][2][3][4][5]. In the past years, many S1/S2 sound recognition methods were presented based on variety of preprocessing and feature extraction techniques and classifiers such as logistic regression-hidden semi-Markov model (HSMM) and wavelet transform (WT) [4], mel-frequency cepstral coefficients (MFCCs) and deep neural networks (DNNs) [6], multifractal decomposition [7], deep convolutional neural network (CNN) and support vector machine (SVM) [8], ensemble empirical mode decomposition (EEMD) and kurtosis features [9], total variation and Shannon energy (SE) [10], S-transform [11], empirical mode decomposition (EMD) WT algorithm with SE [12], Hilbert transform and adaptive thresholding [13], expert frequency-energy based metric [14], matching pursuit [15], duration-dependent hidden Markov model (HMM) [16], sound energy [17], high-frequency signatures (HFSs) [18], homomorphic envelogram and self-organising probabilistic model [19], adaptive sub-level tracking and Shannon-energy tracking [20], probabilistic models [21], time-delay neural network (NN) [22], NN and conventional classifiers [23], and spectral tracking [24].…”

mentioning

confidence: 99%

“…Chen et al [6] proposed S1 and S2 recognition algorithm based on the MFCCs and DNNs. Thomas et al [7] proposed the multifractal property-based method to identify S1 and S2 heart sounds (HSs). Tschannen et al [8] proposed a robust method for HS classification that combines a deep CNN-based feature extractor and an SVM.…”

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confidence: 99%

Wavelet‐based fundamental heart sound recognition method using morphological and interval features

Varghees

Ramachandran

2018

Healthcare Technology Letters

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Accurate and reliable recognition of fundamental heart sounds (FHSs) plays a significant role in automated analysis of heart sound (HS) patterns. This Letter presents an automated wavelet-based FHS recognition (WFHSR) method using morphological and interval features. The proposed method first performs the decomposition of phonocardiogram (PCG) signal using a synchrosqueezing wavelet transform to extract the HSs and suppresses the murmurs, low-frequency and high-frequency noises. The HS delineation (HSD) is presented using Shannnon energy envelope and amplitude-dependent thresholding rule. The FHS recognition (FHSR) is presented using interval, HS duration and envelope area features with a decision-rule algorithm. The performance of the method is evaluated on PASCAL HSs Challenge, PhysioNet/CinC HS Challenge, eGeneralMedical databases and real-time recorded PCG signals. Results show that the HSD approach achieves an average sensitivity (Se) of 98.87%, positive predictivity (Pp) of 97.50% with detection error rate of 3.67% for PCG signals with signal-to-noise ratio of 10 dB, and outperforms the existing HSD methods. The proposed FHSR method achieves a Se of 99.00%, Sp of 99.08% and overall accuracy of 99.04% on both normal and abnormal PCG signals. Evaluation results show that the proposed WFHSR method is able to accurately recognise the S1/S2 HSs in noisy real-world PCG recordings with murmurs and other abnormal sounds.

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Heart sound segmentation using fractal decomposition

Cited by 11 publications

References 14 publications

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

Wavelet‐based fundamental heart sound recognition method using morphological and interval features

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