Design and evaluation of a parametric model for cardiac sounds

Ibarra-Hernández, Roilhi F.; Alonso-Arévalo, Miguel A.; Cruz-Gutiérrez, Alejandro; Licona-Chávez, Ana L.; Villarreal-Reyes, Salvador

doi:10.1016/j.compbiomed.2017.08.007

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…We have previously evaluated several time-frequency dictionaries to decompose the PCG, showing that Gabor wavelets accurately represent this signal [71] . For the experiments conducted in this research, the selected number of atoms was M a =15 in order to reach almost 99 % of the energy to reconstruct a PCG cycle.…”

Section: Feature Extractionmentioning

confidence: 99%

“…we selected the RF method for its simplicity and high performance [69] . The main reason to use MP+LPC as features stems from the heart sound reconstruction model we have previously proposed [71] . This sparse We suppose that this fact results from the higher diversity produced when taking the mean value of the features rather than the direct calculation of the features from a single averaged cycle.…”

Section: Feature Selectionmentioning

confidence: 99%

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Comparison of Spectral and Sparse Feature Extraction Methods for Heart Sounds Classification

Ibarra Hernández,

Alonso-Arévalo,

García-Canseco

2023

Rev. Mex. Ing. Biomed.

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Cardiovascular diseases (CVDs) remain the leading cause of morbidity worldwide. The heart sound signal or phonocardiogram (PCG) is the most simple, low-cost, and effective tool to assist physicians in diagnosing CVDs. Advances in signal processing and machine learning have motivated the design of computer-aided systems for heart illness detection based only on the PCG. The objective of this work is to compare the effects of using spectral and sparse features for a classification scheme to detect the presence/absence of a pathological state in a heart sound signal, more specifically, sparse representations using Matching Pursuit with multiscale Gabor time-frequency dictionaries, linear prediction coding, and Mel-frequency cepstral coefficients. This work compares the performance of PCGs classification applying features as a result of averaging the samples or the features for each PCG sound event when feeding a random forest (RF) classifier. For data balancing, random under-sampling and synthetic minority oversampling (SMOTE) methods were applied. Furthermore, we compare the Correlation Feature Selection (CFS) and Information Gain (IG) for the dimensionality reduction. The findings show a SE=93.17 %, SP=84.32 % and ACC=85.9 % when joining MP+LPC+MFCC features set with an AUC=0.969 showing that these features are promising to be used in heart sounds anomaly detection schemes.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Comparison of Spectral and Sparse Feature Extraction Methods for Heart Sounds Classification

Ibarra Hernández,

Alonso-Arévalo,

García-Canseco

2023

Rev. Mex. Ing. Biomed.

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“…In previous work we showed that this representation of the residual preserves some meaningful heart sound signal components. 37 • We considered two main approaches to build and extract PCG signal feature sets: feature averaging and cycle averaging. Most of Physionet challenge submissions focused on the first one, while the second one had not been evaluated with these recordings so far.…”

Section: Featuresmentioning

confidence: 99%

A benchmark of heart sound classification systems based on sparse decompositions

Ibarra-Hernández

Bertin

Alonso-Arévalo

et al. 2018

14th International Symposium on Medical Information Processing and Analysis

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Background: Nowadays, cardiovascular diseases (CVD) remain the main cause of death worldwide. A heart sound signal or phonocardiogram (PCG) is the most simple, economical and non-invasive tool to detect CVDs. Advances in technology and signal processing allow the design of computer-aided systems for heart illnesses detection from PCG signals. Purpose: The paper proposes a pipeline and benchmark for binary heart sounds classification. The features extraction architecture is focused on the use of Matching Pursuit time-frequency decomposition using Gabor dictionaries and the Linear Predictive Coding method of a residual. We compare seven classifiers with two different approaches: feature averaging and cycle averaging. Methods: We test our proposal on the PhysioNet/CinC challenge 2016 database, which comprises a wide variety of heart sounds recorded from patients with normal and different pathological heart conditions. We conduct a 10-fold stratified cross-validation method to evaluate the performance of different classification algorithms. The feature sets were also tested when using an oversampling method for balancing. Results: The benchmark identified systems showing a satisfying performance in terms of accuracy, sensitivity, and Matthews correlation coefficient. Results can be improved when using feature averaging and an oversampling strategy.

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“…Differences in heart sounds that correspond to different heart disease symptoms are extremely difficult to distinguish. In detail, changes in heart sounds to detect heart diseases are too small so that it is difficult to perform proper diagnosis [22] , [23] , [24] , [25] , [26] .…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

Deperlioğlu

Köse

Gupta

et al. 2020

Computer Communications

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Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

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Design and evaluation of a parametric model for cardiac sounds

Cited by 5 publications

References 31 publications

Comparison of Spectral and Sparse Feature Extraction Methods for Heart Sounds Classification

Comparison of Spectral and Sparse Feature Extraction Methods for Heart Sounds Classification

A benchmark of heart sound classification systems based on sparse decompositions

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

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