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Ranipa K, Zhu WP, Swamy MNS. A novel feature-level fusion scheme with multimodal attention CNN for heart sound classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108122. [PMID: 38507960 DOI: 10.1016/j.cmpb.2024.108122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/03/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND AND OBJECTIVE Most of the existing machine learning-based heart sound classification methods achieve limited accuracy. Since they primarily depend on single domain feature information and tend to focus equally on each part of the signal rather than employing a selective attention mechanism. In addition, they fail to exploit convolutional neural network (CNN) - based features with an effective fusion strategy. METHODS In order to overcome these limitations, a novel multimodal attention convolutional neural network (MACNN) with a feature-level fusion strategy, in which Mel-cepstral domain as well as general frequency domain features are incorporated to increase the diversity of the features, is proposed in this paper. In the proposed method, DilationAttenNet is first utilized to construct attention-based CNN feature extractors and then these feature extractors are jointly optimized in MACNN at the feature-level. The attention mechanism aims to suppress irrelevant information and focus on crucial diverse features extracted from the CNN. RESULTS Extensive experiments are carried out to study the efficacy of the feature level fusion in comparison to that with early fusion. The results show that the proposed MACNN method significantly outperforms the state-of-the-art approaches in terms of accuracy and score for the two publicly available Github and Physionet datasets. CONCLUSION The findings of our experiments demonstrated the high performance for heart sound classification based on the proposed MACNN, and hence have potential clinical usefulness in the identification of heart diseases. This technique can assist cardiologists and researchers in the design and development of heart sound classification methods.
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Affiliation(s)
- Kalpeshkumar Ranipa
- Department of Electrical and Computer Engineering, Concordia University, Canada.
| | - Wei-Ping Zhu
- Department of Electrical and Computer Engineering, Concordia University, Canada.
| | - M N S Swamy
- Department of Electrical and Computer Engineering, Concordia University, Canada.
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Alkhodari M, Hadjileontiadis LJ, Khandoker AH. Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms. IEEE J Biomed Health Inform 2024; 28:1803-1814. [PMID: 38261492 DOI: 10.1109/jbhi.2024.3357506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low- and middle-income countries. Here, we developed a deep learning-based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23 % and 72.41 %, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10 % when evaluated on unseen data. The model had accuracies of 70 %, 88 %, and 86 % in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment.
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Sinha A, Narula D, Pandey SK, Kumar A, Hassan MM, Jha P, Kumar B, Tiwari MK. CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach. Health Sci Rep 2024; 7:e1802. [PMID: 38192732 PMCID: PMC10772473 DOI: 10.1002/hsr2.1802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/02/2023] [Accepted: 12/17/2023] [Indexed: 01/10/2024] Open
Abstract
Background and Aims Diabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature-fusion strategy employing supervised learning classifiers. Methods Preprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics. Results Based on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes. Conclusion The recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.
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Affiliation(s)
- Anurag Sinha
- Department of Computer Science and Information TechnologyIndira Gandhi National Open UniversityDelhiNew DelhiIndia
| | - Dev Narula
- Department of Computer Science, Amity Institute of Information TechnologyAmity University JharkhandRanchiJharkhandIndia
| | - Saroj Kumar Pandey
- Department of Computer Engineering and ApplicationGLA UniversityMathuraUttar PradeshIndia
| | - Ankit Kumar
- Department of Information TechnologyGuru Ghasidas VishwavidyalayaBilaspurChhattisgarhIndia
| | - Md. Mehedi Hassan
- Computer Science and Engineering DisciplineKhulna UniversityKhulnaBangladesh
| | - Pooja Jha
- Department of Computer Science and Information Technology, Amity Institute of Information TechnologyAmity University JharkhandRanchiJharkhandIndia
| | - Biresh Kumar
- Department of Computer Science and Information Technology, Amity Institute of Information TechnologyAmity University JharkhandRanchiJharkhandIndia
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Netto AN, Abraham L, Philip S. HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram. Technol Health Care 2024; 32:1925-1945. [PMID: 38393859 DOI: 10.3233/thc-231290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
BACKGROUND Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds. OBJECTIVE The study intends to develop a novel blended ensemble model using hybrid deep learning models and softmax regression to classify adult, and paediatric heart sounds into five distinct classes, distinguishing itself as a groundbreaking work in this domain. Furthermore, the research aims to create a comprehensive 5-class paediatric phonocardiogram (PCG) dataset. The dataset includes two critical pathological classes, namely atrial septal defects and ventricular septal defects, along with functional murmurs, pathological and normal heart sounds. METHODS The work proposes a blended ensemble model (HbNet-Heartbeat Network) comprising two hybrid models, CNN-BiLSTM and CNN-LSTM, as base models and Softmax regression as meta-learner. HbNet leverages the strengths of base models and improves the overall PCG classification accuracy. Mel Frequency Cepstral Coefficients (MFCC) capture the crucial audio signal characteristics relevant to the classification. The amalgamation of these two deep learning structures enhances the precision and reliability of PCG classification, leading to improved diagnostic results. RESULTS The HbNet model exhibited excellent results with an average accuracy of 99.72% and sensitivity of 99.3% on an adult dataset, surpassing all the existing state-of-the-art works. The researchers have validated the reliability of the HbNet model by testing it on a real-time paediatric dataset. The paediatric model's accuracy is 86.5%. HbNet detected functional murmur with 100% precision. CONCLUSION The results indicate that the HbNet model exhibits a high level of efficacy in the early detection of cardiac disorders. Results also imply that HbNet has the potential to serve as a valuable tool for the development of decision-support systems that aid medical practitioners in confirming their diagnoses. This method makes it easier for medical professionals to diagnose and initiate prompt treatment while performing preliminary auscultation and reduces unnecessary echocardiograms.
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Affiliation(s)
- Ann Nita Netto
- Department of Electronics and Communication Engineering, LBS Institute of Technology for Women, APJ Abdul Kalam Technological University, Trivandrum, India
| | - Lizy Abraham
- Department of Electronics and Communication Engineering, LBS Institute of Technology for Women, APJ Abdul Kalam Technological University, Trivandrum, India
| | - Saji Philip
- Department of Cardiology, Thiruvalla Medical Mission Hospital, Thiruvalla, India
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Jaros R, Koutny J, Ladrova M, Martinek R. Novel phonocardiography system for heartbeat detection from various locations. Sci Rep 2023; 13:14392. [PMID: 37658080 PMCID: PMC10474097 DOI: 10.1038/s41598-023-41102-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/22/2023] [Indexed: 09/03/2023] Open
Abstract
The paper presents evaluation of the proposed phonocardiography (PCG) measurement system designed primarily for heartbeat detection to estimate heart rate (HR). Typically, HR estimation is performed using electrocardiography (ECG) or pulse wave as one of the fundamental diagnostic methodologies for assessing cardiac function. The system includes novel both sensory part and data processing procedure, which is based on signal preprocessing using Wavelet Transform (WT) and Shannon energy computation and heart sounds classification using K-means. Due to the lack of standardization in the placement of PCG sensors, the study focuses on evaluating the signal quality obtained from 7 different sensor locations on the subject's chest and investigates which locations are most suitable for recording heart sounds. The suitability of sensor localization was examined in 27 subjects by detecting the first two heart sounds (S1, S2). The HR detection sensitivity related to reference ECG from all sensor positions reached values over 88.9 and 77.4% in detection of S1 and S2, respectively. The placement in the middle of sternum showed the higher signal quality with median of the proper S1 and S2 detection sensitivity of 98.5 and 97.5%, respectively.
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Affiliation(s)
- Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia.
| | - Jiri Koutny
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia
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Sheikh AB, Sobotka PA, Garg I, Dunn JP, Minhas AMK, Shandhi MMH, Molinger J, McDonnell BJ, Fudim M. Blood Pressure Variability in Clinical Practice: Past, Present and the Future. J Am Heart Assoc 2023; 12:e029297. [PMID: 37119077 PMCID: PMC10227216 DOI: 10.1161/jaha.122.029297] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Recent advances in wearable technology through convenient and cuffless systems will enable continuous, noninvasive monitoring of blood pressure (BP), heart rate, and heart rhythm on both longitudinal 24-hour measurement scales and high-frequency beat-to-beat BP variability and synchronous heart rate variability and changes in underlying heart rhythm. Clinically, BP variability is classified into 4 main types on the basis of the duration of monitoring time: very-short-term (beat to beat), short-term (within 24 hours), medium-term (within days), and long-term (over months and years). BP variability is a strong risk factor for cardiovascular diseases, chronic kidney disease, cognitive decline, and mental illness. The diagnostic and therapeutic value of measuring and controlling BP variability may offer critical targets in addition to lowering mean BP in hypertensive populations.
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Affiliation(s)
- Abu Baker Sheikh
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Paul A. Sobotka
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
| | - Ishan Garg
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Jessilyn P. Dunn
- Department of Biomedical EngineeringDuke UniversityDurhamNCUSA
- Department of Biostatistics & BioinformaticsDuke UniversityDurhamNCUSA
| | | | | | | | - Barry J. McDonnell
- Department of Biomedical ResearchCardiff Metropolitan UniversitySchool of Sport and Health SciencesCardiffUnited Kingdom
| | - Marat Fudim
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
- Duke Clinical Research InstituteDurhamNCUSA
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An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT). Comput Biol Med 2023; 158:106734. [PMID: 36989745 DOI: 10.1016/j.compbiomed.2023.106734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 01/31/2023] [Accepted: 02/28/2023] [Indexed: 03/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate. METHODS To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms. MAIN RESULTS Among all classifiers, ViT provides the best performance by achieving mean average accuracy Acc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models. CONCLUSIONS The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.
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Automated detection of heart valve disorders with time-frequency and deep features on PCG signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zhang A, Wang J, Qu F, He Z. Classification of Children's Heart Sounds With Noise Reduction Based on Variational Modal Decomposition. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:854382. [PMID: 35693881 PMCID: PMC9178247 DOI: 10.3389/fmedt.2022.854382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/15/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose Children's heart sounds were denoised to improve the performance of the intelligent diagnosis. Methods A combined noise reduction method based on variational modal decomposition (VMD) and wavelet soft threshold algorithm (WST) was proposed, and used to denoise 103 phonocardiogram samples. Features were extracted after denoising and employed for an intelligent diagnosis model to verify the effect of the denoising method. Results The noise in children's phonocardiograms, especially crying noise, was suppressed. The signal-to-noise ratio obtained by the method for normal heart sounds was 14.69 dB at 5 dB Gaussian noise, which was higher than that obtained by WST only and the other VMD denoising method. Intelligent classification showed that the accuracy, sensitivity and specificity of the classification system for congenital heart diseases were 92.23, 92.42, and 91.89%, respectively and better than those with WST only. Conclusion The proposed noise reduction method effectively eliminates noise in children's phonocardiograms and improves the performance of intelligent screening for the children with congenital heart diseases.
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Affiliation(s)
- Anqi Zhang
- Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, China
| | - Jiaming Wang
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Fei Qu
- Shanghai Lishen Information Technology Co., Ltd., Shanghai, China
| | - Zhaoming He
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, United States
- *Correspondence: Zhaoming He
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An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone. Comput Biol Med 2022; 145:105415. [PMID: 35366471 DOI: 10.1016/j.compbiomed.2022.105415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/22/2022] [Accepted: 03/14/2022] [Indexed: 11/27/2022]
Abstract
Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds.
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Transfer Learning Models for Detecting Six Categories of Phonocardiogram Recordings. J Cardiovasc Dev Dis 2022; 9:jcdd9030086. [PMID: 35323634 PMCID: PMC8951694 DOI: 10.3390/jcdd9030086] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 02/01/2023] Open
Abstract
Background and aims: Auscultation is a cheap and fundamental technique for detecting cardiovascular disease effectively. Doctors’ abilities in auscultation are varied. Sometimes, there may be cases of misdiagnosis, even when auscultation is performed by an experienced doctor. Hence, it is necessary to propose accurate computational tools to assist auscultation, especially in developing countries. Artificial intelligence technology can be an efficient diagnostic tool for detecting cardiovascular disease. This work proposed an automatic multiple classification method for cardiovascular disease detection by heart sound signals. Methods and results: In this work, a 1D heart sound signal is translated into its corresponding 3D spectrogram using continuous wavelet transform (CWT). In total, six classes of heart sound data are used in this experiment. We combine an open database (including five classes of heart sound data: aortic stenosis, mitral regurgitation, mitral stenosis, mitral valve prolapse and normal) with one class (pulmonary hypertension) of heart sound data collected by ourselves to perform the experiment. To make the method robust in a noisy environment, the background deformation technique is used before training. Then, 10 transfer learning networks (GoogleNet, SqueezeNet, DarkNet19, MobileNetv2, Inception-ResNetv2, DenseNet201, Inceptionv3, ResNet101, NasNet-Large, and Xception) are used for comparison. Furthermore, other models (LSTM and CNN) are also compared with our proposed algorithm. The experimental results show that four transfer learning networks (ResNet101, DenseNet201, DarkNet19 and GoogleNet) outperformed their peer models with an accuracy of 0.98 to detect the multiple heart diseases. The performances have been validated both in the original heart sound and the augmented heart sound using 10-fold cross validation. The results of these 10 folds are reported in this research. Conclusions: Our method obtained high classification accuracy even under a noisy background, which suggests that the proposed classification method could be used in auxiliary diagnosis for cardiovascular diseases.
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Altaf M, Akram T, Khan MA, Iqbal M, Ch MMI, Hsu CH. A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals. SENSORS 2022; 22:s22052012. [PMID: 35271159 PMCID: PMC8914793 DOI: 10.3390/s22052012] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/13/2021] [Accepted: 12/23/2021] [Indexed: 02/01/2023]
Abstract
In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.
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Affiliation(s)
- Muhammad Altaf
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah 47000, Pakistan; (M.A.); (T.A.); (M.I.)
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah 47000, Pakistan; (M.A.); (T.A.); (M.I.)
| | | | - Muhammad Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah 47000, Pakistan; (M.A.); (T.A.); (M.I.)
| | - M Munawwar Iqbal Ch
- Institute of Information Technology, Quaid-i-Azam University, Islamabad 44000, Pakistan;
| | - Ching-Hsien Hsu
- Department of Computer Science and Information Engineering, Asia University, Taichung 400-439, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 400-439, Taiwan
- Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, School of Mathematics and Big Data, Foshan University, Foshan 528000, China
- Correspondence:
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Burns J, Ganigara M, Dhar A. Application of intelligent phonocardiography in the detection of congenital heart disease in pediatric patients: A narrative review. PROGRESS IN PEDIATRIC CARDIOLOGY 2022. [DOI: 10.1016/j.ppedcard.2021.101455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Nileshwar A, Ahuja V, Kini P. Evaluation of the electronic stethoscope (FONODOC) as a cardiac screening tool during the preoperative evaluation of children. Indian J Anaesth 2022; 66:625-630. [PMID: 36388445 PMCID: PMC9662099 DOI: 10.4103/ija.ija_305_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/17/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Aims: An electronic stethoscope with an inbuilt phonocardiogram is a potentially useful tool for paediatric cardiac evaluation in a resource-limited setting. We aimed to compare the acoustic and electronic stethoscopes with respect to the detection of murmurs as compared to the transthoracic echocardiogram (TTE). Methods: This was an observational study. Fifty children aged 0–12 years with congenital heart diseases (CHDs) and 50 without CHD scheduled for echocardiography were examined using both stethoscopes. The findings were corroborated with clinical findings and compared with the echocardiography report. Results: Among the 50 cases without CHD, no murmur was detected using either of the stethoscopes. This was in agreement with TTE findings. The calculated specificity of both stethoscopes was 100%. Amongst the 50 cases with CHD, the electronic stethoscope picked up murmurs in 32 cases and missed 18 cases. The acoustic stethoscope picked up murmurs in 29 cases and missed 21 cases. Thus, the sensitivity of electronic and acoustic stethoscopes as compared to TTE was calculated to be 64% and 58%, respectively. The positive predictive value of the electronic stethoscope as compared to TTE was 100% while the negative predictive value was 73%. The kappa statistic was 0.93 suggesting agreement in 93%. Mc-Nemar’s test value was 0.24 suggesting that the electronic stethoscope did not offer any advantage over the acoustic stethoscope for the detection of CHD in children. Conclusion: A comparison of the electronic stethoscope with an acoustic stethoscope suggests that the rate of detection of CHD with both stethoscopes is similar and echocardiography remains the gold standard.
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Van den Eynde J, Kutty S, Danford DA, Manlhiot C. Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data. Curr Opin Cardiol 2022; 37:130-136. [PMID: 34857721 DOI: 10.1097/hco.0000000000000927] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) has changed virtually every aspect of modern life, and medicine is no exception. Pediatric cardiology is both a perceptual and a cognitive subspecialty that involves complex decision-making, so AI is a particularly attractive tool for this medical discipline. This review summarizes the foundational work and incremental progress made as AI applications have emerged in pediatric cardiology since 2020. RECENT FINDINGS AI-based algorithms can be useful for pediatric cardiology in many areas, including: (1) clinical examination and diagnosis, (2) image processing, (3) planning and management of cardiac interventions, (4) prognosis and risk stratification, (5) omics and precision medicine, and (6) fetal cardiology. Most AI initiatives showcased in medical journals seem to work well in silico, but progress toward implementation in actual clinical practice has been more limited. Several barriers to implementation are identified, some encountered throughout medicine generally, and others specific to pediatric cardiology. SUMMARY Despite barriers to acceptance in clinical practice, AI is already establishing a durable role in pediatric cardiology. Its potential remains great, but to fully realize its benefits, substantial investment to develop and refine AI for pediatric cardiology applications will be necessary to overcome the challenges of implementation.
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Affiliation(s)
- Jef Van den Eynde
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Cardiovascular Sciences, KU Leuven & Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium
| | - Shelby Kutty
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David A Danford
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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16
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Kafeel A, Aziz S, Awais M, Khan MA, Afaq K, Idris SA, Alshazly H, Mostafa SM. An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis. SENSORS 2021; 21:s21227587. [PMID: 34833662 PMCID: PMC8617882 DOI: 10.3390/s21227587] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 12/03/2022]
Abstract
Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.
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Affiliation(s)
- Ayaz Kafeel
- Eco Pack Ltd. 112, Hattar Industrial State, Haripur 7040, Pakistan;
| | - Sumair Aziz
- Department of Electronics Engineering, University of Engineering and Technology, Taxila 47040, Pakistan;
| | - Muhammad Awais
- Department of Electrical and Computer Engineering, COMSATS University Islamabad Wah Campus, Wah 47080, Pakistan;
| | | | - Kamran Afaq
- Department of Mechanical Engineering, HITEC University Taxila, Taxila 47040, Pakistan;
| | - Sahar Ahmed Idris
- College of Industrial Engineering, King Khalid University, Abha 61421, Saudi Arabia;
| | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena 83523, Egypt;
| | - Samih M. Mostafa
- Faculty of Computers and Information, South Valley University, Qena 83523, Egypt;
- Correspondence:
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17
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Helman SM, Herrup EA, Christopher AB, Al-Zaiti SS. The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. Cardiol Young 2021; 31:1770-1780. [PMID: 34725005 PMCID: PMC8805679 DOI: 10.1017/s1047951121004212] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.
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Affiliation(s)
- Stephanie M Helman
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth A Herrup
- Division of Pediatric Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Adam B Christopher
- Division of Pediatric Cardiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Salah S Al-Zaiti
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
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18
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Iqtidar K, Qamar U, Aziz S, Khan MU. Phonocardiogram signal analysis for classification of Coronary Artery Diseases using MFCC and 1D adaptive local ternary patterns. Comput Biol Med 2021; 138:104926. [PMID: 34656868 DOI: 10.1016/j.compbiomed.2021.104926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 09/15/2021] [Accepted: 10/01/2021] [Indexed: 11/30/2022]
Abstract
Coronary Artery Diseases (CADs) are a dominant cause of worldwide fatalities. The development of accurate and timely diagnosis routines is imperative to reduce these risks and mortalities. Coronary angiography, an invasive and expensive technique, is currently used as a diagnostic tool for the detection of CAD but it has some procedural hazards, i.e., it requires arterial puncture, and the subject gets exposed to iodinated radiation. Phonocardiography (PCG), a non-invasive and inexpensive technique, is a modality employing heart sounds to diagnose heart diseases but it requires only trained medical personnel to apprehend cardiac murmurs in clinical environments. Furthermore, there is a strong compulsion to characterize CAD into its types, such as Single vessel coronary artery disease (SVCAD), Double vessel coronary artery disease (DVCAD), and Triple vessel coronary artery disease (TVCAD) to assist the cardiologist in decision making about the treatment procedure followed. This paper presents a computer-aided diagnosis system for the categorization of CAD and its types based on Phonocardiogram (PCG) signal analysis. The raw PCG signals were denoised via empirical mode decomposition (EMD) to remove redundant information and noise. Next, we extract MFCC and proposed 1D-Adaptive Local Ternary Patterns (1D-ALTP) and fused them serially to get a strong feature representation of multiple PCG signal classes. Features were further reduced through Multidimensional Scaling (MDS) and subjected to several classification methods such as support vector machines (SVM), Decision Tree (DT), and K-nearest neighbors (KNN) in a comparative fashion. The best classification performances of 98.3% and 97.2% mean accuracies were obtained through SVM with the cubic kernel for binary and multiclass experiments, respectively. The performance of the proposed system is comprehensively tested through 10-fold cross-validation and hold-out train-test techniques to avoid model overfitting. Comparative analysis with existing approaches advocates the superiority of the proposed approach.
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Affiliation(s)
- Khushbakht Iqtidar
- Knowledge and Data Science Research Centre, Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
| | - Usman Qamar
- Knowledge and Data Science Research Centre, Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Sumair Aziz
- Department of Electronics Engineering, University of Engineering and Technology, Taxila, Pakistan
| | - Muhammad Umar Khan
- Department of Electronics Engineering, University of Engineering and Technology, Taxila, Pakistan
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19
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Tuncer T, Dogan S, Tan RS, Acharya UR. Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.088] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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20
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Riaz U, Aziz S, Umar Khan M, Zaidi SAA, Ukasha M, Rashid A. A novel embedded system design for the detection and classification of cardiac disorders. Comput Intell 2021. [DOI: 10.1111/coin.12469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Umair Riaz
- Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan
| | - Sumair Aziz
- Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan
| | - Muhammad Umar Khan
- Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan
| | - Syed Azhar Ali Zaidi
- Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan
| | - Muhammad Ukasha
- Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan
| | - Aamir Rashid
- Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan
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21
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Khan MU, Aziz S, Akram T, Amjad F, Iqtidar K, Nam Y, Khan MA. Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme. SENSORS 2021; 21:s21010247. [PMID: 33401652 PMCID: PMC7794944 DOI: 10.3390/s21010247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/27/2022]
Abstract
Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.
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Affiliation(s)
- Muhammad Umar Khan
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
| | - Sumair Aziz
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
- Correspondence: (S.A.); (Y.N.)
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment, Islamabad 45550, Pakistan;
| | - Fatima Amjad
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
| | - Khushbakht Iqtidar
- Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Yunyoung Nam
- Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
- Correspondence: (S.A.); (Y.N.)
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22
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de Campos Souza PV, Lughofer E. Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6477. [PMID: 33198426 PMCID: PMC7698187 DOI: 10.3390/s20226477] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 11/16/2022]
Abstract
Heart problems are responsible for the majority of deaths worldwide. The use of intelligent techniques to assist in the identification of existing patterns in these diseases can facilitate treatments and decision making in the field of medicine. This work aims to extract knowledge from a dataset based on heart noise behaviors in order to determine whether heart murmur predilection exists or not in the analyzed patients. A heart murmur can be pathological due to defects in the heart, so the use of an evolving hybrid technique can assist in detecting this comorbidity team, and at the same time, extract knowledge through fuzzy linguistic rules, facilitating the understanding of the nature of the evaluated data. Heart disease detection tests were performed to compare the proposed hybrid model's performance with state of the art for the subject. The results obtained (90.75% accuracy) prove that in addition to great assertiveness in detecting heart murmurs, the evolving hybrid model could be concomitant with the extraction of knowledge from data submitted to an intelligent approach.
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