Recognition and classification of cardiac murmurs using ANN and segmentation

Rios-Gutiérrez, Fernando; Alba-Flores, Rocio; Strunic, Spencer

doi:10.1109/conielecomp.2012.6189912

Cited by 10 publications

(11 citation statements)

References 11 publications

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“…The classification at this stage is very important because heart murmurs in newborns are more difficult to diagnose [5]. We achieve a high-resolution result for of heart murmurs analysis by high order spectral methods such as Bispectrum and Wigner distribution [8].…”

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confidence: 99%

Diagnosis and Classification of Systolic Murmur in Newborns

Amiri¹,

Armano²

2013

Computer Graphics and Imaging / 798: Signal Processing, Pattern Recognition and Applications

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The problem addressed in this paper is the detection and classification of systolic murmurs in newborns. As the estimates of higher-order spectra have been shown to be useful in various signal processing problems, this paper uses bispectra and Wigner distribution for systolic murmurs detection. The diagnostic system that has been implemented is based on artificial neural networks, can be used for detecting and classifying systolic murmurs. The system outputs the classification of the sound as either normal (innocent murmurs) or a type of pathological systolic murmurs. The ultimate goal of this research is to implement a heart sounds diagnostic system that can be used to help physicians in the auscultation of patients, so to reduce number of unnecessary echocardiograms and prevent the release of newborns that are in fact patients. In this study, 96.4% accuracy, 97% sensitivity, and 97% specificity were obtained on a dataset of 56 samples.

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confidence: 99%

Diagnosis and Classification of Systolic Murmur in Newborns

Amiri¹,

Armano²

2013

Computer Graphics and Imaging / 798: Signal Processing, Pattern Recognition and Applications

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“…A variety of other features including murmur likelihood as temporal feature and HMM state likelihood followed by support vector machine (SVM) is proposed by Kwak and Kwon (). Artificial neural network (ANN) based recognition of the cardiac valve disorder has been performed using systole frequency and diastole frequency as features (Gutierrez, Flores, & Strunic, ). The entropy of wavelet coefficients is a useful feature for the comparative study of different classifiers (Safara, Doraisamy, Azman, Jantan, & Ranga, ).…”

Section: Introductionmentioning

confidence: 99%

Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM

Fahad

Khan

Saba

et al. 2018

Microscopy Res & Technique

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Auscultation of heart dispenses identification of the cardiac valves. An electronic stethoscope is used for the acquisition of heart murmurs that is further classified into normal or abnormal murmurs. The process of heart sound segmentation involves discrete wavelet transform to obtain individual components of the heart signal and its separation into systole and diastole intervals. This research presents a novel scheme to develop a semi-automatic cardiac valve disorder diagnosis system. Accordingly, features are extracted using wavelet transform and spectral analysis of input signals. The proposed classification scheme is the fusion of adaptive-neuro fuzzy inference system (ANFIS) and HMM. Both classifiers are trained using the extracted features to correctly identify normal and abnormal heart murmurs. Experimental results thus achieved exhibit that proposed system furnishes promising classification accuracy with excellent specificity and sensitivity. However, the proposed system has fewer classification errors, fewer computations, and lower dimensional feature set to build an intelligent system for detection and classification of heart murmurs.

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“…proposed a segmentation method to overcome the shortcomings of traditional algorithms by segmenting a heart margin on the basis of the mixture model of heart tissue. Others employed a neural network with iterative convergence by minimizing the energy function to automatically realize the segmenting of heart image sequences. Fritz proposed a 4D–CT left ventricle inside and outside of the heart membrane segmentation model based on random walks, active shape models and capacity curves.…”

Section: Introductionmentioning

confidence: 99%