Cumulant-based trapezoidal basis selection for heart sound classification

Safara, Fatemeh

doi:10.1007/s11517-015-1394-4

Cited by 9 publications

(5 citation statements)

References 28 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, identification of S 2 (SHSD) at the event level, was reported in [25], [29], [45], [47], [52], [57], [64], [87]- [89], [92], [94], [96], [100], and [104], achieving a mean accuracy of 93.96 ± 5.01%; while the mean classification accuracy reported in [90], [106], and [111] was 90.82 ± 6.58%. Pathological heart sounds detection (PHSD) at the event level reported in [29], [64], [65], [67], and [112], achieved mean accuracy of 88.50 ± 5.93%, while pathological heart sounds classification (PHSC) reported in [64], [69], [75], [78], [95], [105], [110], [140], [142], [145], [146], [155], [157], [158], [162]- [164], [167], [170], [183], [185], and [191], achieved mean classification accuracy of 90.28 ± 7.82%. The mean accuracy in the identification of S 1 at the event level was found to be the highest.…”

Section: Synthesis Of Resultsmentioning

confidence: 99%

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

2019

View full text Add to dashboard Cite

Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peerreviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.

show abstract

Section: Synthesis Of Resultsmentioning

confidence: 99%

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

2019

View full text Add to dashboard Cite

show abstract

“…Experimental data included the different categories: normal heart sounds, aortic stenosis, mitral regurgitation, and aortic regurgitation. The results were promising to achieve indicate the capabilities of HOC of wavelet packet coefficients [9]. In 2016, Deng Shiwen proposed a novel heart sound classification method based on the autocorrelation feature and diffusion map.…”

Section: Introductionmentioning

confidence: 94%

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

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…The difficulty of that approach is to adjust a few thresholds for reconstruction of the cardiac sound signal [14]. Moreover, other study used external reference such as Electrocardiogram (ECG) signal for detection of S1 and S2 in PCG signal [15].…”

Section: Figure 1 a General Block Diagram Of Automated Cardiac Soundmentioning

confidence: 99%

Detection of cardiac sounds components: a pilot study

Jamal

Ibrahim

Sha’abani

et al. 2020

IJEECS

View full text Add to dashboard Cite

<span>This paper presents a preliminary study related to the detection and identification of cardiac sounds components including first sound (S1), second sound (S2) and murmurs. Detection and identification of cardiac sounds are an important process in automated cardiac sound analysis system in order to automatically diagnose people who are having cardiovascular disorder and determine the existence of murmurs. Sixteen of recorded cardiac sounds (eight normal cardiac sounds, four abnormal cardiac sounds with systole murmur, and four abnormal cardiac sounds with diastole murmur) from PASCAL Classifying Heart Sounds Challenge database were examined for analysis. This work is significant in studying the time and time-frequency based detection of cardiac sounds components characteristics. In time-based analysis, envelope of signal energy was used to do the peak detection of S1, S2 and murmur and also analysis of cardiac cycle, systole and diastole duration. While time-frequency based analysis was used to determine the S1, S2 and murmur frequency range. The findings yield the overall accuracy of envelope-based detection for normal cardiac sound signal at 60.85% while for abnormal cardiac sound signal at 57.24%.</span>

show abstract

Cumulant-based trapezoidal basis selection for heart sound classification

Cited by 9 publications

References 28 publications

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

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

Detection of cardiac sounds components: a pilot study

Contact Info

Product

Resources

About