A Review of Computer-Aided Heart Sound Detection Techniques

Li, Suyi; Li, Feng; Tang, Shijie; Xiong, Wei

doi:10.1155/2020/5846191

Cited by 66 publications

(64 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computed-aided heart auscultation (CAA) is a system of automated heart sound analysis, which allows to record, visualize, store and analyze phonocardiograms [ 19 , 25 , 26 , 27 , 28 , 29 ]. It is also known as computerized assisted auscultation, and it has several advantages over the auscultation performed by physicians with a classic stethoscope: It helps doctors to make a more accurate and objective diagnosis of the patient’s heart health, since it is likely to outperform the auscultation skills and subjective interpretation of humans [ 30 ]; it facilitates cardiac auscultation, since not only doctors are capable of performing it, but also other health care providers can inspect correctly the patients; it has an important use in telemedicine since a physician that is somewhere in the world can diagnose the patient’s heart health in real time who is somewhere else [ 27 , 31 ]; the analysis results can be stored in a electronic patient record, which can be retrieved for subsequent patient appointments or for teaching and training purposes with medical students [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

Soto-Murillo

Galván-Tejada

et al. 2021

Healthcare

View full text Add to dashboard Cite

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

Soto-Murillo

Galván-Tejada

et al. 2021

Healthcare

View full text Add to dashboard Cite

show abstract

“…Over the past decade, research in sound classification and recognition has gained in popularity and rapidly broaden in its application from the more traditional focus on speech recognition [1] and music genre classification [2] to biometric identification [3], computer-aided heart sound detection [4], environmental audio scene and sound recognition [5,6], biodiversity assessment [7], human voice classification and emotion recognition [8], English accent classification, and gender identification [9], to list a few of a wide range of application areas. As with research in pattern recognition generally, the features fed into classifiers were initially engineered, which, in the case of sound applications, meant extracting from raw audio traces such descriptors as the Statistical Spectrum Descriptor and Rhythm Histogram [10].…”

Section: Introductionmentioning

confidence: 99%

Animal Sound Classification Using Dissimilarity Spaces

et al. 2020

View full text Add to dashboard Cite

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four different backbones with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, experiments process the spectrograms using the heterogeneous auto-similarities of characteristics. Once the similarity spaces are computed, each pattern is “projected” into the space to obtain a vector space representation; this descriptor is then coupled to a support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best standalone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset.

show abstract

“…designed an optimized neural network model for better detection of S1 and S2 heart sounds [41] . Here, Li and colleagues provided a very recent review for heart sound detection/classification solutions from the literature [42] .…”

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

View full text Add to dashboard Cite

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.

show abstract

A Review of Computer-Aided Heart Sound Detection Techniques

Cited by 66 publications

References 52 publications

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

Animal Sound Classification Using Dissimilarity Spaces

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

Contact Info

Product

Resources

About