Heart sound analysis using neural and statistical classifiers: a comparison

Barschdorff, D.; Bothe, Achim; Rengshausen, U.

doi:10.1109/cic.1989.130582

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…After extracting the features from each signal, in a classification problem we need to learn a model that discriminates between normal and abnormal heart sounds. Most of the previous studies that learn models to classify heart sounds use artificial neural networks (ANN) or Support Vector Machines (SVM) [4]. In [14], Gupta et al addressed the problem of distinguishing between two abnormal and one normal heart states.…”

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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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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“…The instantaneous frequency [3] is estimated using the central-finite difference frequency estimate (CFDFE) [4], that is defined as…”

Section: Instantaneous Energy and Frequency Estimation (Iefe)mentioning

confidence: 99%

“…The signal parameters for the various types of heart sounds and murmurs are estimated from the IEFE [4] as follows 1 EJE2 -energy ratio of the S1 and S2.…”

Section: Definition Of Signal Parametersmentioning

confidence: 99%

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An expert system approach for classification of heart sounds and murmurs

Sharif

Daliman

Sha'ameri³

et al.

Proceedings of the Sixth International Symposium on Signal Processing and Its Applications (Cat.No.01EX467)

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Digital Signal Processing Lab Faculty of Electrical Engineering Universiti Teknologi Malaysia 8 13 IO UTM Skudai Johor Darul Takzim Fax: 60 7 556 6272 Email : shaparas@hotmail.com PARAMETER ESTIMATIONThe various types of heart valvular problems can be classified from the auscultation of heart sounds and murmurs that only requires the use of the stethoscope. To discriminate the various heart sounds and murmurs, the signal parameters are estimated using the instantaneous energy and frequency estimation (IEFE) technique. The parameters are then used as inputs to expert system classifier to classify the heart sounds and murmurs based on the ruled-based method.

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“…The specific heart sound patterns can be easily listened by a stethoscope. Today the stethoscope is still well established, the phonocardiogram (PCG), reveals full information on diseases of cardiac valves, valvular defect, heart insufficiencies and heart throb [2]. Many attempts have been undertaken to automatically classify those signals using pattern recognition [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

An intelligent pattern recognition system based on neural network and wavelet decomposition for interpretation of heart sounds

Türkoğlu

Arslan²

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In this study, we develop a new automated pattern recognition system for interpretation of heart sound based on wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a noiseless environment. We generate features for the objective concise representation of heart sound signals by means of wavelet decomposition. Classification of the features is performed using a back propagation neural network with adaptive learning rate. With two hundred record windows obtained from young humans are studied. One hundred of the record windows in database are selected for use as training phase for neural network. In the test result of the intelligent pattern recognition system with ten different types heart sound signals are acquired a high success.

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Heart sound analysis using neural and statistical classifiers: a comparison

Cited by 9 publications

References 5 publications

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Classifying Heart Sounds Using Images of MFCC and Temporal Features

An expert system approach for classification of heart sounds and murmurs

An intelligent pattern recognition system based on neural network and wavelet decomposition for interpretation of heart sounds

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