Wavelet Packet Entropy for Heart Murmurs Classification

Safara, Fatemeh; Doraisamy, Shyamala; Azman, Azreen; Jantan, Azrul Hazri; Ranga, Sri

doi:10.1155/2012/327269

Cited by 43 publications

(34 citation statements)

References 19 publications

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“…Due to the denoising of the signal the results achieved were better. Wavelet based PCG signal analysis achieved accuracy of 90% -97.56% [7,13,14,16]. …”

Section: Resultsmentioning

confidence: 99%

“…The best results were achieved using Bayes Net as a classifier with an accuracy of 96.94%. The results showed that the proposed wavelet packet entropy was effective for heart sounds classification [14]. In 2013, Sun et al combined Improved EMD (Empirical Mode Decomposition) and Shannon energy envelope algorithm for extraction of S1 and S2 components and achieved a high accuracy of 99.74% [15].…”

Section: Discussionmentioning

confidence: 99%

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Survey of Different Methodologies Used in Phonocardiogram Signal Analysis

Randhawa¹,

Singh²

2015

IJCA

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“…Due to the denoising of the signal the results achieved were better. Wavelet based PCG signal analysis achieved accuracy of 90% -97.56% [7,13,14,16]. …”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Survey of Different Methodologies Used in Phonocardiogram Signal Analysis

Randhawa¹,

Singh²

2015

IJCA

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“…Some variations of WPE have been proposed by several researchers. In [6], entropy was calculated using crest energy in each subband of WPD results. Meanwhile, Shannon method was used in WPD subband for bearing inspection in [16].…”

Section: Related Workmentioning

confidence: 99%

“…It uses Shannon entropy calculations on the subband of discrete wavelet transform (DWT) [5]. The improvement of the wavelet entropy is the wavelet packet entropy (WPE) that uses the wavelet packet decomposition (WPD) subband [6]. WE produced low accuracy when used as a feature for pulmonary sound classification as reported in [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multilevel Wavelet Packet Entropy using Various Entropy Measurement for Lung Sound Classification

Rizal¹,

Hidayat²,

Nugroho³

2019

ijacsa

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Wavelet Entropy (WE) is one of the entropy measurement methods by means of the discrete wavelet transform (DWT) subband. Some of the developments of WE are wavelet packet entropy (WPE), wavelet time entropy. WPE has several variations such as the Shannon entropy calculation on each subband of WPD that produces 2N entropy or WPE, which yields an entropy value. One of the WPE improvements is multilevel wavelet packet entropy (MWPE), which yields entropy value as much as N decomposition level. In a previous research, MWPE was calculated using Shannon method; hence, in this research MWPE calculation was done using Renyi and Tsallis method. The results showed that MWPE using Shannon calculation could yield the highest accuracy of 97.98% for N = 4 decomposition level. On the other hand, MWPE using Renyi entropy yielded the highest accuracy of 93.94% and the one using Tsallis entropy yielded 57.58% accuracy. Here, the test was performed on five lung sound data classes using multilayer perceptron as the classifier.

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“…PCG signal processing is one of the active research areas [1]- [3]. A considerable body of these researches is dedicated to heart sound classification.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

Safara¹,

Doraisamy²,

Azman³

et al. 2013

IJBBB

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Abstract-Numerous studies are being conducted in recent years focusing on phonocardiographic (PCG) signals due to their capability to characterize heart sounds. These characteristics can be exploited in developing computer-aided auscultation system as a complementary tool for clinicians in diagnosis of cardiovascular disorders. This study proposes a new type of features to distinguish five categories of heart sounds, including normal, mitral stenosis, mitral regurgitation, aortic stenosis, and aortic regurgitation. PCG signals were collected from online resources and training CDs. Wavelet packet transform was utilized for heart sound analysis as opposed to discrete wavelet transform that has been extensively used in the previous studies. Then, trapezoidal function was calculated for deriving feature vectors. A hybrid classifier was designed composing of three types of classifiers, multilayer perceptron (MLP) artificial neural network, k-nearest neighbor (KNN), and support vector machine (SVM), to classify feature vectors. The promising results demonstrate the effectiveness of the proposed trapezoidal features and hybrid classifier for heart sound classification.

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Wavelet Packet Entropy for Heart Murmurs Classification

Cited by 43 publications

References 19 publications

Survey of Different Methodologies Used in Phonocardiogram Signal Analysis

Survey of Different Methodologies Used in Phonocardiogram Signal Analysis

Comparison of Multilevel Wavelet Packet Entropy using Various Entropy Measurement for Lung Sound Classification

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

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