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Manshadi OD, Mihandoost S. Murmur identification and outcome prediction in phonocardiograms using deep features based on Stockwell transform. Sci Rep 2024; 14:7592. [PMID: 38555390 PMCID: PMC10981708 DOI: 10.1038/s41598-024-58274-6] [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] [Received: 12/17/2023] [Accepted: 03/27/2024] [Indexed: 04/02/2024] Open
Abstract
Traditionally, heart murmurs are diagnosed through cardiac auscultation, which requires specialized training and experience. The purpose of this study is to predict patients' clinical outcomes (normal or abnormal) and identify the presence or absence of heart murmurs using phonocardiograms (PCGs) obtained at different auscultation points. A semi-supervised model tailored to PCG classification is introduced in this study, with the goal of improving performance using time-frequency deep features. The study begins by investigating the behavior of PCGs in the time-frequency domain, utilizing the Stockwell transform to convert the PCG signal into two-dimensional time-frequency maps (TFMs). A deep network named AlexNet is then used to derive deep feature sets from these TFMs. In feature reduction, redundancy is eliminated and the number of deep features is reduced to streamline the feature set. The effectiveness of the extracted features is evaluated using three different classifiers using the CinC/Physionet challenge 2022 dataset. For Task I, which focuses on heart murmur detection, the proposed approach achieved an average accuracy of 93%, sensitivity of 91%, and F1-score of 91%. According to Task II of the CinC/Physionet challenge 2022, the approach showed a clinical outcome cost of 5290, exceeding the benchmark set by leading methods in the challenge.
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Affiliation(s)
| | - Sara Mihandoost
- Department of Electrical Engineering, Urmia University of Technology, Urmia, Iran.
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2
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Hasan M, Sahid MA, Uddin MP, Marjan MA, Kadry S, Kim J. Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets. PeerJ Comput Sci 2024; 10:e1917. [PMID: 38660196 PMCID: PMC11041935 DOI: 10.7717/peerj-cs.1917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/12/2024] [Indexed: 04/26/2024]
Abstract
Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.
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Affiliation(s)
- Mahmudul Hasan
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Md Abdus Sahid
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Md Abu Marjan
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Seifedine Kadry
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, Norway
- MEU Research Unit, Middle East University, Amman, Jordan
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan, Republic of South Korea
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Shastri RK, Shastri AR, Nitnaware PP, Padulkar DM. Hybrid Sneaky algorithm-based deep neural networks for Heart sound classification using phonocardiogram. NETWORK (BRISTOL, ENGLAND) 2024; 35:1-26. [PMID: 38018148 DOI: 10.1080/0954898x.2023.2270040] [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: 04/12/2023] [Accepted: 10/09/2023] [Indexed: 11/30/2023]
Abstract
In the diagnosis of cardiac disorders Heart sound has a major role, and early detection is crucial to safeguard the patients. Computerized strategies of heart sound classification advocate intensive and more exact results in a quick and better manner. Using a hybrid optimization-controlled deep learning strategy this paper proposed an automatic heart sound classification module. The parameter tuning of the Deep Neural Network (DNN) classifier in a satisfactory manner is the importance of this research which depends on the Hybrid Sneaky optimization algorithm. The developed sneaky optimization algorithm inherits the traits of questing and societal search agents. Moreover, input data from the Phonocardiogram (PCG) database undergoes the process of feature extraction which extract the important features, like statistical, Heart Rate Variability (HRV), and to enhance the performance of this model, the features of Mel frequency Cepstral coefficients (MFCC) are assisted. The developed Sneaky optimization-based DNN classifier's performance is determined in respect of the metrics, namely precision, accuracy, specificity, and sensitivity, which are around 97%, 96.98%, 97%, and 96.9%, respectively.
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Affiliation(s)
- Rajveer K Shastri
- Electronics and Telecommunication, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Maharashtra, India
| | - Aparna R Shastri
- Electronics and Telecommunication, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Maharashtra, India
| | - Prashant P Nitnaware
- Computer Engineering, Pillai College of Engineering, Mumbai, India
- Computer Engineering, Pillai College of Engineering (PCE), Navi Mumbai, Maharashtra, India
| | - Digambar M Padulkar
- Computer Engineering, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology, Baramati, Maharashtra, India
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Tsai YT, Liu YH, Zheng ZW, Chen CC, Lin MC. Heart Murmur Classification Using a Capsule Neural Network. Bioengineering (Basel) 2023; 10:1237. [PMID: 38002361 PMCID: PMC10669720 DOI: 10.3390/bioengineering10111237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset.
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Affiliation(s)
- Yu-Ting Tsai
- Master’s Program in Electro-Acoustics, Feng Chia University, Taichung 40724, Taiwan
- Hyper-Automation Laboratory, Feng Chia University, Taichung 40724, Taiwan
| | - Yu-Hsuan Liu
- Master’s Program in Electro-Acoustics, Feng Chia University, Taichung 40724, Taiwan
| | - Zi-Wei Zheng
- Hyper-Automation Laboratory, Feng Chia University, Taichung 40724, Taiwan
- Program of Mechanical and Aeronautical Engineering, Feng Chia University, Taichung 40724, Taiwan
| | - Chih-Cheng Chen
- Hyper-Automation Laboratory, Feng Chia University, Taichung 40724, Taiwan
- Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
| | - Ming-Chih Lin
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402, Taiwan
- Children’s Medical Center, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Food and Nutrition, Providence University, Taichung 433, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
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Prince J, Maidens J, Kieu S, Currie C, Barbosa D, Hitchcock C, Saltman A, Norozi K, Wiesner P, Slamon N, Del Grippo E, Padmanabhan D, Subramanian A, Manjunath C, Chorba J, Venkatraman S. Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease. J Am Heart Assoc 2023; 12:e030377. [PMID: 37830333 PMCID: PMC10757522 DOI: 10.1161/jaha.123.030377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/11/2023] [Indexed: 10/14/2023]
Abstract
Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Kambiz Norozi
- Department of Pediatrics, Pediatric CardiologyWestern UniversityLondonONCanada
- Department of Pediatric Cardiology and Intensive Care MedicineHannover Medical SchoolHannoverGermany
- Children Health Research InstituteLondonONCanada
| | | | | | | | - Deepak Padmanabhan
- Sri Jayadeva Institute of Cardiovascular Sciences and ResearchBengaluruIndia
| | - Anand Subramanian
- Sri Jayadeva Institute of Cardiovascular Sciences and ResearchBengaluruIndia
| | | | - John Chorba
- Division of Cardiology, Zuckerberg San Francisco General Hospital, Department of MedicineUniversity of California San FranciscoSan FranciscoCAUSA
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Cheng J, Sun K. Heart Sound Classification Network Based on Convolution and Transformer. SENSORS (BASEL, SWITZERLAND) 2023; 23:8168. [PMID: 37836998 PMCID: PMC10575162 DOI: 10.3390/s23198168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 08/31/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023]
Abstract
Electronic auscultation is vital for doctors to detect symptoms and signs of cardiovascular diseases (CVDs), significantly impacting human health. Although progress has been made in heart sound classification, most existing methods require precise segmentation and feature extraction of heart sound signals before classification. To address this, we introduce an innovative approach for heart sound classification. Our method, named Convolution and Transformer Encoder Neural Network (CTENN), simplifies preprocessing, automatically extracting features using a combination of a one-dimensional convolution (1D-Conv) module and a Transformer encoder. Experimental results showcase the superiority of our proposed method in both binary and multi-class tasks, achieving remarkable accuracies of 96.4%, 99.7%, and 95.7% across three distinct datasets compared with that of similar approaches. This advancement holds promise for enhancing CVD diagnosis and treatment.
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Affiliation(s)
- Jiawen Cheng
- College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
| | - Kexue Sun
- College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
- Nation-Local Joint Project Engineering Laboratory of RF Integration & Micropackage, Nanjing 210023, China
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Torre-Cruz J, Canadas-Quesada F, Ruiz-Reyes N, Vera-Candeas P, Garcia-Galan S, Carabias-Orti J, Ranilla J. Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals. J Biomed Inform 2023; 145:104475. [PMID: 37595770 DOI: 10.1016/j.jbi.2023.104475] [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] [Received: 03/24/2023] [Revised: 07/25/2023] [Accepted: 08/11/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND AND OBJECTIVE Valvular heart disease (VHD) is associated with elevated mortality rates. Although transthoracic echocardiography (TTE) is the gold standard detection tool, phonocardiography (PCG) could be an alternative as it is a cost-effective and noninvasive method for cardiac auscultation. Many researchers have dedicated their efforts to improving the decision-making process and developing robust and precise approaches to assist physicians in providing reliable diagnoses of VHD. METHODS This research proposes a novel approach for the detection of anomalous valvular heart sounds from PCG signals. The proposed approach combines orthogonal non-negative matrix factorization (ONMF) and convolutional neural network (CNN) architectures in a three-stage cascade. The aim of the proposal is to improve the learning process by identifying the optimal ONMF temporal or spectral patterns for accurate detection. In the first stage, the time-frequency representation of the input PCG signal is computed. Next, band-pass filtering is performed to locate the spectral range that is most relevant for the presence of such cardiac abnormalities. In the second stage, the temporal and spectral cardiac structures are extracted using the ONMF approach. These structures are utilized in the third stage and fed into the CNN architecture to detect abnormal heart sounds. RESULTS Several state-of-the-art CNN architectures, such as LeNet5, AlexNet, ResNet50, VGG16 and GoogLeNet, have been evaluated to determine the effectiveness of using ONMF temporal features for VHD detection. The results reveal that the integration of ONMF temporal features with a CNN classifier significantly improve VHD detection. Specifically, the proposed approach achieves an accuracy improvement of approximately 45% when ONMF spectral features are used and 35% when time-frequency features from the short-time Fourier transform (STFT) spectrogram are used. Additionally, feeding ONMF temporal features into low-complexity CNN architectures yields competitive results comparable to those obtained with complex architectures. CONCLUSIONS The temporal structure factorized by ONMF plays a critical role in distinguishing between normal heart sounds and abnormal heart sounds since the repeatability of normal heart cycles is disrupted by the presence of cardiac abnormalities. Consequently, the results highlight the importance of appropriate input data representation in the learning process of CNN models in the biomedical field of valvular heart sound detection.
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Affiliation(s)
- J Torre-Cruz
- Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain.
| | - F Canadas-Quesada
- Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain
| | - N Ruiz-Reyes
- Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain
| | - P Vera-Candeas
- Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain
| | - S Garcia-Galan
- Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain
| | - J Carabias-Orti
- Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain
| | - J Ranilla
- Department of Computer Science, University of Oviedo, Campus de Gijón s/n, Gijon (Asturias), 33203, Spain
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Elola A, Aramendi E, Oliveira J, Renna F, Coimbra MT, Reyna MA, Sameni R, Clifford GD, Rad AB. Beyond Heart Murmur Detection: Automatic Murmur Grading From Phonocardiogram. IEEE J Biomed Health Inform 2023; 27:3856-3866. [PMID: 37163396 PMCID: PMC10482086 DOI: 10.1109/jbhi.2023.3275039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
OBJECTIVE Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area. METHODS The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients. RESULTS The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%. CONCLUSIONS This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs. SIGNIFICANCE The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity, which may provide an enhanced classification of clinical outcomes.
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Nakalega R, Mukiza N, Menge R, Kizito S, Babirye JA, Kuteesa CN, Mawanda D, Mulumba E, Nabukeera J, Ggita J, Nakanjako L, Akello C, Mirembe BG, Lukyamuzi Z, Nakaye C, Kataike H, Maena J, Etima J, Nabunya HK, Biira F, Nagawa C, Heffron R, Celum C, Gandhi M, Mujugira A. Feasibility and acceptability of peer-delivered HIV self-testing and PrEP for young women in Kampala, Uganda. BMC Public Health 2023; 23:1163. [PMID: 37322510 PMCID: PMC10273744 DOI: 10.1186/s12889-023-16081-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/08/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Adolescent girls and young women (AGYW) account for 29% of new HIV infections in Uganda despite representing just 10% of the population. Peer support improves AGYW linkage to HIV care and medication adherence. We evaluated the feasibility and acceptability of peer delivered HIV self-tests (HIVST) and oral pre-exposure prophylaxis (PrEP) to young women in Uganda. METHODS Between March and September 2021, we conducted a pilot study of 30 randomly selected young women, aged 18-24 years, who had received oral PrEP for at least three months, but had suboptimal adherence as measured by urine tenofovir testing (< 1500 ng/ml). Participants were offered daily oral PrEP and attended clinic visits three and six months after enrollment. Between clinic visits, participants were visited monthly by trained peers who delivered HIVST and PrEP. Feasibility and acceptability of peer-delivered PrEP and HIVST (intervention) were measured by comparing actual versus planned intervention delivery and product use. We conducted two focus groups with young women, and five in-depth interviews with peers and health workers to explore their experiences with intervention delivery. Qualitative data were analyzed using thematic analysis. RESULTS At baseline, all 30 enrolled young women (median age 20 years) accepted peer-delivered PrEP and HIVST. Peer delivery visit completion was 97% (29/30) and 93% (28/30) at three and six months, respectively. The proportion of participants with detectable tenofovir in urine was 93% (27/29) and 57% (16/28) at months three and six, respectively. Four broad themes emerged from the qualitative data: (1) Positive experiences of peer delivered HIVST and PrEP; (2) The motivating effect of peer support; (3) Perceptions of female controlled HIVST and PrEP; and (4) Multi-level barriers to HIVST and PrEP use. Overall, peer delivery motivated young women to use HIVST and PrEP and encouraged persistence on PrEP by providing non-judgmental client-friendly services and adherence support. CONCLUSION Peer delivery of HIVST and oral PrEP was feasible and acceptable to this sample of young women with suboptimal PrEP adherence in Uganda. Future larger controlled studies should evaluate its effectiveness among African AGWY.
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Affiliation(s)
- Rita Nakalega
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda.
| | | | | | - Samuel Kizito
- Brown School at Washington University, Saint Louis, MO, USA
| | - Juliet Allen Babirye
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | | | | | - Emmie Mulumba
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Josephine Nabukeera
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Joseph Ggita
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | | | - Carolyne Akello
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Brenda Gati Mirembe
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Zubair Lukyamuzi
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Catherine Nakaye
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Hajira Kataike
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Joel Maena
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Juliane Etima
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Hadijah Kalule Nabunya
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Florence Biira
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | - Christine Nagawa
- Makerere University-Johns Hopkins University (MU-JHU) Research Collaboration, Kampala, Uganda
| | | | | | - Monica Gandhi
- University of California San Francisco, San Francisco, California, USA
| | - Andrew Mujugira
- University of Washington, Seattle, WA, USA
- Infectious Diseases Institute, Makerere University, Kampala, Uganda
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Yin Q, Huang X, Yang Q, Lin S, Song Q, Fan W, Li W, Li Z, Gao L. LncRNA model predicts liver cancer drug resistance and validate in vitro experiments. Front Cell Dev Biol 2023; 11:1174183. [PMID: 37077416 PMCID: PMC10106610 DOI: 10.3389/fcell.2023.1174183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023] Open
Abstract
Introduction: Hepatocellular carcinoma (HCC) patients may benefit from chemotherapy, but drug resistance is an important obstacle to favorable prognoses. Overcoming drug resistance is an urgent problem to be solved.Methods: Differential expression analysis was used to identify long non-coding RNAs (LncRNAs) that differed in chemotherapy-sensitive and chemotherapy-resistant patients. Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. A back propagation (BP) network was then used to validate the predictive capacity of important LncRNAs. The molecular functions of hub LncRNAs were investigated via qRT-PCR and cell proliferation assay. Molecular-docking technique was used to explore candidate drug of targets of hub LncRNA in the model.Results: A total of 125 differentially expressed LncRNAs between sensitive and resistant patients. Seventeen important LncRNAs were identified via RF, and seven factors were identified via LR. With respect to SVM, the top 15 LncRNAs of AvgRank were selected. Five merge chemotherapy-related LncRNAs were used to predict chemotherapy resistance with high accuracy. CAHM was a hub LncRNA of model and expression high in sorafenib resistance cell lines. In addition, the results of CCK8 showed that the sensitivity of HepG2-sorafenib cells to sorafenib was significantly lower than that of HepG2; and the sensitivity of HepG2-sorafenib cells transfected with sh-CAHM was significantly higher than that of Sorafenib. In the non-transfection group, the results of clone formation experiments showed that the number of clones formed by HepG2-sorafenib cells treated with sorafenib was significantly more than that of HepG2; after HepG2-sorafenib cells were transfected with sh-CAHM, the number of clones formed by Sorafenib treatment was significantly higher than that of HepG2 cells. The number was significantly less than that of HepG2-s + sh-NC group. Molecular Docking results indicate that Moschus was candidate drug for target protein of CAHM.Conclusion: Five chemotherapy-related LncRNAs could predict drug resistance in HCC with high accuracy, and the hub LncRNA CAHM has potential as a new biomarker for HCC chemotherapy resistance.
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Affiliation(s)
- Qiushi Yin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Xiaolong Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Qiuxi Yang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Shibu Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Qifeng Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Weiqiang Fan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Wang Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
| | - Zhongyi Li
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Lianghui Gao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan Province, China
- *Correspondence: Lianghui Gao,
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11
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Diagnosis of cardiovascular disease using deep learning technique. Soft comput 2022. [DOI: 10.1007/s00500-022-07788-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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12
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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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13
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Rath A, Mishra D, Panda G, Pal M. Development and assessment of machine learning based heart disease detection using imbalanced heart sound signal. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103730] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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14
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Torre-Cruz J, Martinez-Muñoz D, Ruiz-Reyes N, Muñoz-Montoro AJ, Puentes-Chiachio M, Canadas-Quesada FJ. Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106909. [PMID: 35649297 DOI: 10.1016/j.cmpb.2022.106909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/28/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Auscultation is the first technique applied to the early diagnose of any cardiovascular disease (CVD) in rural areas and poor-resources countries because of its low cost and non-invasiveness. However, it highly depends on the physician's expertise to recognize specific heart sounds heard through the stethoscope. The analysis of phonocardiogram (PCG) signals attempts to segment each cardiac cycle into the four cardiac states (S1, systole, S2 and diastole) in order to develop automatic systems applied to an efficient and reliable detection and classification of heartbeats. In this work, we propose an unsupervised approach, based on time-frequency characteristics shown by cardiac sounds, to detect and classify heartbeats S1 and S2. METHODS The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1 and S2. The first contribution is a novel approach that combines the dissimilarity matrix with the frame-level spectral divergence to locate heartbeats using the repetitiveness shown by the heart sounds and the temporal relationships between the intervals defined by the events S1/S2 and non-S1/S2 (systole and diastole). The second contribution is a verification-correction-classification process based on a sliding window that allows the preservation of the temporal structure of the cardiac cycle in order to be applied in the heart sound classification. The proposed method has been assessed using the open access databases PASCAL, CirCor DigiScope Phonocardiogram and an additional sound mixing procedure considering both Additive White Gaussian Noise (AWGN) and different kinds of clinical ambient noises from a commercial database. RESULTS The proposed method outperforms the detection and classification performance of other recent state-of-the-art methods. Although our proposal achieves the best average accuracy for PCG signals without cardiac abnormalities, 99.4% in heartbeat detection and 97.2% in heartbeat classification, its worst average accuracy is always above 92% for PCG signals with cardiac abnormalities, signifying an improvement in heartbeat detection/classification above 10% compared to the other state-of-the-art methods evaluated. CONCLUSIONS The proposed method provides the best detection/classification performance in realistic scenarios where the presence of cardiac anomalies as well as different types of clinical environmental noises are active in the PCG signal. Of note, the promising modelling of the temporal structures of the heart provided by the dissimilarity matrix together with the frame-level spectral divergence, as well as the removal of a significant number of spurious heart events and recovery of missing heart events, both corrected by the proposed verification-correction-classification algorithm, suggest that our proposal is a successful tool to be applied in heart segmentation.
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Affiliation(s)
- J Torre-Cruz
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares 23700, Jaen, Spain.
| | - D Martinez-Muñoz
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares 23700, Jaen, Spain
| | - N Ruiz-Reyes
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares 23700, Jaen, Spain
| | - A J Muñoz-Montoro
- Department of Computer Science, University of Oviedo, Campus de Gijón, s/n, Gijón 33203, Spain
| | - M Puentes-Chiachio
- Cardiology, University Hospital of Jaen, Av. del Ejercito Espanol, 10, 23007 Jaen, Spain
| | - F J Canadas-Quesada
- Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares 23700, Jaen, Spain
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15
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Dargam V, Ng HH, Nasim S, Chaparro D, Irion CI, Seshadri SR, Barreto A, Danziger ZC, Shehadeh LA, Hutcheson JD. S2 Heart Sound Detects Aortic Valve Calcification Independent of Hemodynamic Changes in Mice. Front Cardiovasc Med 2022; 9:809301. [PMID: 35694672 PMCID: PMC9174427 DOI: 10.3389/fcvm.2022.809301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/18/2022] [Indexed: 11/16/2022] Open
Abstract
Background Calcific aortic valve disease (CAVD) is often undiagnosed in asymptomatic patients, especially in underserved populations. Although artificial intelligence has improved murmur detection in auscultation exams, murmur manifestation depends on hemodynamic factors that can be independent of aortic valve (AoV) calcium load and function. The aim of this study was to determine if the presence of AoV calcification directly influences the S2 heart sound. Methods Adult C57BL/6J mice were assigned to the following 12-week-long diets: (1) Control group (n = 11) fed a normal chow, (2) Adenine group (n = 4) fed an adenine-supplemented diet to induce chronic kidney disease (CKD), and (3) Adenine + HP (n = 9) group fed the CKD diet for 6 weeks, then supplemented with high phosphate (HP) for another 6 weeks to induce AoV calcification. Phonocardiograms, echocardiogram-based valvular function, and AoV calcification were assessed at endpoint. Results Mice on the Adenine + HP diet had detectable AoV calcification (9.28 ± 0.74% by volume). After segmentation and dimensionality reduction, S2 sounds were labeled based on the presence of disease: Healthy, CKD, or CKD + CAVD. The dataset (2,516 S2 sounds) was split subject-wise, and an ensemble learning-based algorithm was developed to classify S2 sound features. For external validation, the areas under the receiver operating characteristic curve of the algorithm to classify mice were 0.9940 for Healthy, 0.9717 for CKD, and 0.9593 for CKD + CAVD. The algorithm had a low misclassification performance of testing set S2 sounds (1.27% false positive, 1.99% false negative). Conclusion Our ensemble learning-based algorithm demonstrated the feasibility of using the S2 sound to detect the presence of AoV calcification. The S2 sound can be used as a marker to identify AoV calcification independent of hemodynamic changes observed in echocardiography.
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Affiliation(s)
- Valentina Dargam
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Hooi Hooi Ng
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
- Department of Human and Molecular Genetics, Florida International University, Miami, FL, United States
| | - Sana Nasim
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Daniel Chaparro
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Camila Iansen Irion
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Coral Gables, FL, United States
| | - Suhas Rathna Seshadri
- Department of Medical Education, University of Miami Miller School of Medicine, Coral Gables, FL, United States
| | - Armando Barreto
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, United States
| | - Zachary C. Danziger
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Lina A. Shehadeh
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Coral Gables, FL, United States
- Division of Cardiology, Department of Medicine, University of Miami Miller School of Medicine, Coral Gables, FL, United States
| | - Joshua D. Hutcheson
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States
- Biomolecular Sciences Institute, Florida International University, Miami, FL, United States
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16
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Embedded platform based heart murmur classification using deep learning approach. Int J Health Sci (Qassim) 2022. [DOI: 10.53730/ijhs.v6ns2.6082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ubiquitous Perturbations in cardiac auscultation properties, cardiovascular diseases (CVDs) are widely recognized. In the auscultation procedure, the appearance of pathological cardiac murmurs is linked to heart disorders. A noble automated detection system using 1-D Convolutional Neural Network (CNN) for the detection of pathological heart murmurs is proposed in this study, which removes the difficult task of extracting and selecting features. It directly acts on the phonocardiogram (PCG) signals. The fundamental purpose of this research is to develop a classification model for consistent recognition of cardiac murmurs when the data-set is imbalanced. In view of this, the proposed study for the imbalanced data-set incorporates the Adaptive Synthetic (ADASYN) approach to generate synthetic data for the minority class. The outcome analysis illustrates the positive result in the identification of heart murmurs on both balanced and imbalanced data-sets. Therefore, the developed deep learning model will learn better from the minority class and classify heart murmurs accurately.
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17
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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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18
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Megalmani DR, G SB, Rao M V A, Jeevannavar SS, Ghosh PK. Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:713-717. [PMID: 34891391 DOI: 10.1109/embc46164.2021.9629596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cardiac Auscultation, an integral part of the physical examination of a patient, is essential for early diagnosis of cardiovascular diseases (CVDs). The ability to accurately diagnose the heart sounds requires experience and expertise, which is lacking in doctors in the early years of clinical practice. Thus, there is a need for an automatic diagnostic tool that would aid medical practitioners with their diagnosis. We propose novel hybrid architectures for classification of unsegmented heart sounds to normal and abnormal classes. We propose two methods, with and without the conventional feature extraction step in the classification pipeline. We demonstrate that the F score using the approach with conventional feature extraction is 1.25 (absolute) more than using a baseline implementation on the Physionet dataset. We also introduce a mechanism to tag predictions as unsure and compare results with a varying threshold.
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19
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Automatic Assessment of Mitral Regurgitation Severity Using the Mask R-CNN Algorithm with Color Doppler Echocardiography Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:2602688. [PMID: 34552659 PMCID: PMC8452404 DOI: 10.1155/2021/2602688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/18/2021] [Indexed: 11/17/2022]
Abstract
Accurate assessment of mitral regurgitation (MR) severity is critical in clinical diagnosis and treatment. No single echocardiographic method has been recommended for MR quantification thus far. We sought to define the feasibility and accuracy of the mask regions with a convolutional neural network (Mask R-CNN) algorithm in the automatic qualitative evaluation of MR using color Doppler echocardiography images. The authors collected 1132 cases of MR from hospital A and 295 cases of MR from hospital B and divided them into the following four types according to the 2017 American Society of Echocardiography (ASE) guidelines: grade I (mild), grade II (moderate), grade III (moderate), and grade IV (severe). Both grade II and grade III are moderate. After image marking with the LabelMe software, a method using the Mask R-CNN algorithm based on deep learning (DL) was used to evaluate MR severity. We used the data from hospital A to build the artificial intelligence (AI) model and conduct internal verification, and we used the data from hospital B for external verification. According to severity, the accuracy of classification was 0.90, 0.89, and 0.91 for mild, moderate, and severe MR, respectively. The Macro F1 and Micro F1 coefficients were 0.91 and 0.92, respectively. According to grading, the accuracy of classification was 0.90, 0.87, 0.81, and 0.91 for grade I, grade II, grade III, and grade IV, respectively. The Macro F1 and Micro F1 coefficients were 0.89 and 0.89, respectively. Automatic assessment of MR severity is feasible with the Mask R-CNN algorithm and color Doppler electrocardiography images collected in accordance with the 2017 ASE guidelines, and the model demonstrates reasonable performance and provides reliable qualitative results for MR severity.
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20
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Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey. BIOSENSORS-BASEL 2021; 11:bios11070228. [PMID: 34356699 PMCID: PMC8301976 DOI: 10.3390/bios11070228] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/02/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Smartwatches have the potential to support health care in everyday life by supporting self-monitoring of health conditions and personal activities. This paper aims to develop a model that predicts the prevalence of cardiovascular disease using health-related data that can be easily measured by smartwatch users. To this end, the data corresponding to the health-related data variables provided by the smartwatch are selected from the Korea National Health and Nutrition Examination Survey. To classify the prevalence of cardiovascular disease with these selected variables, we apply logistic regression, artificial neural network, and support vector machine among machine learning classification techniques, and compare the appropriateness of the algorithm through classification performance indicators. The prediction model using support vector machine showed the highest accuracy. Next, we analyze which structures or parameters of the support vector machine contribute to increasing accuracy and derive the importance of input variables. Since it is very important to diagnose cardiovascular disease early correctly, we expect that this model will be very useful if there is a tool to predict whether cardiovascular disease develops or not.
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21
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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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22
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Chen Y, Sun Y, Lv J, Jia B, Huang X. End-to-end heart sound segmentation using deep convolutional recurrent network. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00325-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
AbstractHeart sound segmentation (HSS) aims to detect the four stages (first sound, systole, second heart sound and diastole) from a heart cycle in a phonocardiogram (PCG), which is an essential step in automatic auscultation analysis. Traditional HSS methods need to manually extract the features before dealing with HSS tasks. These artificial features highly rely on extraction algorithms, which often result in poor performance due to the different operating environments. In addition, the high-dimension and frequency characteristics of audio also challenge the traditional methods in effectively addressing HSS tasks. This paper presents a novel end-to-end method based on convolutional long short-term memory (CLSTM), which directly uses audio recording as input to address HSS tasks. Particularly, the convolutional layers are designed to extract the meaningful features and perform the downsampling, and the LSTM layers are developed to conduct the sequence recognition. Both components collectively improve the robustness and adaptability in processing the HSS tasks. Furthermore, the proposed CLSTM algorithm is easily extended to other complex heart sound annotation tasks, as it does not need to extract the characteristics of corresponding tasks in advance. In addition, the proposed algorithm can also be regarded as a powerful feature extraction tool, which can be integrated into the existing models for HSS. Experimental results on real-world PCG datasets, through comparisons to peer competitors, demonstrate the outstanding performance of the proposed algorithm.
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23
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Artificial intelligence in critical care: Its about time! Med J Armed Forces India 2021; 77:266-275. [PMID: 34305278 DOI: 10.1016/j.mjafi.2020.10.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 10/07/2020] [Indexed: 11/21/2022] Open
Abstract
Currently, most critical care information is not expressed automatically at a granular level, rather is continually assessed by overindulged Intensive Care Unit (ICU) staff. Furthermore, due to different confounding morbidities and the uniqueness of the ICU setting, it is difficult to protocolize treatment regimens in the ICU. In highly complex ICU setting where man and resource management becomes extremely challenging, definite advancements are required to implement Artificial Intelligence (AI) for prognosticating the course of the disease to aid in informed decision-making. AI is the intelligence of a computer or computer-supervised robot to execute a piece of work commonly associated with intelligent beings, wherein the machines go beyond the realms of normal information processing by adding the characteristics of learning, sound reasoning, and weighting of the inputs. AI recognizes circuitous, relational time-series blueprint within datasets and this reasoning of analysis transcends conventional threshold-based analysis adapted in ICU protocols. AI works on the principle of a more complex form of Machine Learning by Artificial Neural Networks (ANN). These information-processing paradigms use multidimensional arrays called tensors which aid in 'learning' and 'weighting' all the information made available to it, thereby converting normal machine learning into Deep Learning. Here, the use of AI for data mining in complex ICU settings for protocol formulation and temporal representation and reasoning is discussed.
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Kumar N, Kumar D. AN IMPROVED GREY WOLF OPTIMIZATION-BASED LEARNING OF ARTIFICIAL NEURAL NETWORK FOR MEDICAL DATA CLASSIFICATION. JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY 2021. [DOI: 10.32890/jict2021.20.2.4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson's diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an ANN.
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Affiliation(s)
- Narender Kumar
- Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, India
| | - Dharmender Kumar
- Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, India
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25
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Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020651] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.
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Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8843963. [PMID: 33415163 PMCID: PMC7769642 DOI: 10.1155/2020/8843963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/22/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022]
Abstract
The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.
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Alqudah AM, Alquran H, Qasmieh IA. Classification of heart sound short records using bispectrum analysis approach images and deep learning. NETWORK MODELING ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2020; 9:66. [DOI: 10.1007/s13721-020-00272-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/11/2020] [Accepted: 08/31/2020] [Indexed: 08/30/2023]
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Vasudevan RS, Horiuchi Y, Torriani FJ, Cotter B, Maisel SM, Dadwal SS, Gaynes R, Maisel AS. Persistent Value of the Stethoscope in the Age of COVID-19. Am J Med 2020; 133:1143-1150. [PMID: 32569591 PMCID: PMC7303610 DOI: 10.1016/j.amjmed.2020.05.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 12/25/2022]
Abstract
The stethoscope has long been at the center of patient care, as well as a symbol of the physician-patient relationship. While advancements in other diagnostic modalities have allowed for more efficient and accurate diagnosis, the stethoscope has evolved in parallel to address the needs of the modern era of medicine. These advancements include sound visualization, ambient noise reduction/cancellation, Bluetooth (Bluetooth SIG Inc, Kirkland, Wash) transmission, and computer algorithm diagnostic support. However, despite these advancements, the ever-changing climate of infection prevention, especially in the wake of the COVID-19 pandemic, has led many to question the stethoscope as a vector for infectious diseases. Stethoscopes have been reported to harbor bacteria with contamination levels comparable with a physician's hand. Although disinfection is recommended, stethoscope hygiene compliance remains low. In addition, disinfectants may not be completely effective in eliminating microorganisms. Despite these risks, the growing technological integration with the stethoscope continues to make it a highly valuable tool. Rather than casting our valuable tool and symbol of medicine aside, we must create and implement an effective method of stethoscope hygiene to keep patients safe.
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Affiliation(s)
- Rajiv S Vasudevan
- Department of Medicine, University of California San Diego, La Jolla.
| | - Yu Horiuchi
- Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan
| | - Francesca J Torriani
- Department of Medicine, University of California San Diego, La Jolla; Division of Infectious Diseases
| | - Bruno Cotter
- Department of Medicine, University of California San Diego, La Jolla; Division of Cardiovascular Medicine, University of California San Diego, La Jolla
| | | | - Sanjeet S Dadwal
- Division of Infectious Diseases, City of Hope National Medical Center, Duarte, Calif
| | - Robert Gaynes
- Division of Infectious Diseases, Emory University, Atlanta, Ga
| | - Alan S Maisel
- Department of Medicine, University of California San Diego, La Jolla; Division of Cardiovascular Medicine, University of California San Diego, La Jolla
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A Novel Approach for Coronary Artery Disease Diagnosis using Hybrid Particle Swarm Optimization based Emotional Neural Network. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.09.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Chen Y, Lv J, Sun Y, Jia B. Heart sound segmentation via Duration Long–Short Term Memory neural network. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106540] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Deperlioglu O, Kose U, Gupta D, Khanna A, Sangaiah AK. Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network. COMPUTER COMMUNICATIONS 2020; 162:31-50. [PMID: 32843778 PMCID: PMC7434639 DOI: 10.1016/j.comcom.2020.08.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/01/2020] [Accepted: 08/17/2020] [Indexed: 05/04/2023]
Abstract
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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Affiliation(s)
| | - Utku Kose
- Suleyman Demirel University, Isparta, Turkey
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Ashish Khanna
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Arun Kumar Sangaiah
- School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India
- Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Taiwan
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Khan FA, Abid A, Khan MS. Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features. Physiol Meas 2020; 41:055006. [PMID: 32259811 DOI: 10.1088/1361-6579/ab8770] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. APPROACH In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. MAIN RESULTS It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significanc e: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.
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Affiliation(s)
- Faiq Ahmad Khan
- Artificial Intelligence in Healthcare, Intelligent Information Processing Lab, National Center for Artificial Intelligence, University of Engineering and Technology, Peshawar, Pakistan
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Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico. INFORMATION 2020. [DOI: 10.3390/info11040207] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.
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Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern. Neural Comput Appl 2020. [DOI: 10.1007/s00521-018-3935-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals. Comput Biol Med 2020; 118:103632. [DOI: 10.1016/j.compbiomed.2020.103632] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 01/25/2020] [Accepted: 01/25/2020] [Indexed: 12/20/2022]
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YANG LIJUN, LI SHUANG, ZHANG ZHI, YANG XIAOHUI. CLASSIFICATION OF PHONOCARDIOGRAM SIGNALS BASED ON ENVELOPE OPTIMIZATION MODEL AND SUPPORT VECTOR MACHINE. J MECH MED BIOL 2020. [DOI: 10.1142/s0219519419500623] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than [Formula: see text] on both datasets show the effectiveness of the proposed model.
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Affiliation(s)
- LIJUN YANG
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, P. R. China
| | - SHUANG LI
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, P. R. China
| | - ZHI ZHANG
- Department of Computer Science, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - XIAOHUI YANG
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, P. R. China
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Saraf K, Baek CI, Wasko MH, Zhang X, Zheng Y, Borgstrom PH, Mahajan A, Kaiser WJ. Fully-Automated Diagnosis of Aortic Stenosis Using Phonocardiogram-Based Features .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6673-6676. [PMID: 31947372 DOI: 10.1109/embc.2019.8857506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The irreversible damage and eventual heart failure caused by untreated aortic stenosis (AS) can be prevented by early detection and timely intervention. Prior work in the field of phonocardiogram (PCG) signal analysis has provided proof of concept for using heart-sound data in AS diagnosis. However, such systems either require operation by trained technicians, fail to address a diverse subject set, or involve unwieldy configuration procedures that challenge real-world application. This paper presents an end-to-end, fully-automated system that uses noise-subtraction, heartbeat-segmentation and quality-assurance algorithms to extract physiologically-motivated features from PCG signals to diagnose AS. When tested on n=96 patients showing a diverse set of cardiac and non-cardiac conditions, the system was able to diagnose AS with 92% sensitivity and 95% specificity.
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Dong F, Qian K, Ren Z, Baird A, Li X, Dai Z, Dong B, Metze F, Yamamoto Y, Schuller B. Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS - the Heart Sounds Shenzhen Corpus. IEEE J Biomed Health Inform 2019; 24:2082-2092. [PMID: 31765322 DOI: 10.1109/jbhi.2019.2955281] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Auscultation of the heart is a widely studied technique, which requires precise hearing from practitioners as a means of distinguishing subtle differences in heart-beat rhythm. This technique is popular due to its non-invasive nature, and can be an early diagnosis aid for a range of cardiac conditions. Machine listening approaches can support this process, monitoring continuously and allowing for a representation of both mild and chronic heart conditions. Despite this potential, relevant databases and benchmark studies are scarce. In this paper, we introduce our publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), which was first released during the recent INTERSPEECH 2018 ComParE Heart Sound sub-challenge. Additionally, we provide a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models. At best our support vector machine with Log Mel features achieves 49.7% unweighted average recall on a three category task (normal, mild, moderate/severe).
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Machine learning-based classification of cardiac diseases from PCG recorded heart sounds. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04547-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Vennemann B, Obrist D, Rösgen T. Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning. PLoS One 2019; 14:e0222983. [PMID: 31557196 PMCID: PMC6762068 DOI: 10.1371/journal.pone.0222983] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 09/11/2019] [Indexed: 11/28/2022] Open
Abstract
The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loads which render them prone to premature degradation. Failing native aortic valves are routinely replaced with bioprosthetic heart valves. This type of prosthesis is limited by a durability that is often less than the patient's life expectancy. Frequent assessment of valvular function can therefore help to ensure good long-term outcomes and to plan reinterventions. In this article, we describe how unsupervised novelty detection algorithms can be used to automate the interpretation of blood flow data to improve outcomes through early detection of adverse cardiovascular events without requiring repeated check-ups in a clinical environment. The proposed method was tested in an in-vitro flow loop which allowed simulating a failing aortic valve in a laboratory setting. Aortic regurgitation of increasing severity was deliberately introduced with tube-shaped inserts, preventing complete valve closure during diastole. Blood flow recordings from a flow meter at the location of the ascending aorta were analyzed with the algorithms introduced in this article and a diagnostic index was defined that reflects the severity of valvular degradation. The results indicate that the proposed methodology offers a high sensitivity towards pathological changes of valvular function and that it is capable of automatically identifying valvular degradation. Such methods may be a step towards computer-assisted diagnostics and telemedicine that provide the clinician with novel tools to improve patient care.
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Affiliation(s)
- Bernhard Vennemann
- Institute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Dominik Obrist
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Rösgen
- Institute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland
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SINGH SINAMAJITKUMAR, MAJUMDER SWANIRBHAR. CLASSIFICATION OF UNSEGMENTED HEART SOUND RECORDING USING KNN CLASSIFIER. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419500258] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Due to low physical workout, high-calorie intake, and bad behavioral character, people were affected by cardiological disorders. Every instant, one out of four deaths are due to heart-related ailments. Hence, the early diagnosis of a heart is essential. Most of the approaches for automated classification of the heart sound need segmentation of Phonocardiograms (PCG) signal. The main aim of this study was to decline the segmentation process and to estimate the utility for accurate and detailed classification of short unsegmented PCG recording. Based on wavelet decomposition, Hilbert transform, homomorphic filtering, and power spectral density (PSD), the features had been obtained using the beginning 5 second PCG recording. The extracted features were classified using nearest neighbors with Euclidean distances for different values of [Formula: see text] by bootstrapping 50% PCG recording for training and 50% for testing over 100 iterations. The overall accuracy of 100%, 85%, 80.95%, 81.4%, and 98.13% had been achieved for five different datasets using KNN classifiers. The classification performance for analyzing the whole datasets is 90% accuracy with 93% sensitivity and 90% specificity. The classification of unsegmented PCG recording based on an efficient feature extraction is necessary. This paper presents a promising classification performance as compared with the state-of-the-art approaches in short time less complexity.
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Affiliation(s)
- SINAM AJITKUMAR SINGH
- Department of Electronics and Communication Engineering, NERIST Nirjuli, Arunachal Pradesh 791109, India
| | - SWANIRBHAR MAJUMDER
- Department of Information Technology, Tripura University, Agartala 799022, India
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Ukil A, Jara AJ, Marin L. Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2733. [PMID: 31216659 PMCID: PMC6631067 DOI: 10.3390/s19122733] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/12/2019] [Accepted: 06/13/2019] [Indexed: 11/18/2022]
Abstract
Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of relevant stakeholders. In this paper, we consider the cardiac health management system to demonstrate that data-driven techniques produce substantial performance merits in terms of clinical efficacy by employing robust machine learning methods with relevant and selected signal processing features. We consider phonocardiogram (PCG) or heart sound as the exemplary physiological signal. PCG carries substantial cardiac health signature to establish our claim of data-centric superior clinical utility. Our method demonstrates close to 85% accuracy on publicly available MIT-Physionet PCG datasets and outperform relevant state-of-the-art algorithm. Due to its simpler computational architecture of shallow classifier with just three features, the proposed analytics method is performed at edge gateway. However, it is to be noted that healthcare analytics deal with number of sensitive data and subsequent inferences, which need privacy protection. Additionally, the problem of healthcare data privacy prevention is addressed by de-risking of sensitive data management using differential privacy, such that controlled privacy protection on sensitive healthcare data can be enabled. When a user sets for privacy protection, appropriate privacy preservation is guaranteed for defense against privacy-breaching knowledge mining attacks. In this era of IoT and machine intelligence, this work is of practical importance, which enables on-demand automated screening of cardiac health under minimizing the privacy breaching risk.
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Affiliation(s)
- Arijit Ukil
- Research and Innovation, Tata Consultancy Services, Kolkata 700156, India.
| | - Antonio J Jara
- Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland.
- HOP Ubiquitous, 30562 Murcia, Spain.
| | - Leandro Marin
- Area of Applied Mathematics, Department of Engineering and Technology of Computers, Faculty of Computer Science, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain.
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Shi K, Schellenberger S, Michler F, Steigleder T, Malessa A, Lurz F, Ostgathe C, Weigel R, Koelpin A. Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification. IEEE Trans Biomed Eng 2019; 67:773-785. [PMID: 31180834 DOI: 10.1109/tbme.2019.2921071] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Radar technology promises to be a touchless and thereby burden-free method for continuous heart sound monitoring, which can be used to detect cardiovascular diseases. However, the first and most crucial step is to differentiate between high- and low-quality segments in a recording to assess their suitability for a subsequent automated analysis. This paper gives a comprehensive study on this task and first addresses the specific characteristics of radar-recorded heart sound signals. METHODS To gather heart sound signals recorded from radar, a bistatic radar system was built and installed at the university hospital. Under medical supervision, heart sound data were recorded from 30 healthy test subjects. The signals were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification algorithms were evaluated for the task of automated signal quality determination and the most promising one was optimized and evaluated using leave-one-subject-out cross validation. RESULTS The proposed classifier is able to achieve an accuracy of up to 96.36% and demonstrates a superior classification performance compared with the state-of-the-art classifier with a maximum accuracy of 76.00%. CONCLUSION This paper introduces an ensemble classifier that is able to perform automated signal quality determination of radar-recorded heart sound signals with a high accuracy. SIGNIFICANCE Besides achieving a higher performance compared with state-of-the-art classifiers, this study is the first one to deal with the quality determination of heart sounds that are recorded by radar systems. The proposed method enables contactless and continuous heart sound monitoring for the detection of cardiovascular diseases.
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Lee CH, Laurence DW, Ross CJ, Kramer KE, Babu AR, Johnson EL, Hsu MC, Aggarwal A, Mir A, Burkhart HM, Towner RA, Baumwart R, Wu Y. Mechanics of the Tricuspid Valve-From Clinical Diagnosis/Treatment, In-Vivo and In-Vitro Investigations, to Patient-Specific Biomechanical Modeling. Bioengineering (Basel) 2019; 6:E47. [PMID: 31121881 PMCID: PMC6630695 DOI: 10.3390/bioengineering6020047] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 05/16/2019] [Accepted: 05/17/2019] [Indexed: 12/29/2022] Open
Abstract
Proper tricuspid valve (TV) function is essential to unidirectional blood flow through the right side of the heart. Alterations to the tricuspid valvular components, such as the TV annulus, may lead to functional tricuspid regurgitation (FTR), where the valve is unable to prevent undesired backflow of blood from the right ventricle into the right atrium during systole. Various treatment options are currently available for FTR; however, research for the tricuspid heart valve, functional tricuspid regurgitation, and the relevant treatment methodologies are limited due to the pervasive expectation among cardiac surgeons and cardiologists that FTR will naturally regress after repair of left-sided heart valve lesions. Recent studies have focused on (i) understanding the function of the TV and the initiation or progression of FTR using both in-vivo and in-vitro methods, (ii) quantifying the biomechanical properties of the tricuspid valve apparatus as well as its surrounding heart tissue, and (iii) performing computational modeling of the TV to provide new insight into its biomechanical and physiological function. This review paper focuses on these advances and summarizes recent research relevant to the TV within the scope of FTR. Moreover, this review also provides future perspectives and extensions critical to enhancing the current understanding of the functioning and remodeling tricuspid valve in both the healthy and pathophysiological states.
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Affiliation(s)
- Chung-Hao Lee
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
- Institute for Biomedical Engineering, Science and Technology (IBEST), The University of Oklahoma, Norman, OK 73019, USA.
| | - Devin W Laurence
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
| | - Colton J Ross
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
| | - Katherine E Kramer
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
| | - Anju R Babu
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008, India.
| | - Emily L Johnson
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.
| | - Ming-Chen Hsu
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.
| | - Ankush Aggarwal
- Glasgow Computational Engineering Centre, School of Engineering, University of Glasgow, Scotland G12 8LT, UK.
| | - Arshid Mir
- Division of Pediatric Cardiology, Department of Pediatrics, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
| | - Harold M Burkhart
- Division of Cardiothoracic Surgery, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
| | - Rheal A Towner
- Advance Magnetic Resonance Center, MS 60, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA.
| | - Ryan Baumwart
- Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK 74078, USA.
| | - Yi Wu
- Biomechanics and Biomaterials Design Laboratory, School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
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Ayatollahi H, Gholamhosseini L, Salehi M. Predicting coronary artery disease: a comparison between two data mining algorithms. BMC Public Health 2019; 19:448. [PMID: 31035958 PMCID: PMC6489351 DOI: 10.1186/s12889-019-6721-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 03/28/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals. METHODS The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing. RESULTS The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test's result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model. CONCLUSION According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.
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Affiliation(s)
- Haleh Ayatollahi
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Leila Gholamhosseini
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
- School of Paramedical Sciences, AJA University of Medical Sciences, Tehran, Iran
| | - Masoud Salehi
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
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SHARMA RAHUL, SIRCAR PRADIP, PACHORI RAMBILAS. A NEW TECHNIQUE FOR CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING HIGHER-ORDER SPECTRA. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419400104] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a neurological disorder characterized by epileptic seizures inside the human brain. An authentic localization of epileptogenic area will help the clinicians for a successful epilepsy surgery. The epileptogenic area can be characterized by the focal electroencephalogram (EEG) signals. Hence, in this article, a bispectrum-based approach is implemented to characterize the focal EEG signals. The highest twenty-five magnitudes of bispectrum from the principal domain are used as features. The locality sensitive discriminant analysis (LSDA), data reduction technique, is implemented to reduce the number of attributes. The ranked LSDA attributes are input to the support vector machine (SVM) classifier yielding 96.2% classification accuracy using the entire Bern Barcelona EEG database. Hence, the proposed technique can be employed to confirm the epileptogenic area for successful epilepsy surgery and can be employed in the community health care centers and hospitals.
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Affiliation(s)
- RAHUL SHARMA
- Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - PRADIP SIRCAR
- Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - RAM BILAS PACHORI
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
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SHARMA RAHUL, SIRCAR PRADIP, PACHORI RB, BHANDARY SULATHAV, ACHARYA URAJENDRA. AUTOMATED GLAUCOMA DETECTION USING CENTER SLICE OF HIGHER ORDER STATISTICS. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419400116] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Glaucoma is one of the leading causes of blindness. The raised intraocular pressure is one of the important modifiable risk factor causing glaucomatous optic nerve damage. Glaucomatous optic nerve damage is seen as increase in the cupping of the optic disc and loss of neuroretinal rim. An automated detection system using nonlinear higher order statistics (HOS) based method is used to capture the detailed information present in the fundus image efficiently. The center slice of bispectrum and bicepstrum are applied on fundus images. Various features are extracted from the diagonal of these central slices. In order to reduce the number of features the locality sensitive discriminant analysis (LSDA) data reduction technique method is implemented. The ranked LSDA features are fed to support vector machine (SVM) classifier with various kernels for automated glaucoma detection. The simulation is performed on two databases. The proposed algorithm has yielded classification accuracy of 98.8% and 95% using entire private and public databases, respectively. The proposed technique achieved the highest classification accuracy, hence, confirm the diagnosis of ophthalmologists and can be employed in the community health care centers and hospitals.
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Affiliation(s)
- RAHUL SHARMA
- Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - PRADIP SIRCAR
- Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - R. B. PACHORI
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
| | - SULATHA V. BHANDARY
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
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Priyanga P, Naveen NC. Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2018. [DOI: 10.4018/ijhisi.2018100106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.
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Affiliation(s)
- P Priyanga
- Dept. of CSE, K.S. Institute of Technology, Bengaluru, India
| | - N C Naveen
- Dept. of CSE, J S S Academy of Technical Education, Bengaluru, India
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