pcg signals
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This study proposed a novel TFNNS method, which aimed to solve the imbalanced phonocardiogram (PCG) signals’ classification. TFFNS consisted of three submodules: HeartNet, 2D-Maps transformation, and TF-Mask augmentation. HeartNet, deep neural networks (CNNs), was designed to recognize the categories of PCG signals. In particular, on the basis of short-time Fourier transform and Mel filtering, 2D-Maps transformation was used to convert one-dimensional PCG into two-dimensional Savitzky-MFSC feature maps that were fed into HeartNet; TF-Mask augmentation was designed to augment the training datasets by randomly shielded Savitzky-MFSC maps in the domains of time and frequency. We trained our model on the PASCAL heart sounds’ datasets to classify three categories of heart sounds including normal, murmur, and extrasystole. We also evaluated and compared the model with the baselines on the consistent evaluation protocols. The experimental results showed that the proposed TFFNS method significantly promoted the performance of the PCG signals’ classification and exceeded the baselines by giving the mean precision of 94%, heart problem specificity of 99%, and discriminant power of 1.317.

This paper is concerned a “The Wigner distribution (WD)” analysis of the Heart cardiac (or phonocardiogram signals: PCG). The Wigner distribution (WD) and the corresponding WVD (Wigner Ville Distribution) have shown good performances in the analysis of non-stationary and quantitative measurements of the time-frequency PCG signal characteristics. It is shown that these transforms provides enough features of the PCG signals that will help clinics to obtain diagnosis.

This paper proposes an unsupervised way for Phonocardiogram (PCG) analysis, which uses a revised auto encoder based on distribution density estimation in the latent space. Auto encoders especially Variational Auto-Encoders (VAEs) and its variant β−VAE are considered as one of the state-of-the-art methodologies for PCG analysis. VAE based models for PCG analysis assume that normal PCG signals can be represented by latent vectors that obey a normal Gaussian Model, which may not be necessary true in PCG analysis. This paper proposes two methods DBVAE and DBAE that are based on estimating the density of latent vectors in latent space to improve the performance of VAE based PCG analysis systems. Examining the system performance with PCG data from the a single domain and multiple domains, the proposed systems outperform the VAE based methods. The representation of normal PCG signals in the latent space is also investigated by calculating the kurtosis and skewness where DBAE introduces normal PCG representation following Gaussian-like models but DBVAE does not introduce normal PCG representation following Gaussian-like models.

In this paper we discussed the heart valve disease. This heart valve disease occur throughout the world due to the more ethical estimation and grow curator of heart valve diseases use the diagnosis for this type of valve disease . Actually Phonocardiogram (PCG) signals are used because it having less price and acquire the signals. In this we learn five different kind of heart areas, Also typical are aortic stenosis, mitral valve prolapse, mitral stenosis and mitral regurgitation.