Classification of heart sounds using discrete and continuous wavelet transform and random forests

Balili, Christine; Sobrepena, Ma. Caryssa C.; Naval, Prospero C.

doi:10.1109/acpr.2015.7486584

Cited by 31 publications

(14 citation statements)

References 9 publications

(10 reference statements)

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“…Other works have been developed, based on the segmentation of PCG signals using different methods of envelope extraction, such as Shannon energy [17], Shannon entropy [18], Hilbert-Huang transform [19], and the autocorrelation [20], there are other approaches based on the wavelet transform to add the frequency features of S1 and S2 [21]. After, they proceed to extract the temporal, frequency and timefrequency features from the PCG signals, in order to classify heart sound records into normal/abnormal by using one of the classifiers most recognized (SVM, KNN, ANN, etc,).…”

Section: Related Workmentioning

confidence: 99%

Heart Sounds Classification for a Medical Diagnostic Assistance

et al. 2019

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<span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.</span>

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Section: Related Workmentioning

confidence: 99%

Heart Sounds Classification for a Medical Diagnostic Assistance

et al. 2019

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“…There are a large number of feature extraction algorithms available. These include the Fourier transform [19], the short time Fourier transform (STFT) [6], the time-frequency representation (TFR) [2], the MFCC [9] and the Discrete Wavelet Transform (DWT) coefficients [3]. However, the most widely used algorithms are the MFCC and the DWT.…”

Section: Related Workmentioning

confidence: 99%

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Nogueira

Ferreira

Jorge

2017

Progress in Artificial Intelligence

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Phonocardiogram signals contain very useful information about the condition of the heart. It is a method of registration of heart sounds, which can be visually represented on a chart. By analyzing these signals, early detections and diagnosis of heart diseases can be done. Intelligent and automated analysis of the phonocardiogram is therefore very important, to determine whether the patient's heart works properly or should be referred to an expert for further evaluation. In this work, we use electrocardiograms and phonocardiograms collected simultaneously, from the Physionet challenge database, and we aim to determine whether a phonocardiogram corresponds to a "normal" or "abnormal" physiological state. The main idea is to translate a 1D phonocardiogram signal into a 2D image that represents temporal and Mel-frequency cepstral coefficients features. To do that, we develop a novel approach that uses both features. First we segment the phonocardiogram signals with an algorithm based on a logistic regression hidden semi-Markov model, which uses the electrocardiogram signals as reference. After that, we extract a group of features from the time and frequency domain (Mel-frequency cepstral coefficients) of the phonocardiogram. Then, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, we run a binary classifier to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, we study the contribution of temporal and Mel-frequency cepstral coefficients features and evaluate three classification algorithms: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when we map both temporal and Mel-frequency cepstral coefficients features into a 2D image and use the Support Vector Machines with a radial basis function kernel. Indeed, by including both temporal and Mel-frequency cepstral coefficients features, we obtain sligthly better results than the ones reported by the challenge participants, which use large amounts of data and high computational power.

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“…Most commonly these methods utilize more or less complex conventional techniques like wavelet transformation [4], and frequency analysis [11], etc. to extract features from the signal and perform some kind of classification over them like is presented in [12]- [14]. In recent work, the authors have also presented various kinds of hybrid methods tackling the signal segmentation tasks, including the heart sound signal segmentation tasks [15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

Vrbančič¹,

Fister²,

Podgorelec³

2019

ElAEE

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The analysis of non-stationary signals commonly includes the signal segmentation process, dividing such signals into smaller time series, which are considered stationary and thus easier to process. Most commonly, the methods for signal segmentation utilize complex filtering, transformation and feature extraction techniques together with various kinds of classifiers, which especially in the field of biomedical signals, do not perform very well and are generally prone to poor performance when dealing with signals obtained in highly variable environments. In order to address these problems, we designed a new method for the segmentation of heart sound signals using deep convolutional neural networks, which works in a straightforward automatic manner and does not require any complex pre-processing. The proposed method was tested on a set of heartbeat sound clips, collected by non-experts with mobile devices in highly variable environments with excessive background noise. The obtained results show that the proposed method outperforms other methods, which are taking advantage of using domain knowledge for the analysis of the signals. Based on the encouraging experimental results, we believe that the proposed method can be considered as a solid basis for the further development of the automatic segmentation of highly variable signals using deep neural networks.

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Classification of heart sounds using discrete and continuous wavelet transform and random forests

Cited by 31 publications

References 9 publications

Heart Sounds Classification for a Medical Diagnostic Assistance

Heart Sounds Classification for a Medical Diagnostic Assistance

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

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