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Wahbah M, Zitouni MS, Al Sakaji R, Funamoto K, Widatalla N, Krishnan A, Kimura Y, Khandoker AH. A deep learning framework for noninvasive fetal ECG signal extraction. Front Physiol 2024; 15:1329313. [PMID: 38711954 PMCID: PMC11073781 DOI: 10.3389/fphys.2024.1329313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/22/2024] [Indexed: 05/08/2024] Open
Abstract
Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.
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Affiliation(s)
- Maisam Wahbah
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - M. Sami Zitouni
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Raghad Al Sakaji
- Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | | | - Namareq Widatalla
- Health Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Anita Krishnan
- Children’s National Hospital, Washington, DC, United States
| | | | - Ahsan H. Khandoker
- Health Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
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2
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Noroozi Z, Orooji A, Erfannia L. Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction. Sci Rep 2023; 13:22588. [PMID: 38114600 PMCID: PMC10730875 DOI: 10.1038/s41598-023-49962-w] [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: 08/27/2023] [Accepted: 12/14/2023] [Indexed: 12/21/2023] Open
Abstract
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random Forest were applied to identify the best models for heart disease prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, and PRC were measured to compare feature selection methods' effect on prediction algorithms. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods (e.g., j48), whereas it led to a decrease in model performance in other models (e.g. MLP, RF). SVM-based filtering methods have a best-fit accuracy of 85.5. In fact, in a best-case scenario, filtering methods result in + 2.3 model accuracy. SVM-CFS/information gain/Symmetrical uncertainty methods have the highest improvement in this index. The filter feature selection methods with the highest number of features selected outperformed other methods in terms of models' ACC, Precision, and F-measures. However, wrapper-based and evolutionary algorithms improved models' performance from sensitivity and specificity points of view.
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Affiliation(s)
- Zeinab Noroozi
- Department of Artificial Intelligence, Islamic Azad University of Kazeroon, Kazeroon, Iran
| | - Azam Orooji
- Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Sciences (NKUMS), Bojnurd, North Khorasan, Iran
| | - Leila Erfannia
- Health Human Resources Research Center, Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
- Health Information Management Department, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
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3
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Khatun R, Akter M, Islam MM, Uddin MA, Talukder MA, Kamruzzaman J, Azad AKM, Paul BK, Almoyad MAA, Aryal S, Moni MA. Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data. Genes (Basel) 2023; 14:1802. [PMID: 37761941 PMCID: PMC10530870 DOI: 10.3390/genes14091802] [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: 08/15/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.
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Affiliation(s)
- Rabea Khatun
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka 1207, Bangladesh;
| | - Maksuda Akter
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh; (M.A.); (M.A.T.)
| | - Md. Manowarul Islam
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh; (M.A.); (M.A.T.)
| | - Md. Ashraf Uddin
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, VIC 3125, Australia; (M.A.U.); (S.A.)
| | - Md. Alamin Talukder
- Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh; (M.A.); (M.A.T.)
| | - Joarder Kamruzzaman
- Centre for Smart Analytics, Federation University Australia, Ballarat, VIC 3842, Australia;
| | - AKM Azad
- Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia;
| | - Bikash Kumar Paul
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh;
- Department of Software Engineering, Daffodil International University (DIU), Dhaka 1342, Bangladesh
| | - Muhammad Ali Abdulllah Almoyad
- Department of Basic Medical Sciences, College of Applied Medical Sciences in Khamis Mushyt King Khalid University, Abha 61412, Saudi Arabia;
| | - Sunil Aryal
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, VIC 3125, Australia; (M.A.U.); (S.A.)
| | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
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4
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Menshawi A, Hassan MM, Allheeib N, Fortino G. A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031392. [PMID: 36772430 PMCID: PMC9921250 DOI: 10.3390/s23031392] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/25/2022] [Accepted: 11/27/2022] [Indexed: 05/31/2023]
Abstract
The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline's ability to be retrained and used for other datasets collected using different measurements and with different distributions.
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Affiliation(s)
- Alaa Menshawi
- Information Systems Department, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mohammad Mehedi Hassan
- Information Systems Department, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia
| | - Nasser Allheeib
- Information Systems Department, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
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5
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Ha SS, Kim DK. Diagnostic Efficacy of Ultra-Short Term HRV Analysis in Obstructive Sleep Apnea. J Pers Med 2022; 12:jpm12091494. [PMID: 36143279 PMCID: PMC9505782 DOI: 10.3390/jpm12091494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
Heart rate variability (HRV) is the standard method for assessing autonomic nervous system (ANS) activity and is considered a surrogate marker for sympathetic overactivity in obstructive sleep apnea (OSA). Although HRV features are usually obtained from the short-term segment method, it is impossible to evaluate rapid dynamic changes in ANS activity. Herein, we propose the ultra-short-term analysis to detect the balance of ANS activity in patients with OSA. In 1021 OSA patients, 10 min HRV target datasets were extracted from polysomnographic data and analyzed by shifting the 2 min (ultra-short-term) and 5 min (short-term) segments. We detected frequency-domain parameters, including total power (Ln TP), very low frequency (Ln VLF), low frequency (Ln LF), and high frequency (Ln HF). We found that overall HRV feature alterations indicated sympathetic overactivity dependent on OSA severity, and that this was more pronounced in the ultra-short-term methodology. The apnea-hypopnea index, oxygen desaturation index, and Epworth sleepiness scale correlated with increased sympathetic activity and decreased parasympathetic activity, regardless of the methodology. The Bland-Altman plot analyses also showed a higher agreement of HRV features between the two methodologies. This study suggests that ultra-short-term HRV analysis may be a useful method for detecting alterations in ANS function in OSA patients.
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Affiliation(s)
- Seung-Su Ha
- Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Korea
| | - Dong-Kyu Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Korea
- Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Korea
- Correspondence: ; Tel.: +82-33-240-5180
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6
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Moth-Flame Optimization for Early Prediction of Heart Diseases. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9178302. [PMID: 36132544 PMCID: PMC9484941 DOI: 10.1155/2022/9178302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/16/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022]
Abstract
Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.
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Javeed A, Ali L, Mohammed Seid A, Ali A, Khan D, Imrana Y. A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1901735. [PMID: 35707186 PMCID: PMC9192258 DOI: 10.1155/2022/1901735] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/16/2022] [Indexed: 12/14/2022]
Abstract
Nowadays, caesarean section (CS) is given preference over vaginal birth and this trend is rapidly rising around the globe, although CS has serious complications such as pregnancy scar, scar dehiscence, and morbidly adherent placenta. Thus, CS should only be performed when it is absolutely necessary for mother and fetus. To avoid unnecessary CS, researchers have developed different machine-learning- (ML-) based clinical decision support systems (CDSS) for CS prediction using electronic health record of the pregnant women. However, previously proposed methods suffer from the problems of poor accuracy and biasedness in ML. To overcome these problems, we have designed a novel CDSS where random oversampling example (ROSE) technique has been used to eliminate the problem of minority classes in the dataset. Furthermore, principal component analysis has been employed for feature extraction from the dataset while, for classification purpose, random forest (RF) model is deployed. We have fine-tuned the hyperparameter of RF using a grid search algorithm for optimal classification performance. Thus, the newly proposed system is named ROSE-PCA-RF and it is trained and tested using an online CS dataset available on the UCI repository. In the first experiment, conventional RF model is trained and tested on the dataset while in the second experiment, the proposed model is tested. The proposed ROSE-PCA-RF model improved the performance of traditional RF by 4.5% with reduced time complexity, while only using two extracted features through the PCA. Moreover, the proposed model has obtained 96.29% accuracy on training data while improving the accuracy of 97.12% on testing data.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institute, Solna, Sweden
| | - Liaqat Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Bannu, Pakistan
| | - Abegaz Mohammed Seid
- Information & Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan
| | - Dilpazir Khan
- Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan
| | - Yakubu Imrana
- School of Engineering, University for Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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9
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Umer M, Sadiq S, Karamti H, Karamti W, Majeed R, NAPPI M. IoT Based Smart Monitoring of Patients' with Acute Heart Failure. SENSORS 2022; 22:s22072431. [PMID: 35408045 PMCID: PMC9003513 DOI: 10.3390/s22072431] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/05/2022]
Abstract
The prediction of heart failure survivors is a challenging task and helps medical professionals to make the right decisions about patients. Expertise and experience of medical professionals are required to care for heart failure patients. Machine Learning models can help with understanding symptoms of cardiac disease. However, manual feature engineering is challenging and requires expertise to select the appropriate technique. This study proposes a smart healthcare framework using the Internet-of-Things (IoT) and cloud technologies that improve heart failure patients’ survival prediction without considering manual feature engineering. The smart IoT-based framework monitors patients on the basis of real-time data and provides timely, effective, and quality healthcare services to heart failure patients. The proposed model also investigates deep learning models in classifying heart failure patients as alive or deceased. The framework employs IoT-based sensors to obtain signals and send them to the cloud web server for processing. These signals are further processed by deep learning models to determine the state of patients. Patients’ health records and processing results are shared with a medical professional who will provide emergency help if required. The dataset used in this study contains 13 features and was attained from the UCI repository known as Heart Failure Clinical Records. The experimental results revealed that the CNN model is superior to other deep learning and machine learning models with a 0.9289 accuracy value.
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Affiliation(s)
- Muhammad Umer
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan; (M.U.); (S.S.)
- Department of Computer Science Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
| | - Saima Sadiq
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan; (M.U.); (S.S.)
| | - Hanen Karamti
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Walid Karamti
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia;
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax 3052, Tunisia
| | - Rizwan Majeed
- Directorate of Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Michele NAPPI
- Department of Computer Science, University of Salerno, 84084 Fisciano, Italy
- Correspondence:
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Javid I, Zager Alsaedi AK, Ghazali R, Mohmad Hassim YM, Zulqarnain M. Optimally organized GRU-deep learning model with Chi2 feature selection for heart disease prediction. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17%. In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease.
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Affiliation(s)
- Irfan Javid
- Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn, Malaysia
- Department of Computer Science and Information Technology, University of Poonch, Rawalakot, AJK, Pakistan
| | | | - Rozaida Ghazali
- Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn, Malaysia
| | | | - Muhammad Zulqarnain
- Riphah College of Computing, Riphah International University Faisalabad Campus, Pakistan
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Eltrass AS, Tayel MB, Ammar AI. Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06889-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
AbstractElectrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.
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12
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Ogundokun RO, Misra S, Sadiku PO, Adeniyi JK. Assessment of Machine Learning Classifiers for Heart Diseases Discovery. INFORM SYST 2022. [DOI: 10.1007/978-3-030-95947-0_31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Cheng X, Manandhar I, Aryal S, Joe B. Application of Artificial Intelligence in Cardiovascular Medicine. Compr Physiol 2021; 11:2455-2466. [PMID: 34558666 DOI: 10.1002/cphy.c200034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine. © 2021 American Physiological Society. Compr Physiol 11:1-12, 2021.
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Affiliation(s)
- Xi Cheng
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Ishan Manandhar
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Sachin Aryal
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Bina Joe
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
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Naz H, Ahuja S. SMOTE-SMO-based expert system for type II diabetes detection using PIMA dataset. Int J Diabetes Dev Ctries 2021. [DOI: 10.1007/s13410-021-00969-x] [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] Open
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15
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Wang J, Liu W, Chen H, Liu C, Wang M, Chen H, Zhou H, Liu Z, Zhang S, Yu Z, Duan S, Deng Q, Sun J, Jiang H, Yu L. Novel Insights Into the Interaction Between the Autonomic Nervous System and Inflammation on Coronary Physiology: A Quantitative Flow Ratio Study. Front Cardiovasc Med 2021; 8:700943. [PMID: 34386531 PMCID: PMC8354298 DOI: 10.3389/fcvm.2021.700943] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/02/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Heart rate variability (HRV) was proposed as a noninvasive biomarker to stratify the risk of cardiovascular disease. However, it remains to be determined if HRV can be used as a surrogate for coronary artery physiology as analyzed by quantitative flow ratio (QFR) in patients with new-onset unstable angina pectoris (UAP). Methods: A total of 129 consecutive patients with new-onset UAP who underwent 24-h long-range 12-channel electrocardiography from June 2020 to December 2020 were included in this study. HRV, coronary angiography, and QFR information was retrieved from patient medical records, the severity of coronary lesions was evaluated using the Gensini score (GS), and total atherosclerotic burden was assessed using the three-vessel contrast QFR (3V-cQFR) calculated as the sum of cQFR in three vessels. Results: Multivariate logistic analysis showed that low-frequency power (LF) and high-sensitivity C-reactive protein (hs-CRP) were directly correlated with functional ischemia of target vessel, which were inversely correlated with total atherosclerotic burden as assessed by 3V-cQFR. Moreover, incorporation of the increase in LF into the existing model that uses clinical risk factors, GS, and hs-CRP significantly increased the discriminatory ability for evaluating coronary artery physiology of target vessel. Conclusions: LF and hs-CRP are independently associated with functional ischemia in patients with new-onset UAP. The relative increase of LF and hs-CRP could add value to the use of classical cardiovascular risk factors to predict the functional severity of coronary artery stenosis. Our results suggest a potential association between the autonomic nervous system, inflammation, and coronary artery physiology.
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Affiliation(s)
- Jun Wang
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Wei Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Huaqiang Chen
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Chengzhe Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Meng Wang
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Hu Chen
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Huixin Zhou
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Zhihao Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Song Zhang
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Zhongyang Yu
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Shoupeng Duan
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Qiang Deng
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Ji Sun
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Hong Jiang
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Lilei Yu
- Department of Cardiology, Renmin Hospital of Wuhan University, Cardiac Autonomic Nervous System Research Centre of Wuhan University, Cardiovascular Research Institute, Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan, China
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Ketu S, Mishra PK. Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05972-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27:2979-2993. [PMID: 34168402 PMCID: PMC8192292 DOI: 10.3748/wjg.v27.i22.2979] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.
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Affiliation(s)
- Yu-Jer Hsiao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yuan-Chih Wen
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Medical Education, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Wei-Yi Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Ying Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Internal Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Critical Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Yueh Chien
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | | | - De-Kuang Hwang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
| | - Tai-Chi Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
| | - Yun-Chia Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Ting-Yi Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Department of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Kao-Jung Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Shih-Hwa Chiou
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Ying-Chun Jheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Big Data Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
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18
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Basheer S, Alluhaidan AS, Bivi MA. Real-time monitoring system for early prediction of heart disease using Internet of Things. Soft comput 2021. [DOI: 10.1007/s00500-021-05865-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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19
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Xu J, Chen W. Impact of Water Temperature on Heart Rate Variability during Bathing. Life (Basel) 2021; 11:life11050378. [PMID: 33922202 PMCID: PMC8145520 DOI: 10.3390/life11050378] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
Abstract
Background: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. Methods: The bathtub WT was preset at three conditions: i.e., low WT (36–38 °C), medium WT (38–40 °C), and high WT (40–42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of t-tests, and the mean and standard deviation (SD) of each HRV feature based on the WT were calculated. Results: The statistics show that with increasing WT, 11 HRV features are significantly (p < 0.05) and monotonously reduced, four HRV features are significantly (p < 0.05) and monotonously rising, two HRV features are rising first and then reduced, two HRV features (fuzzy and approximate entropy) are almost unchanged, and vLF power is rising. Conclusion: The WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works.
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20
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Buyrukoğlu S. New hybrid data mining model for prediction of
Salmonella
presence in agricultural waters based on ensemble feature selection and machine learning algorithms. J Food Saf 2021. [DOI: 10.1111/jfs.12903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Selim Buyrukoğlu
- Department of Computer Engineering, Faculty of Engineering Çankırı Karatekin University Çankırı Turkey
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21
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Parsi A, Glavin M, Jones E, Byrne D. Prediction of paroxysmal atrial fibrillation using new heart rate variability features. Comput Biol Med 2021; 133:104367. [PMID: 33866252 DOI: 10.1016/j.compbiomed.2021.104367] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/09/2021] [Accepted: 03/29/2021] [Indexed: 02/01/2023]
Abstract
Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any further complications and avoid fatalities. An implantable defibrillator device could be used to both detect and treat the condition though such devices have limited computational capability. With this constraint in mind, this paper presents a novel set of features to accurately predict the presence of PAF. The method is evaluated using ECG signals from the widely used atrial fibrillation prediction database (AFPDB) from PhysioNet. We analysed 106 signals from 53 pairs of ECG recordings. Each pair of signals contains one 5-min ECG segment that ends just before the onset of a PAF event and another 5-min ECG segment at least 45 min distant from the PAF event, to represent a non-PAF event. Seven novel features are extracted through the Poincaré representation of R-R interval signals, and are prioritised through feature ranking schemes. The features are used with four standard classification techniques for PAF prediction and compared to the existing state of the art from the literature. Using only the seven proposed features, classification performance outperforms those of the classical state-of-the-art feature set, registering sensitivity and specificity measurements of over 96%. The results further improve when the features are combined with several of the classical features, with an accuracy increasing to 98% using a linear kernel SVM. The results show that the proposed features provide a useful representation of the PAF condition and achieve good prediction with off-the-shelf classification techniques that would be suitable for ICU deployment.
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Affiliation(s)
- Ashkan Parsi
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
| | - Martin Glavin
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
| | - Edward Jones
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
| | - Dallan Byrne
- National University of Ireland (NUI) Galway, Galway, H91 TK33, Ireland.
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22
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Alkhodari M, Jelinek HF, Werghi N, Hadjileontiadis LJ, Khandoker AH. Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models. IEEE J Biomed Health Inform 2021; 25:746-754. [PMID: 32750938 DOI: 10.1109/jbhi.2020.3002336] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVES The purpose of this study was to set an optimal fit of the estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines. METHODS Support vector regression (SVR) models were applied to estimate LVEF from ECG derived heart rate variability (HRV) data in one-hour intervals from 24-hour ECG recordings of patients with either preserved, mid-range, or reduced LVEF, obtained from the Intercity Digital ECG Alliance (IDEAL) study. A step-wise feature selection approach was used to ensure the best possible estimations of LVEF levels. RESULTS The experimental results have shown that the lowest Root Mean Square Error (RMSE) between the original and estimated LVEF levels was during 3-4 am, 5-6 am and 6-7 pm. CONCLUSION The observations suggest these hours as possible times for intervention and optimal treatment outcomes. In addition, LVEF classifications following the ACCF/AHA guidelines leads to a more accurate assessment of mid-range LVEF. SIGNIFICANCE This study paves the way to explore the use of HRV features in the prediction of LVEF percentages as an indicator of disease progression, which may lead to an automated classification process for CAD patients.
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23
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Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8387680. [PMID: 34306056 PMCID: PMC8266441 DOI: 10.1155/2021/8387680] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/15/2021] [Accepted: 06/21/2021] [Indexed: 02/07/2023]
Abstract
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.
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Affiliation(s)
- Rohit Bharti
- 1School of Computer Science and Engineering, Lovely Professional University, Phagwara, India
| | - Aditya Khamparia
- 2Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India
| | | | - Gaurav Dhiman
- 4Department of Computer Science, Government Bikram College of Commerce, Patiala, India
| | - Sagar Pande
- 1School of Computer Science and Engineering, Lovely Professional University, Phagwara, India
| | - Parneet Singh
- 5All India Institute of Medical Science, Rishikesh, India
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24
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Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network. Symmetry (Basel) 2020. [DOI: 10.3390/sym12122019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.
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25
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Fernandes SL, Gurupur VP, Sunder NR, Arunkumar N, Kadry S. A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2017.07.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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Maheswari S, Pitchai R. Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm. Curr Med Imaging 2020; 15:712-717. [PMID: 32008540 DOI: 10.2174/1573405614666180322141259] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 07/15/2017] [Accepted: 02/07/2018] [Indexed: 11/22/2022]
Abstract
The huge information of healthcare data is collected from the healthcare industry which is not "mined" unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Bayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.
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Affiliation(s)
- Subburaj Maheswari
- Department of Computer Science and Engineering, National Engineering College, Kovilpatti-628503, Tamil nadu, India
| | - Ramu Pitchai
- Department of Computer Science and Engineering, B.V. Raju Institute of Technology, Vishnupur, Narsapur, Telangana 502313, India
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27
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Zhao M, Guan L, Collet JP, Wang Y. Relationship between ischemic stroke locations, etiology subtypes, neurological outcomes, and autonomic cardiac function. Neurol Res 2020; 42:630-639. [PMID: 32701421 DOI: 10.1080/01616412.2020.1782103] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND Post-stroke autonomic nervous dysfunction measured with heart rate variability (HRV) is correlated with the traditional risk factors and poor outcome. This study aimed to investigate the association between HRV and infarct locations, etiology subtypes, and neurological functional outcomes in patients with acute ischemic stroke (AIS). METHODS In this prospective observational study, 186 consecutive patients were assigned to four major stroke severity categories based on the National Institutes of Health Stroke Scale score (NIHSS) and the modified Rankin Scale score (mRS): mild (NIHSS 0-4) stroke, moderate (NIHSS 5-14) stroke, 'favorable' (mRS 0-2) group, and 'unfavorable' (mRS 3-5) group. HRV time domain parameters were applied to evaluate the autonomic function of patients within 1 week after admission. All patients were classified into different etiology subtypes based on the TOAST (modified Trial of ORG 10172 in Acute Stroke Treatment) classification. The association of HRV with stroke location, etiology subtypes, neurological outcome was explored for all participants. Univariate and multivariate analyses were applied to explore the prediction value of HRV. RESULTS 160 participants had large artery atherosclerotic infarction (LAA), 61 had right internal carotid artery system infarction (R-ICA), and 61 had vertebrobasilar artery system infarction (VB). Root-mean-square of differences (RMSSD) of adjacent RR intervals and the proportion calculated by dividing the interbeat interval differences >50 ms (pNN50) in patients of VB group was significantly lower than those of patients in R-ICA group (P < 0.01). HRV parameters in the LAA group was significantly lower than non-LAA group (P < 0.01). At discharge, significant lower HRV presented in the unfavorable group and moderate group (P < 0.05). After logistic univariate and multivariate analysis, lower SDNN (OR = 1.019; 95% CI = 1.003-1.035; p= 0.021) was independently associated with unfavorable mRS and higher NIHSS at discharge (OR = 1.013; 95%CI = 1.003-1.024; p= 0.015). Only SDNN showed predictive value for mRS≥3 (OR = 1.012; 95%CI = 1.002-1.022; p= 0.016) at 1 year. CONCLUSIONS HRV measured after admission is related to the AIS infarction basin, TOAST subtypes, and neurological outcomes at discharge suggesting a possible role for HRV in evaluating AIS and identifying high-risk patients.
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Affiliation(s)
- Mengxi Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University , Beijing, China
| | - Ling Guan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University , Beijing, China.,National Clinical Research Center for Neurological Diseases , Beijing, China
| | - Jean-Paul Collet
- Department of Medicine, BC Children's Hospital Research Institute, University of British Columbia , Vancouver, Canada.,Advanced Innovation Center for Human Brain Protection, Capital Medical University , Beijing, China
| | - Yilong Wang
- Advanced Innovation Center for Human Brain Protection, Capital Medical University , Beijing, China.,Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University , Beijing, China
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28
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Hussain L, Awan IA, Aziz W, Saeed S, Ali A, Zeeshan F, Kwak KS. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4281243. [PMID: 32149106 PMCID: PMC7049402 DOI: 10.1155/2020/4281243] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 12/21/2019] [Accepted: 01/20/2020] [Indexed: 01/11/2023]
Abstract
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
| | - Wajid Aziz
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
- College of Computer Sciences and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
| | - Sharjil Saeed
- Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan
| | - Amjad Ali
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Farukh Zeeshan
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Kyung Sup Kwak
- Department of Information and Communication Engineering, Inha University, Incheon, Republic of Korea
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Classification models for heart disease prediction using feature selection and PCA. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100330] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Darmawahyuni A, Nurmaini S, Yuwandini M, Muhammad Naufal Rachmatullah, Firdaus F, Tutuko B. Congestive heart failure waveform classification based on short time-step analysis with recurrent network. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100441] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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A Hybrid Risk Assessment Model for Cardiovascular Disease Using Cox Regression Analysis and a 2-means clustering algorithm. Comput Biol Med 2019; 113:103400. [PMID: 31491657 DOI: 10.1016/j.compbiomed.2019.103400] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 08/21/2019] [Accepted: 08/22/2019] [Indexed: 02/01/2023]
Abstract
Cardiovascular disease (CVD) refers to a state that indicates narrowed or blocked blood vessels, and it can lead to cardiac arrest, chest pain (angina) or stroke. CVD is a leading cause of silent massive heart attacks and is a major threat to life. The mere prediction of the presence or absence of CVD alone is inefficient in current scenarios. Rather, a major need has arisen for the prediction of CVD, the acquisition of knowledge about CVD and the assessment of the likelihood that an individual will experience cardiac arrest. The objective of establishing an individual CVD risk assessment has been attained in this paper using a hybrid model. The CVD of an individual is due to various controllable and uncontrollable factors. The computation and analysis of all these factors are difficult and time consuming. Only a few attributes are identified to be the most critical. This optimization of the critical features is performed using a modified Differential Evolution (DE) algorithm. The identified critical factors are sufficient to predict the presence/absence of CVD. In this paper, these identified critical features of individuals are considered using Cox regression analysis that evaluates the prevalence rates of the critical attributes. These individual prevalence rates together predict the cumulative prevalence ratios of the respective individuals. This cumulative prevalence ratio of an individual, along with the class attribute, is processed using the 2-means clustering technique to determine the risk of a particular individual developing CVD. The evaluation of the risk assessment model is carried out in this paper by calculating the prediction accuracy of the Cox regression analysis and the Davies-Bouldin (DB) index for 2-means clustering. The Cox regression analysis results in a 91% CVD prediction accuracy using the critical attributes and is comparatively higher than that of other models. The DB index of 2-means clustering with specific initial means for clusters of individuals with CVD is 0.282 and that for clusters of individuals without CVD is 0.2836, which are comparatively lower than those of the traditional k-means clustering algorithm.
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Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173496] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.
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Jovic A, Brkic K, Krstacic G. Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101583] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9122544] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.
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Jahmunah V, Oh SL, Wei JKE, Ciaccio EJ, Chua K, San TR, Acharya UR. Computer-aided diagnosis of congestive heart failure using ECG signals - A review. Phys Med 2019; 62:95-104. [PMID: 31153403 DOI: 10.1016/j.ejmp.2019.05.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/02/2019] [Accepted: 05/04/2019] [Indexed: 12/16/2022] Open
Abstract
The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF.
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Affiliation(s)
- V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Joel Koh En Wei
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | - Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia.
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Tripoliti EE, Karanasiou GS, Kalatzis FG, Bechlioulis A, Goletsis Y, Naka K, Fotiadis DI. HEARTEN KMS - A knowledge management system targeting the management of patients with heart failure. J Biomed Inform 2019; 94:103203. [PMID: 31071455 DOI: 10.1016/j.jbi.2019.103203] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 05/03/2019] [Accepted: 05/04/2019] [Indexed: 11/19/2022]
Abstract
The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multi-parametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78% to 95% depending on the module/functionality.
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Affiliation(s)
- Evanthia E Tripoliti
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Georgia S Karanasiou
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Fanis G Kalatzis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Aris Bechlioulis
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Yorgos Goletsis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Economics, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Katerina Naka
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios I Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
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He T, Puppala M, Ezeana CF, Huang YS, Chou PH, Yu X, Chen S, Wang L, Yin Z, Danforth RL, Ensor J, Chang J, Patel T, Wong ST. A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer. JCO Clin Cancer Inform 2019; 3:1-12. [PMID: 31141423 PMCID: PMC10445790 DOI: 10.1200/cci.18.00121] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2019] [Indexed: 08/25/2023] Open
Abstract
PURPOSE The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the application of the BI-RADS lexicon, the decision to biopsy varies greatly and results in overdiagnosis and excessive biopsies. The false-positive rate from mammograms is estimated to be 7% to approximately 10% overall, but within the BI-RADS 4 category, it is greater than 70%. Therefore, we developed the Breast Cancer Risk Calculator (BRISK) to target a well-characterized and specific patient subgroup (BI-RADS 4) rather than a broad heterogeneous group in assessing breast cancer risk. METHODS BRISK provides a novel precise risk assessment model to reduce overdiagnosis and unnecessary biopsies. It was developed by applying natural language processing and deep learning methods on 5,147 patient records archived in the Houston Methodist systemwide data warehouse from 2006 to May 2015, including imaging and pathology reports, mammographic images, and patient demographics. Key characteristics for BI-RADS 4 patients were collected and computed to output an index measure for biopsy recommendation that is clinically relevant and informative and improves upon the traditional BI-RADS 4 scores. RESULTS For the validation set, we assessed data from 1,247 BI-RADS 4 patients, including mammographic images and medical reports. The BRISK model sensitivity to predict malignancy was 100%, whereas the specificity was 74%. The total accuracy of our implemented model in BRISK was 81%. Overall area under the curve was 0.93. CONCLUSION BRISK for abnormal mammogram uses integrative artificial intelligence technology and has demonstrated high sensitivity in the prediction of malignancy. Prospective evaluation is under way and can lead to improvement in patient-physician engagement in making informed decisions with regard to biopsy.
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Affiliation(s)
| | | | | | - Yan-siang Huang
- Houston Methodist, Houston, TX
- Far-Eastern Memorial Hospital, Taiwan,
Republic of China
| | - Ping-hsuan Chou
- Houston Methodist, Houston, TX
- Far-Eastern Memorial Hospital, Taiwan,
Republic of China
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Parsi A, O'Loughlin D, Glavin M, Jones E. Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features. IEEE Rev Biomed Eng 2019; 13:5-16. [PMID: 31021774 DOI: 10.1109/rbme.2019.2912313] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Over the last four decades, implantable cardioverter defibrillators (ICDs) have been widely deployed to reduce sudden cardiac death (SCD) risk in patients with a history of life-threatening arrhythmia. By continuous monitoring of the heart rate, ICDs can use decision algorithms to distinguish normal cardiac sinus rhythm or supra-ventricular tachycardia from abnormal cardiac rhythms like ventricular tachycardia and ventricular fibrillation and deliver appropriate therapy such as an electrical stimulus. Despite the success of ICDs, more research is still needed, particularly in decision-making algorithms. Because of low specificity in practical devices, patients with ICDs still receive inappropriate shocks, which may lead to inadvertent mortality and reduction of quality of life. At the same time, higher sensitivity can lead to the use of newer tiered therapies. The purpose of this study is to review the literature on common signal features used in detection algorithms for abnormal cardiac sinus rhythm, as well as reviewing datasets used for algorithm development in previous studies. More than 50 different features to address heart rate changes before SCD have been reviewed and general methodology on this area proposed based on variety of studies on ICDs functionality. A comparative study on the prediction performance of these features, using a common database, is also presented. By combining these features with a support vector machine classifier, achieved results have compared well with other studies.
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Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J 2019; 49:629-639. [PMID: 31074221 PMCID: PMC6597456 DOI: 10.4070/kcj.2018.0446] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/28/2019] [Accepted: 02/19/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840-0.845) and 0.889 (0.887-0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797-0.803], 0.847 [0.844-0.850]) and RF (0.807 [0.804-0.810], 0.853 [0.850-0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819-0.823) and 0.850 (0.848-0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
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Affiliation(s)
- Joon Myoung Kwon
- Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea
| | - Kyung Hee Kim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
| | - Ki Hyun Jeon
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Hyue Mee Kim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Min Jeong Kim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Sung Min Lim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Pil Sang Song
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Jinsik Park
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Rak Kyeong Choi
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Byung Hee Oh
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
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Castaldo R, Montesinos L, Melillo P, James C, Pecchia L. Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life. BMC Med Inform Decis Mak 2019; 19:12. [PMID: 30654799 PMCID: PMC6335694 DOI: 10.1186/s12911-019-0742-y] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 01/10/2019] [Indexed: 11/24/2022] Open
Abstract
Background This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. Methods ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. Results Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. Conclusion This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation. Electronic supplementary material The online version of this article (10.1186/s12911-019-0742-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- R Castaldo
- School of Engineering, University of Warwick, CV47AL, Coventry, UK.,Institute of Advanced Studies, University of Warwick, CV47AL, Coventry, UK
| | - L Montesinos
- School of Engineering, University of Warwick, CV47AL, Coventry, UK
| | - P Melillo
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania Luigi Vanvitelli, Naples, Italy
| | - C James
- School of Engineering, University of Warwick, CV47AL, Coventry, UK
| | - L Pecchia
- School of Engineering, University of Warwick, CV47AL, Coventry, UK.
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Shirole U, Joshi M, Bagul P. Cardiac, diabetic and normal subjects classification using decision tree and result confirmation through orthostatic stress index. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Wu HT, Soliman EZ. A new approach for analysis of heart rate variability and QT variability in long-term ECG recording. Biomed Eng Online 2018; 17:54. [PMID: 29720178 PMCID: PMC5932763 DOI: 10.1186/s12938-018-0490-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 04/23/2018] [Indexed: 12/29/2022] Open
Abstract
Background and purpose With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings. Methods In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches. Results As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account. Conclusion The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings. Electronic supplementary material The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hau-Tieng Wu
- Department of Mathematics and Department of Statistical Science, Duke University, 207 Physics Building, 120 Science Dr, Durham, NC, 27705, USA. .,Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan.
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center (EPICARE), Department of Epidemiology, Wake Forest School of Medicine, Winston-Salem, NC, USA.,Department of Internal Medicine, Section on Cardiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Lan KC, Raknim P, Kao WF, Huang JH. Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study. J Med Syst 2018; 42:103. [DOI: 10.1007/s10916-018-0942-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 03/15/2018] [Indexed: 10/17/2022]
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Perveen S, Shahbaz M, Keshavjee K, Guergachi A. A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression. Sci Rep 2018; 8:2112. [PMID: 29391513 PMCID: PMC5794753 DOI: 10.1038/s41598-018-20166-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 01/02/2018] [Indexed: 12/14/2022] Open
Abstract
Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently no non-invasive ATP III criteria based prediction method is available to diagnose NAFLD risk. Firstly, the objective of this research is to develop machine learning based method in order to identify individuals at an increased risk of developing NAFLD using risk factors of ATP III clinical criteria updated in 2005 for Metabolic Syndrome (MetS). Secondly, to validate the relative ability of quantitative score defined by Italian Association for the Study of the Liver (IASF) and guideline explicitly defined for the Canadian population based on triglyceride thresholds to predict NAFLD risk. We proposed a Decision Tree based method to evaluate the risk of developing NAFLD and its progression in the Canadian population, using Electronic Medical Records (EMRs) by exploring novel risk factors for NAFLD. Our results show proposed method could potentially help physicians make more informed choices about their management of patients with NAFLD. Employing the proposed application in ordinary medical checkup is expected to lessen healthcare expenditures compared with administering additional complicated test.
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Affiliation(s)
- Sajida Perveen
- Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan.
| | - Muhammad Shahbaz
- Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, M5B 2K3, Canada
| | - Karim Keshavjee
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, M5B 2K3, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Aziz Guergachi
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, M5B 2K3, Canada
- Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Ontario, Canada
- Department of Mathematics & Statistics, York University, Toronto, Ontario, Canada
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Valenza G, Wendt H, Kiyono K, Hayano J, Watanabe E, Yamamoto Y, Abry P, Barbieri R. Mortality Prediction in Severe Congestive Heart Failure Patients with Multifractal Point-Process Modeling of Heartbeat Dynamics. IEEE Trans Biomed Eng 2018; 65:2345-2354. [PMID: 29993522 DOI: 10.1109/tbme.2018.2797158] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Multifractal analysis of human heartbeat dynamics has been demonstrated to provide promising markers of Congestive Heart Failure (CHF). Yet, it crucially builds on the interpolation of RR intervals series, which has been generically performed with limited links to CHF pathophysiology. We devise a novel methodology estimating multifractal autonomic dynamics from heartbeat-derived series defined in the continuous time. We hypothesize that markers estimated from our novel framework are also effective for mortality prediction in severe CHF. We merge multifractal analysis within a methodological framework based on inhomogeneous point process models of heartbeat dynamics. Specifically, wavelet coefficients and wavelet leaders are computed over measures extracted from instantaneous statistics of probability density functions characterizing and predicting the time until the next heartbeat event occurs. The proposed approach is tested on data from 94 CHF patients, aiming at predicting survivor and non-survivor individuals as determined after a 4 years follow up. Instantaneous markers of vagal and sympatho-vagal dynamics display power-law scaling for a large range of scales, from s to s. Using standard SVM algorithms, the proposed inhomogeneous point-process representation based multifractal analysis achieved the best CHF mortality prediction accuracy of 79.11 % (sensitivity 90.48%, specificity 67.74%). Our results suggest that heartbeat scaling and multifractal properties in CHF patients are not generated at the sinus-node level, but rather by the intrinsic action of vagal short-term control and of sympatho-vagal fluctuations associated with circadian cardiovascular control, especially within the VLF band. These markers might provide critical information in devising a clinical tool for individualized prediction of survivor and non-survivor CHF patients.
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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications. SUSTAINABILITY 2017. [DOI: 10.3390/su9122309] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Mahajan R, Viangteeravat T, Akbilgic O. Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics. Int J Med Inform 2017; 108:55-63. [DOI: 10.1016/j.ijmedinf.2017.09.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 07/22/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022]
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Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice. J Med Syst 2017; 41:178. [PMID: 28956226 DOI: 10.1007/s10916-017-0823-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.
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Arevalillo JM, Sztein MB, Kotloff KL, Levine MM, Simon JK. Identification of immune correlates of protection in Shigella infection by application of machine learning. J Biomed Inform 2017; 74:1-9. [PMID: 28802838 DOI: 10.1016/j.jbi.2017.08.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 07/10/2017] [Accepted: 08/08/2017] [Indexed: 11/25/2022]
Abstract
BACKGROUND Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. METHODS The proposed methodology utilizes the Random Forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. RESULTS The results demonstrate that the combination of Classification and Regression Trees and Random Forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. CONCLUSION Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines.
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Affiliation(s)
- Jorge M Arevalillo
- Department of Statistics and Operational Research, University Nacional Educación a Distancia, Paseo Senda del Rey 9, 28040 Madrid, Spain.
| | - Marcelo B Sztein
- Center for Vaccine Development, Departments of Pediatrics and Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Karen L Kotloff
- Center for Vaccine Development, Departments of Pediatrics and Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Myron M Levine
- Center for Vaccine Development, Departments of Pediatrics and Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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