Detection of heart murmurs based on radial wavelet neural network with Kalman learning

Guillermo, Juan; Ricalde, Luis J.; Sánchez, Edgar N.; Alanis, Alma Y.

doi:10.1016/j.neucom.2014.12.059

Cited by 19 publications

(7 citation statements)

References 27 publications

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“…Similarly, identification of S 2 (SHSD) at the event level, was reported in [25], [29], [45], [47], [52], [57], [64], [87]- [89], [92], [94], [96], [100], and [104], achieving a mean accuracy of 93.96 ± 5.01%; while the mean classification accuracy reported in [90], [106], and [111] was 90.82 ± 6.58%. Pathological heart sounds detection (PHSD) at the event level reported in [29], [64], [65], [67], and [112], achieved mean accuracy of 88.50 ± 5.93%, while pathological heart sounds classification (PHSC) reported in [64], [69], [75], [78], [95], [105], [110], [140], [142], [145], [146], [155], [157], [158], [162]- [164], [167], [170], [183], [185], and [191], achieved mean classification accuracy of 90.28 ± 7.82%. The mean accuracy in the identification of S 1 at the event level was found to be the highest.…”

Section: Synthesis Of Resultsmentioning

confidence: 99%

“…This facilitates identification and extraction of acoustic signals of interest in each cardiac cycle. Broadly, reported segmentation methods can be classified into: envelope based methods [47], [57], [58], [68], [79], [84]- [89], ECG and/or carotid pulse reference based methods [72], [90]- [99], probabilistic models [45], [52], [65], [67], [77], [80], [100]- [105], feature based methods [25], [29], [49], [61], [75], [106]- [108], time-frequency analysis based methods [55], [59], [62], [109], [110], and learning based methods [50], [111]- [113].…”

Section: B Heart Sounds Segmentationmentioning

confidence: 99%

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Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

2019

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Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peerreviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.

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Section: Synthesis Of Resultsmentioning

confidence: 99%

Section: B Heart Sounds Segmentationmentioning

confidence: 99%

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

2019

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“…Guillermo et al [61] proposed a Radial Wavelet Neural Network (RWNN) with Extended Kalman Filter (EKF) model for heart disease classifications. CWT was used to segment PCG signal and identify primary heart sounds, S1 and S2.…”

Section: Pcg Signal Feature Extraction and Classificationmentioning

confidence: 99%

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

Boulares

Alotaibi

Almansour

et al. 2021

IJERPH

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Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

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“…It is better to decide the systole and diastole based on children's heart rate. Second, the commonly used features in previous studies include magnitude [14], frequency spectrum [10,15], normalized energy spectrum [16], power spectral density [17,18], and wavelet coefficients [19]. e differences in these features between heart murmurs and normal heart sounds in both time and frequency domains are used to differentiate heart murmurs from PCG signal.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease

Wang

You

et al. 2020

Journal of Healthcare Engineering

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Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.

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Detection of heart murmurs based on radial wavelet neural network with Kalman learning

Cited by 19 publications

References 27 publications

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease

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