PCG Classification Using Multidomain Features and SVM Classifier

Tang, Hong; Dai, Ziyin; Jiang, Yuanlin; Li, Ting; Liu, Chengyu

doi:10.1155/2018/4205027

Cited by 48 publications

(37 citation statements)

References 36 publications

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“…The variation of normal (9857) and abnormal samples (3158) results in degrading the performance of the model [34]. Author Sensitivity% Specificity% Accuracy% Tang et al [19] 88.00 87.00 88.00 Dominguez et al [12] 93.20 95.12 97.00 Bradley et al [11] 90.07 88.45 89.26 Mostafa et al [13] 76.96 88.31 82.63 Masun et al [14] 79.60 80.60 80.10 Plesinger et al [17] 89.00 81.60 85.00 Vykintas et al [20] 80.63 87.66 84.15 Wei et al [21] 98.33 84.67 91.50 Philip et al [24] 77.00 80.00 79.00 Singh et al [33] 93.00 90.00 90.00 This study 94.08 91.95 92.47…”

Section: Discussionmentioning

confidence: 99%

“…Kamson et al [18] segmented heart sounds based on a modified hidden semi-Markov model (HSMM) with an average F 1 score of 98.38%. Tang et al [19] derived the multimodal features based on the HSMM segmentation method and predicted the abnormality of heart sounds using the SVM classifier. Bradley et al [11] extracted the features using sparse coding and time domain followed by classification based on the SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

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Short unsegmented PCG classification based on ensemble classifier

Singh¹,

Majumder²

2020

Turk J Elec Eng & Comp Sci

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Diseases associated with the heart are one of the main reasons of death worldwide. Hence, early examination of the heart is important. For analysis of cardiac disorders, a study of heart sounds is a crucial and beneficial approach.Still, automated classification of heart sounds is a challenging task that mainly depends on segmentation of heart sounds and derivation of features using segmented samples. In the literature available for PCG classification provided by PhysioNet/CinC Challenge 2016, most of the research has focused on enhancing the accuracy of the classification model based on complicated segmentation processes and has failed to improve the sensitivity. In this paper, we present an automated heart sound classification by eliminating the segmentation steps using multidomain features, which results in enhanced sensitivity. The study is based on homomorphic envelogram, mel frequency cepstral coefficient (MFCC), power spectral density (PSD), and multidomain feature extraction. The extracted features are trained using the 5-fold cross-validation method based on an ensemble boosting algorithm over 100 independent iterations. Our proposed design is evaluated using public datasets published in PhysioNet/Computers in Cardiology Challenge 2016. Accuracy of 92.47% with improved sensitivity of 94.08% and specificity of 91.95% is achieved using our model. The output performance proves that our proposed model offers superior performance results.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short unsegmented PCG classification based on ensemble classifier

Singh¹,

Majumder²

2020

Turk J Elec Eng & Comp Sci

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show abstract

“…Make the data linear separable in high dimensional space. Two hyperplanes based on kernel function are obtained by solving Equations (11) and (12). The decision rule of TWSVM is that the testing sample point belongs to the class which hyperplane is close to it.…”

Section: B Twin Support Vector Machine (Twsvm)mentioning

confidence: 99%

“…They extracted a total of 515 features from nine feature domains. The experimental results showed that the sensitivity, specificity, and overall score were 0.88, 0.87, and 0.88, respectively [12]. Bozkurt Baris et al proposed the algorithm based on CNN.…”

Section: Introductionmentioning

confidence: 99%

Heart Sound Signal Classification Algorithm: A Combination of Wavelet Scattering Transform and Twin Support Vector Machine

et al. 2019

IEEE Access

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By classifying the heart sound signals, it can provide very favorable clinical information to the diagnosis of cardiovascular diseases. According to the characteristics of heart sound signals which are complex and difficult to classify and recognize, a new method of feature extraction and classification about heart sound signal is proposed by a combination of wavelet scattering transform and twin support vector machine in this paper. The method is as follows: The heart sound signal data set is firstly divided into two parts, one as a training set and the other as a testing set. Then the wavelet scattering transform is applied to the heart sound signals in the training set and the testing set. The scattering transform is a new timefrequency analysis method. It overcomes the shortcomings of the traditional wavelet transform which has the time-shift changes. It has the advantages of translation invariance and elastic deformation stability. Thus obtain the scattering feature matrix of the heart sound signal. Due to the large dimension of scattering feature matrix, this paper uses multidimensional scaling (MDS) method to reduce the dimension. This method is compared with the classical dimension reduction method-principal component analysis (PCA). Finally, the dimensionality-reduced feature matrix is input into the twin support vector machine (TWSVM) for training. After training the classifier to get the optimal parameters, the dimensionality-reduced scattering feature matrix of the testing signal is input into the classifier for testing. Experimental results show that the classification accuracy of the proposed method can reach 98% or more, and the running time is greatly reduced compared with support vector machine (SVM). INDEX TERMS Wavelet scattering transform, multidimensional scaling (MDS), twin support vector machine (TWSVM), signal classification.

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“…The authors of the winner solution of the challenge proposed to join the advantages of a neural network model that analysed raw data and a classical boosting classifier fed with time and frequency domain features [ 19 ]. The runner-up approach authors utilised a large set of different acoustic features extracted from various feature domains fed into a support vector machine (SVM) classifier [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

et al. 2021

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The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

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PCG Classification Using Multidomain Features and SVM Classifier

Cited by 48 publications

References 36 publications

Short unsegmented PCG classification based on ensemble classifier

Short unsegmented PCG classification based on ensemble classifier

Heart Sound Signal Classification Algorithm: A Combination of Wavelet Scattering Transform and Twin Support Vector Machine

A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

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