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Alassafi MO, Aziz W, AlGhamdi R, Alshdadi AA, Nadeem MSA, Khan IR, Albishry N, Bahaddad A, Altalbe A. Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery. Comput Biol Med 2024; 170:108032. [PMID: 38310805 DOI: 10.1016/j.compbiomed.2024.108032] [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: 08/27/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/06/2024]
Abstract
COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.
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Affiliation(s)
- Madini O Alassafi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Wajid Aziz
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir Muzaffarabad (AK), Pakistan
| | - Rayed AlGhamdi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | | | - Malik Sajjad Ahmed Nadeem
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir Muzaffarabad (AK), Pakistan
| | | | - Nabeel Albishry
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Adel Bahaddad
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ali Altalbe
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Revathi T, Balasubramaniam S, Sureshkumar V, Dhanasekaran S. An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction. Diagnostics (Basel) 2024; 14:239. [PMID: 38337755 PMCID: PMC10855367 DOI: 10.3390/diagnostics14030239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/17/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.
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Affiliation(s)
- T.K. Revathi
- Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India;
| | | | - Vidhushavarshini Sureshkumar
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai 600026, India;
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Wang CL, Wei CC, Tsai CT, Lee YH, Liu LYM, Chen KY, Lin YJ, Lin PL. Early detection of myocardial ischemia in resting ECG: analysis by HHT. Biomed Eng Online 2023; 22:23. [PMID: 36894984 PMCID: PMC9999640 DOI: 10.1186/s12938-023-01089-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 03/01/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Exercise electrocardiography (ECG) is a noninvasive test aiming at producing ischemic changes. However, resting ECG cannot be adopted in diagnosing myocardial ischemia till ST-segment depressions. Therefore, this study aimed to detect myocardial energy defects in resting ECG using the Hilbert-Huang transformation (HHT) in patients with angina pectoris. METHODS Electrocardiographic recordings of positive exercise ECG by performing coronary imaging test (n = 26) and negative exercise ECG (n = 47) were collected. Based on the coronary stenoses severity, patients were divided into three categories: normal, < 50%, and ≥ 50%. During the resting phase of the exercise ECG, all 10-s ECG signals are decomposed by HHT. The RT intensity index, composed of the power spectral density of the P, QRS, and T components, is used to estimate the myocardial energy defect. RESULTS After analyzing the resting ECG using HHT, the RT intensity index was significantly higher in patients with positive exercise ECG (27.96%) than in those with negative exercise ECG (22.30%) (p < 0.001). In patients with positive exercise ECG, the RT intensity index was gradually increasing with the severity of coronary stenoses: 25.25% (normal, n = 4), 27.14% (stenoses < 50%, n = 14), and 30.75% (stenoses ≥ 50%, n = 8). The RT intensity index of different coronary stenoses was significantly higher in patients with negative exercise ECG, except for the normal coronary imaging test. CONCLUSIONS Patients with coronary stenoses had a higher RT index at the resting stage of exercise ECG. Resting ECG analyzed using HHT could be a method for the early detection of myocardial ischemia.
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Affiliation(s)
- Chun-Lin Wang
- Ph.D. Program of Technology Management, Chung Hua University, Hsinchu, 30012, Taiwan, ROC
| | - Chiu-Chi Wei
- Department of Industrial Management, Chung Hua University, Hsinchu, 30012, Taiwan, ROC
| | - Cheng-Ting Tsai
- Cardiovascular Center, MacKay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Rd., Zhongshan Dist., Taipei City, 104217, Taiwan, ROC.,Department of Cosmetic Applications and Management, MacKay Junior College of Medicine, Nursing, and Management, Taipei City, 112021, Taiwan, ROC
| | - Ying-Hsiang Lee
- Cardiovascular Center, MacKay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Rd., Zhongshan Dist., Taipei City, 104217, Taiwan, ROC.,Department of Medicine, MacKay Medical College, No. 46, Sec. 3, Zhongzheng Rd., Sanzhi Dist., New Taipei City, 252005, Taiwan, ROC.,Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, No. 46, Sec. 3, Zhongzheng Rd., Sanzhi Dist., New Taipei City, 252005, Taiwan, ROC
| | - Lawrence Yu-Min Liu
- Department of Medicine, MacKay Medical College, No. 46, Sec. 3, Zhongzheng Rd., Sanzhi Dist., New Taipei City, 252005, Taiwan, ROC.,Department of Nursing, MacKay Junior College of Medicine, Nursing, and Management, No. 92, Shengjing Rd., Beitou Dist., Taipei City, 112021, Taiwan, ROC
| | - Kang-Ying Chen
- Cardiovascular Center, MacKay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Rd., Zhongshan Dist., Taipei City, 104217, Taiwan, ROC.,Taoyuan Metro Corporation, Taoyuan City, 337601, Taiwan, ROC
| | - Yu-Jen Lin
- Department of Cosmetic Applications and Management, MacKay Junior College of Medicine, Nursing, and Management, Taipei City, 112021, Taiwan, ROC.,Big Light Optics Co., Ltd, Zhubei City, Hsinchu, 302051, Taiwan, ROC
| | - Po-Lin Lin
- Division of Cardiology, Department of Internal Medicine, Hsinchu MacKay Memorial Hospital, No. 690, Sec.2, Guangfu Rd., East Dist., Hsinchu City, 30071, Taiwan, ROC. .,Department of Nursing, MacKay Junior College of Medicine, Nursing, and Management, No. 92, Shengjing Rd., Beitou Dist., Taipei City, 112021, Taiwan, ROC.
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Nogueira MA, Calcagno S, Campbell N, Zaman A, Koulaouzidis G, Jalil A, Alam F, Stankovic T, Szabo E, Szabo AB, Kecskes I. Detecting heart failure using novel bio-signals and a knowledge enhanced neural network. Comput Biol Med 2023; 154:106547. [PMID: 36696813 DOI: 10.1016/j.compbiomed.2023.106547] [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: 07/19/2022] [Revised: 01/02/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
BACKGROUND Clinical decisions about Heart Failure (HF) are frequently based on measurements of left ventricular ejection fraction (LVEF), relying mainly on echocardiography measurements for evaluating structural and functional abnormalities of heart disease. As echocardiography is not available in primary care, this means that HF cannot be detected on initial patient presentation. Instead, physicians in primary care must rely on a clinical diagnosis that can take weeks, even months of costly testing and clinical visits. As a result, the opportunity for early detection of HF is lost. METHODS AND RESULTS The standard 12-Lead ECG provides only limited diagnostic evidence for many common heart problems. ECG findings typically show low sensitivity for structural heart abnormalities and low specificity for function abnormalities, e.g., systolic dysfunction. As a result, structural and functional heart abnormalities are typically diagnosed by echocardiography in secondary care, effectively creating a diagnostic gap between primary and secondary care. This diagnostic gap was successfully reduced by an AI solution, the Cardio-HART™ (CHART), which uses Knowledge-enhanced Neural Networks to process novel bio-signals. Cardio-HART reached higher performance in prediction of HF when compared to the best ECG-based criteria: sensitivity increased from 53.5% to 82.8%, specificity from 85.1% to 86.9%, positive predictive value from 57.1% to 70.0%, the F-score from 56.4% to 72.2%, and area under curve from 0.79 to 0.91. The sensitivity of the HF-indicated findings is doubled by the AI compared to the best rule-based ECG-findings with a similar specificity level: from 38.6% to 71%. CONCLUSION Using an AI solution to process ECG and novel bio-signals, the CHART algorithms are able to predict structural, functional, and valve abnormalities, effectively reducing this diagnostic gap, thereby allowing for the early detection of most common heart diseases and HF in primary care.
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Affiliation(s)
- Marta Afonso Nogueira
- Consultant Cardiologist Heart Failure and Cardiomyopathies, Department of Cardiology, Cascais Hospital, Lusíadas Saúde - UnitedHealth Group, Lisbon, Portugal
| | - Simone Calcagno
- Division of Cardiology, Santa Maria Goretti Hospital, Via Canova Snc, 04100, Latina, Italy
| | - Niall Campbell
- Manchester University NHS Foundation Trust, Department of Cardiology, Manchester, UK
| | - Azfar Zaman
- Freeman Hospital, Newcastle University, and Newcastle upon Tyne Hospitals NHS Trust, Newcastle, UK
| | | | - Anwar Jalil
- Cardiology of Karachi, Hill Park General Hospital, Karachi, Pakistan
| | - Firdous Alam
- Cardiology of Karachi, Hill Park General Hospital, Karachi, Pakistan
| | - Tatjana Stankovic
- Division of Cardiology, Regional Hospital Dr Radivoj Simonovic Sombor, Sombor, Serbia
| | - Erzsebet Szabo
- Division of Cardiology, General Hospital Senta, Senta, Serbia
| | - Aniko B Szabo
- Division of Cardiology, General Hospital Senta, Senta, Serbia
| | - Istvan Kecskes
- Dir. Cardiology Research and Scientific Advancements, UVA Research Corp., 24000, Subotica, Henrike Sjenkjevica 14, Serbia.
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Yadan Z, Jian W, Yifu L, Haiying L, Jie L, Hairui L. Solving the inverse problem based on UPEMD for electrocardiographic imaging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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6
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Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease. BMC Pulm Med 2021; 21:321. [PMID: 34654400 PMCID: PMC8518292 DOI: 10.1186/s12890-021-01682-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/29/2021] [Indexed: 11/10/2022] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people’s health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert–Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.
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Selek MB, Yesilkaya B, Egeli SS, Isler Y. The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis. Proc Inst Mech Eng H 2021; 235:1479-1488. [PMID: 34365841 DOI: 10.1177/09544119211036806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers' inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.
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Affiliation(s)
- Mustafa B Selek
- Ege Vocational School, Ege University, Bornova, Izmir, Turkey
| | - Bartu Yesilkaya
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, Izmir, Turkey.,Department of Biomedical Technologies, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Saadet S Egeli
- Department of Biomedical Technologies, Izmir Katip Celebi University, Cigli, Izmir, Turkey.,Islerya Medical and Information Technologies Company, Bornova, Izmir, Turkey
| | - Yalcin Isler
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, Izmir, Turkey.,Islerya Medical and Information Technologies Company, Bornova, Izmir, Turkey
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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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9
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Prediction of atrial fibrillation inducibility using spatiotemporal activation analysis combined with network mapping. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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10
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Lin CH, Wu JX, Kan CD, Chen PY, Chen WL. Arteriovenous shunt stenosis assessment based on empirical mode decomposition and 1D-convolutional neural network: Clinical trial stage. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102461] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Hussain L, Aziz W, Khan IR, Alkinani MH, Alowibdi JS. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 18:69-91. [PMID: 33525081 DOI: 10.3934/mbe.2021004] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, 13100, Muzaffarabad, Pakistan
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, 13230, Muzaffarabad, Pakistan
| | - Wajid Aziz
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Ishtiaq Rasool Khan
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Monagi H Alkinani
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Jalal S Alowibdi
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
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Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico. INFORMATION 2020. [DOI: 10.3390/info11040207] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.
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Altan G, Kutlu Y, Allahverdi N. Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform 2019; 24:1344-1350. [PMID: 31369388 DOI: 10.1109/jbhi.2019.2931395] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is comparing multiple machine learning algorithms for the early diagnosis of COPD using multi-channel lung sounds. METHODS Deep learning is an efficient machine-learning algorithm, which comprises unsupervised training to reduce optimization and supervised training by a feature-based distribution of classification parameters. This study focuses on analyzing multichannel lung sounds using statistical features of frequency modulations that are extracted using the Hilbert-Huang transform. RESULTS Deep learning algorithm was used in the classification stage of the proposed model to separate the patients with COPD and healthy subjects. The proposed DL model with the Hilbert-Huang transform based statistical features was successful in achieving high classification performance rates of 93.67%, 91%, and 96.33% for accuracy, sensitivity, and specificity, respectively. CONCLUSION The proposed computerized analysis of the multi-channel lung sounds using DL algorithms provides a standardized assessment with high classification performance. SIGNIFICANCE Our study is a pioneer study that directly focuses on the lung sounds to separate COPD and non-COPD patients. Analyzing 12-channel lung sounds gives the advantages of assessing the entire lung obstructions.
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Połap D, Woźniak M, Damaševičius R, Maskeliūnas R. Bio-inspired voice evaluation mechanism. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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15
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Tripathy RK, Paternina MRA, Arrieta JG, Zamora-Méndez A, Naik GR. Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 173:53-65. [PMID: 31046996 DOI: 10.1016/j.cmpb.2019.03.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 02/12/2019] [Accepted: 03/13/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. METHODS The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. RESULTS The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. CONCLUSIONS The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.
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Affiliation(s)
- R K Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
| | - Mario R A Paternina
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | | | - Alejandro Zamora-Méndez
- Electrical Engineering Faculty, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mich. 58030, Mexico
| | - Ganesh R Naik
- MARCS Institute, Western Sydney University Kingswood, NSW - 2747, Australia
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Estévez-Báez M, Machado C, García-Sánchez B, Rodríguez V, Alvarez-Santana R, Leisman G, Carrera JME, Schiavi A, Montes-Brown J, Arrufat-Pié E. Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability. Brain Inj 2019; 33:496-516. [PMID: 30755043 DOI: 10.1080/02699052.2018.1553312] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PRIMARY OBJECTIVE The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality. DESIGN AND METHODS Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions. RESULTS Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%. CONCLUSIONS HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.
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Affiliation(s)
- Mario Estévez-Báez
- a Department of Clinical Neurophysiology , Institute of Neurology and Neurosurgery , Havana , Cuba
| | - Calixto Machado
- a Department of Clinical Neurophysiology , Institute of Neurology and Neurosurgery , Havana , Cuba
| | | | | | | | - Gerry Leisman
- d Faculty of Health Sciences , University of Haifa , Haifa , Israel
| | | | - Adam Schiavi
- e Anesthesiology and Critical Care Medicine, Neurosciences Critical Care Division , Johns Hopkins Hospital , Baltimore , MD , USA
| | - Julio Montes-Brown
- f Department of Medicine & Health Science , University of Sonora , Sonora , Mexico
| | - Eduardo Arrufat-Pié
- g Institute of Basic and Preclinical Sciences, "Victoria de Girón" , Havana , Cuba
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Saber Iraji M. Prediction of post-operative survival expectancy in thoracic lung cancer surgery with soft computing. J Appl Biomed 2017. [DOI: 10.1016/j.jab.2016.12.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7030221] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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