Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals

Babaei, Sepideh; Geranmayeh, Amir

doi:10.1016/j.compbiomed.2008.10.004

Cited by 86 publications

(37 citation statements)

References 13 publications

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“…Choi et al [16] presented autoregressive power spectral density curves of DWT as a new feature for PCG signals classification and achieved 99.5% sensitivity and 99.9% specificity to classify normal heart sounds from pathological ones. Babaei et al [17] utilized the main statistical characteristics of PCG signals such as mean and standard deviation in different level of DWT decomposition and acquired 94.24% accuracy for classifying AR, AS and PS (pulmonary stenosis). A framework was proposed by Yuenyong et al [6] based on DWT and without segmentation, for heart sound classification and gained 92% accuracy to classify a number of different heart valve disorders.…”

Section: Constructing the Hybrid Classifiermentioning

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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Section: Constructing the Hybrid Classifiermentioning

confidence: 99%

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

Safara¹,

Doraisamy²,

Azman³

et al. 2013

IJBBB

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“…Linear phase-filtering, which prevents phase and timing distortion, is important because the heart sound has non-stationary characteristic. A CIC filter is composed of a high-pass and a low-pass filter and its transfer function is described in (1) and (2). N hpf , N lpf , and M indicate the high-pass filter order, low-pass filter order, and number of cascaded stages, respectively; their values were 16, 8, and 2, respectively, in the filter design.…”

Section: Cascade Integrator Comb Filtermentioning

confidence: 99%

“…Moreover, the diagnosis of valve dysfunction and hemodynamic research are widely known applications of heart sound analysis. For example, cardiac valve disorder has been classified by a neural network classifier [2] and blood pressure has been estimated with pattern analysis [3] using heart sounds. Furthermore, the relationship between S 2 and aortic blood pressure has been shown [4].…”

Section: Introductionmentioning

confidence: 99%

A Localization Method for First and Second Heart Sounds Based on Energy Detection and Interval Regulation

Min¹,

Shin²

2015

Journal of Electrical Engineering and Technology

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-The present study suggests a localization method for the first (S 1 ) and the second (S 2 ) feature of heart sounds, based on an algorithm involving frequency filtering, energy detection, and interval regulation. Localization accuracy was evaluated by comparing the algorithm with the traditional Hilbert transform-based localization method. Results show that the sensitivity and the positive predictivity value of proposed method, respectively, were 97.27 % and 99.94 % in S 1 detection and 94.99 % and 100 % in S 2 detection.

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“…So far the most common modeling methods are partial least squares (PLS) [4], support vector machine (SVM) [5] and back propagation neural network (BPNN) [6]. These methods have been used widely in electrophysiological studies [7][8][9]. In this paper, a fuzzy linguistic prediction model (FLPM) was used for sinoatrial node field potential analysis in high glucose environment.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment

Feng

Cao

Wang

et al. 2015

BME

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Abstract. The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.

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Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals

Cited by 86 publications

References 13 publications

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

A Localization Method for First and Second Heart Sounds Based on Energy Detection and Interval Regulation

Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment

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