Phonocardiography-based mitral valve prolapse detection using an artificial neural network

Bogdanović, Vesna; Ivan, Bozic; Gavrovska, Ana; Stojic, Vladislava; Jakovljević, Vladimir

doi:10.5937/sjecr14-4436

Cited by 5 publications

References 25 publications

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Paediatric heart sound signal analysis towards classification using multifractal spectra

et al. 2016

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Healthy versus unhealthy heart sound computer-aided classification tools are very popular for supporting clinical decisions. In this paper a new method is proposed for the classification of heart sound recordings from a statistical standpoint without detection and localization of fundamental heart sounds (S1, S2). This study analyzes the possibility of detecting healthy heart sound signal from a large set of measurements, corresponding to different pathologies, such as aortic regurgitation, mitral regurgitation, aortic stenosis and ventricular septal defects. The proposed method employs singularity spectra analysis and long-term dependency of irregular structures. Healthy signals are firstly separated from the rest of the recordings. In the second step, the signals with a click syndrome, used here as a reference, are detected in the unhealthy group. Innocent murmurs have not been considered in this paper. Each auscultatory recording is classified into one of the following classes: healthy; click syndrome; and other heart dysfunctions. The results of the proposed method provided high recall and precision values for each of the three classes. Since the presence of additive noise may affect the classification, we also analyzed the possibility of classifying signals in such circumstances. The method was tested, verified and showed high accuracy.

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Paediatric heart sound signal analysis towards classification using multifractal spectra

et al. 2016

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Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Mehrabbeik¹,

Rashidi²,

Fallah³

et al. 2020

Biomed. Phys. Eng. Express

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Mitral Valve Prolapse (MVP) is a common condition among people, which is often benign and does not need any serious treatment. However, this doesn’t mean that MVP can’t cause any problems. In malignant conditions, MVP can cause mitral failure and also heart failure. Early diagnosis of MVP is significantly important to control and reduce its complications. Since the phonocardiogram signal provides useful information about heart valves function, it can be used for MVP detection. To detect MVP, the signal was denoised and segmented into heart cycles and constant three-second pieces in the first and second approaches, respectively. Next, based on the Fractional Fourier Transform (FrFT), the desired features were extracted. Then, the extracted features were windowed by a Moving Logarithmic Median Window (MLMW) and optimum features were selected using Mahalanobis, Bhattacharyya, Canberra, and Minkowski distance criteria. Finally, using the selected features, classification was performed by using the K-Nearest Neighbor (KNN) and the Suppor Vector Machine (SVM) classifiers to find out whether a segment is prolapsed. The best results of the experiment on the collected database contain 15 prolapsed and 6 non-prolapsed subjects using the A-test method show 96.25 ± 2.43 accuracy, 98.5 ± 3.37 sensitivity, 94.0 ± 5.16 specificity, 96.0 ± 3.44 precision, 92.5 ± 4.86 kappa, and 96.6 ± 2.34 f-score with the SVM classifier.

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Review of some new methods for analyzing vital heart signals

Reljin

Gavrovska

Paskas

2014

2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)

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Phonocardiography-based mitral valve prolapse detection using an artificial neural network

Cited by 5 publications

References 25 publications

Paediatric heart sound signal analysis towards classification using multifractal spectra

Paediatric heart sound signal analysis towards classification using multifractal spectra

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Review of some new methods for analyzing vital heart signals

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