1
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Chen WW, Tseng CC, Huang CC, Lu HHS. Improving deep-learning electrocardiogram classification with an effective coloring method. Artif Intell Med 2024; 149:102809. [PMID: 38462295 DOI: 10.1016/j.artmed.2024.102809] [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: 07/22/2023] [Revised: 12/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis and model training, integrating additional clinical information, especially demographic data, remains challenging. In this study, we present an innovative approach to ECG classification by incorporating demographic information from patients' medical histories through a colorization technique. Our proposed method maps demographic features onto the (R, G, B) color space through normalized scaling. Each demographic feature corresponds to a distinct color, allowing for different ECG leads to be colored. This approach preserves the relationships between data by maintaining the color correlations in the statistical features, enhancing ECG analytics and supporting precision medicine. We conducted experiments with PTB-XL dataset and achieved 1%-6% improvements in the area under the receiving operator characteristic curve performance compared with other methods for various classification problems. Notably, our method excelled in multiclass and challenging classification tasks. The combined use of color features and the original waveform shape features enhanced prediction accuracy for various deep learning models. Our findings suggest that colorization is a promising avenue for advancing ECG classification and diagnosis, contributing to improved prediction and diagnosis of cardiovascular diseases and ultimately enhancing clinical outcomes.
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Affiliation(s)
- Wei-Wen Chen
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chien-Chao Tseng
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ching-Chun Huang
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
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2
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Hussain NM, O'Halloran M, McDermott B, Elahi MA. Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities. Biomed Phys Eng Express 2024; 10:022002. [PMID: 38118183 DOI: 10.1088/2057-1976/ad17a6] [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: 05/13/2023] [Accepted: 12/20/2023] [Indexed: 12/22/2023]
Abstract
Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.
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Affiliation(s)
- Nadia Muhammad Hussain
- Discipline of Electrical & Electronic Engineering, University of Galway, Ireland
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
| | - Martin O'Halloran
- Discipline of Electrical & Electronic Engineering, University of Galway, Ireland
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
| | - Barry McDermott
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
- College of Medicine, Nursing & Health Sciences, University of Galway, Ireland
| | - Muhammad Adnan Elahi
- Discipline of Electrical & Electronic Engineering, University of Galway, Ireland
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
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3
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Minic A, Jovanovic L, Bacanin N, Stoean C, Zivkovic M, Spalevic P, Petrovic A, Dobrojevic M, Stoean R. Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:9878. [PMID: 38139724 PMCID: PMC10747899 DOI: 10.3390/s23249878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/28/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.
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Affiliation(s)
- Ana Minic
- Teacher Education Faculty, University of Pristina in Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia;
| | - Luka Jovanovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Catalin Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Petar Spalevic
- Faculty of Technical Science, University of Pristina in Kosovska Mitrovica, Filipa Visnjica bb, 38220 Kosovska Mitrovica, Serbia;
| | - Aleksandar Petrovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Milos Dobrojevic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Ruxandra Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
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4
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Bian Y, Chen J, Chen X, Yang X, Chen DZ, Wu J. Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2434-2444. [PMID: 34990368 DOI: 10.1109/tcbb.2022.3140785] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A large number of people suffer from life-threatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of such abnormalities. Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases. However, the predictions of the known neural networks still do not satisfactorily meet the needs of clinicians, and this phenomenon suggests that some information used in clinical diagnosis may not be well captured and utilized by these methods. In this paper, we introduce some rules into convolutional neural networks, which help present clinical knowledge to deep learning based ECG analysis, in order to improve automated ECG diagnosis performance. Specifically, we propose a Handcrafted-Rule-enhanced Neural Network (called HRNN) for ECG classification with standard 12-lead ECG input, which consists of a rule inference module and a deep learning module. Experiments on two large-scale public ECG datasets show that our new approach considerably outperforms existing state-of-the-art methods. Further, our proposed approach not only can improve the diagnosis performance, but also can assist in detecting mislabelled ECG samples.
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5
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Xia Y, Wang W, Wang K. ECG signal generation based on conditional generative models. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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6
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Mu N, Rezaeitaleshmahalleh M, Lyu Z, Wang M, Tang J, Strother CM, Gemmete JJ, Pandey AS, Jiang J. Can we explain machine learning-based prediction for rupture status assessments of intracranial aneurysms? Biomed Phys Eng Express 2023; 9:037001. [PMID: 36626819 PMCID: PMC9999353 DOI: 10.1088/2057-1976/acb1b3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/04/2023] [Accepted: 01/10/2023] [Indexed: 01/11/2023]
Abstract
Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP) algorithms to explain and analyze 6 Ml algorithms. All models performed comparably: LR area under the curve (AUC) was 0.71; SVM AUC was 0.76; RF AUC was 0.73; XGBoost AUC was 0.78; MLPNN AUC was 0.73; BART AUC was 0.73. Our interpretability analysis demonstrated consistent results across all the methods; i.e., the utility of the top 12 features was broadly consistent. Furthermore, contributions of 9 important features (aneurysm area, aneurysm location, aneurysm type, wall shear stress maximum during systole, ostium area, the size ratio between aneurysm width, (parent) vessel diameter, one standard deviation among time-averaged low shear area, and one standard deviation of temporally averaged low shear area less than 0.4 Pa) were nearly the same. This research suggested that ML classifiers can provide explainable predictions consistent with general domain knowledge concerning IA rupture. With the improved understanding of ML algorithms, clinicians' trust in ML algorithms will be enhanced, accelerating their clinical translation.
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Affiliation(s)
- N Mu
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - M Rezaeitaleshmahalleh
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Z Lyu
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - M Wang
- Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonino, TX, United States of America
| | - J Tang
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, United States of America
| | - C M Strother
- Department of Radiology, University of Wisconsin, Madison, WI, United States of America
| | - J J Gemmete
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - A S Pandey
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, United States of America
| | - J Jiang
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
- Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, United States of America
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7
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Nagaraj J, Leema A. Light weight multi-branch network-based extraction and classification of myocardial infarction from 12 lead electrocardiogram images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2178608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Jothiaruna Nagaraj
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Anny Leema
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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8
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Min Hyun C, Jun Jang T, Nam J, Kwon H, Jeon K, Lee K. Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1088/2632-2153/acc637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023] Open
Abstract
Abstract
Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.
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9
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Irin Sherly S, Mathivanan G. An efficient honey badger based Faster region CNN for chronc heart Failure prediction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Li C, Sun L, Peng D, Subramani S, Nicolas SC. A multi-label classification system for anomaly classification in electrocardiogram. Health Inf Sci Syst 2022; 10:19. [PMID: 36032778 PMCID: PMC9411383 DOI: 10.1007/s13755-022-00192-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/29/2022] [Indexed: 11/28/2022] Open
Abstract
Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.
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Affiliation(s)
- Chenyang Li
- Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China
| | - Le Sun
- Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China
| | - Dandan Peng
- School of Computer Science and Network Engineering, Guangzhou University, Guangzhou, China
| | - Sudha Subramani
- Information Technology Discipline, Victoria University, Melbourne, Australia
| | - Shangwe Charmant Nicolas
- Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China
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11
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Wu L, Zhou B, Liu D, Wang L, Zhang X, Xu L, Yuan L, Zhang H, Ling Y, Shi G, Ke S, He X, Tian B, Chen Y, Qian X. LASSO Regression-Based Diagnosis of Acute ST-Segment Elevation Myocardial Infarction (STEMI) on Electrocardiogram (ECG). J Clin Med 2022; 11:jcm11185408. [PMID: 36143055 PMCID: PMC9505979 DOI: 10.3390/jcm11185408] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/20/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
Electrocardiogram (ECG) is an important tool for the detection of acute ST-segment elevation myocardial infarction (STEMI). However, machine learning (ML) for the diagnosis of STEMI complicated with arrhythmia and infarct-related arteries is still underdeveloped based on real-world data. Therefore, we aimed to develop an ML model using the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically diagnose acute STEMI based on ECG features. A total of 318 patients with STEMI and 502 control subjects were enrolled from Jan 2017 to Jun 2019. Coronary angiography was performed. A total of 180 automatic ECG features of 12-lead ECG were input into the model. The LASSO regression model was trained and validated by the internal training dataset and tested by the internal and external testing datasets. A comparative test was performed between the LASSO regression model and different levels of doctors. To identify the STEMI and non-STEMI, the LASSO model retained 14 variables with AUCs of 0.94 and 0.93 in the internal and external testing datasets, respectively. The performance of LASSO regression was similar to that of experienced cardiologists (AUC: 0.92) but superior (p < 0.05) to internal medicine residents, medical interns, and emergency physicians. Furthermore, in terms of identifying left anterior descending (LAD) or non-LAD, LASSO regression achieved AUCs of 0.92 and 0.98 in the internal and external testing datasets, respectively. This LASSO regression model can achieve high accuracy in diagnosing STEMI and LAD vessel disease, thus providing an assisting diagnostic tool based on ECG, which may improve the early diagnosis of STEMI.
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Affiliation(s)
- Lin Wu
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
- Department of Endocrine and Metabolic Diseases, Guangdong Provincial Key Laboratory, The Third Affiliated Hospital of Sun Yat-sen University of Diabetology, No. 600, Tianhe Road, Guangzhou 510630, China
| | - Bin Zhou
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Dinghui Liu
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Linli Wang
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Ximei Zhang
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Li Xu
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Lianxiong Yuan
- Department of Science and Technology, The Third Affiliated Hospital of Sun Yat-sen University, No. 600, Tianhe Road, Guangzhou 510630, China
| | - Hui Zhang
- Department of Medical Ultrasound, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, No. 1, Panfu Road, Guangzhou 510641, China
| | - Yesheng Ling
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Guangyao Shi
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Shiye Ke
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Xuemin He
- Department of Endocrine and Metabolic Diseases, Guangdong Provincial Key Laboratory, The Third Affiliated Hospital of Sun Yat-sen University of Diabetology, No. 600, Tianhe Road, Guangzhou 510630, China
| | - Borui Tian
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Yanming Chen
- Department of Endocrine and Metabolic Diseases, Guangdong Provincial Key Laboratory, The Third Affiliated Hospital of Sun Yat-sen University of Diabetology, No. 600, Tianhe Road, Guangzhou 510630, China
- Correspondence: (Y.C.); (X.Q.); Tel.: +86-1892-210-2818 (Y.C.); +86-1371-926-1500 (X.Q.)
| | - Xiaoxian Qian
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
- Correspondence: (Y.C.); (X.Q.); Tel.: +86-1892-210-2818 (Y.C.); +86-1371-926-1500 (X.Q.)
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12
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Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8413294. [PMID: 35978890 PMCID: PMC9377844 DOI: 10.1155/2022/8413294] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 11/18/2022]
Abstract
The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine’s signals and determine the heart’s health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72
score and 0.93 AUC for 5 superclasses, a 0.46
score and 0.92 AUC for 20 subclasses, and a 0.31
score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset.
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13
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Irfan S, Anjum N, Althobaiti T, Alotaibi AA, Siddiqui AB, Ramzan N. Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155606. [PMID: 35957162 PMCID: PMC9370835 DOI: 10.3390/s22155606] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 05/25/2023]
Abstract
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.
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Affiliation(s)
- Saad Irfan
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (S.I.); (A.B.S.)
| | - Nadeem Anjum
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (S.I.); (A.B.S.)
| | - Turke Althobaiti
- Faculty of Science, Northern Border University, Arar 1321, Saudi Arabia;
| | | | - Abdul Basit Siddiqui
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (S.I.); (A.B.S.)
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK;
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14
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Qiu L, Cai W, Zhang M, Dong Y, Zhu W, Wang L. Supraventricular ectopic beats and ventricular ectopic beats detection based on improved U-net. Physiol Meas 2022; 43. [PMID: 35472766 DOI: 10.1088/1361-6579/ac6aa2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Supraventricular ectopic beats (SVEB) or ventricular ectopic beats (VEB) are common arrhythmia with uncertain occurrence and morphological diversity, so realizing their automatic localization is of great significance in clinical diagnosis. METHODS We propose a modified U-net network: USV-net, it can simultaneously realize the automatic positioning of VEB and SVEB. The improvement consists of three parts: Firstly, we reconstruct part of the convolutional layer in U-net using group convolution to reduce the expression of redundant features. Secondly, a plug-and-play multi-scale 2D deformable convolution (MSDC) module is designed to extract cross-channel features of different scales. Thirdly, in addition to conventional output of U-net, we also compress and output the bottom feature map of U-net, the dual-output is trained through Dice-loss to take into account the learning of high/low resolution features of the model. We used the MIT-BIH arrhythmia database for patient-specific training, and used Sensitivity, Positive prediction rate and F1-scores to evaluate the effectiveness of our method. MAIN RESULT The F1-scores of SVEB and VEB achieve the best results compared with other studies in different testing records. It is worth noting that the F1-scores of SVEB and VEB reached 81.3 and 95.4 in the 24 testing records. Moreover, our method is also at the forefront in Sensitivity and Positive prediction rate. SIGNIFICANCE The method proposed in this paper has great potential in the detection of SVEB and VEB. We anticipate efficiency and accuracy of clinical detection of ectopic beats would be improved.
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Affiliation(s)
- Lishen Qiu
- School of Biomedical Engineering (Suzhou),Division of Life Sciences and medicine, University of Science and Technology of China, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, suzhou, 230026, CHINA
| | - Wenqiang Cai
- Soochow University, No. 333 Ganjiang East Road, Gusu District, Suzhou City, Suzhou, 215000, CHINA
| | - Miao Zhang
- , SIBET, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Suzhou, 215000, CHINA
| | - Yanfang Dong
- School of Biomedical Engineering (suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Hefei, 215000, CHINA
| | - Wenliang Zhu
- , SIBET, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Suzhou, 215000, CHINA
| | - Lirong Wang
- Soochow University, No. 333 Ganjiang East Road, Gusu District, Suzhou City, Suzhou, 215006, CHINA
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Caesarendra W, Hishamuddin TA, Lai DTC, Husaini A, Nurhasanah L, Glowacz A, Alfarisy GAF. An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction. Diagnostics (Basel) 2022; 12:diagnostics12040795. [PMID: 35453842 PMCID: PMC9033157 DOI: 10.3390/diagnostics12040795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/20/2022] [Accepted: 03/20/2022] [Indexed: 01/27/2023] Open
Abstract
This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.
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Affiliation(s)
- Wahyu Caesarendra
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei;
- Correspondence:
| | - Taufiq Aiman Hishamuddin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei;
| | - Daphne Teck Ching Lai
- Institute of Applied Data Analytics, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei;
| | - Asmah Husaini
- Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei;
| | - Lisa Nurhasanah
- Physical Medicine and Rehabilitation Department, Faculty of Medicine, Diponegoro University, Semarang 50275, Indonesia;
| | - Adam Glowacz
- Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland;
| | - Gusti Ahmad Fanshuri Alfarisy
- Department of Informatics, Kalimantan Institute of Technology, Jl. Soekarno Hatta KM. 15, Balikpapan 76127, Indonesia;
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16
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Mathunjwa BM, Lin YT, Lin CH, Abbod MF, Sadrawi M, Shieh JS. ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features. SENSORS 2022; 22:s22041660. [PMID: 35214561 PMCID: PMC8877903 DOI: 10.3390/s22041660] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/10/2022] [Accepted: 02/16/2022] [Indexed: 11/26/2022]
Abstract
In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.
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Affiliation(s)
| | - Yin-Tsong Lin
- AI R&D Department, New Era AI Robotic Inc., Taipei 10571, Taiwan; (Y.-T.L.); (C.-H.L.)
| | - Chien-Hung Lin
- AI R&D Department, New Era AI Robotic Inc., Taipei 10571, Taiwan; (Y.-T.L.); (C.-H.L.)
| | - Maysam F. Abbod
- Department of Electronics and Electrical Engineering, Brunel University London, London UB8 3PH, UK;
| | - Muammar Sadrawi
- Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Jl. Pulomas Barat Kav 88, Jakarta 13210, Indonesia;
| | - Jiann-Shing Shieh
- Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan;
- Correspondence:
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17
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Han J, Sun G, Song X, Zhao J, Zhang J, Mao Y. Detecting ECG abnormalities using an ensemble framework enhanced by Bayesian belief network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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18
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Huang J, Dang F. Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2612074. [PMID: 35070230 PMCID: PMC8769818 DOI: 10.1155/2022/2612074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/21/2021] [Accepted: 12/23/2021] [Indexed: 11/17/2022]
Abstract
This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900-2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61-70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.
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Affiliation(s)
- Jiong Huang
- Emergency Department, Huangyuan County People's Hospital, Xining 812100, China
| | - Fulin Dang
- Huangyuan County People's Hospital, Xining 810000, China
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19
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Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:7677568. [PMID: 35003247 PMCID: PMC8739908 DOI: 10.1155/2021/7677568] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022]
Abstract
Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals' autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.
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20
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Vondrak J, Penhakert M. Statistical Evaluation of Transformation Methods Accuracy on Derived Pathological Vectorcardiographic Leads. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:1900208. [PMID: 35769406 PMCID: PMC9106114 DOI: 10.1109/jtehm.2022.3167009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/02/2022] [Accepted: 04/08/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Jaroslav Vondrak
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
| | - Marek Penhakert
- Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
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21
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Contoyiannis Y, Diakonos FK, Kampitakis M, Potirakis SM. Can high-frequency ECG fluctuations differentiate between healthy and myocardial infarction cases? BIOMEDICAL ENGINEERING ADVANCES 2021. [DOI: 10.1016/j.bea.2021.100011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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22
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Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2254594. [PMID: 34567478 PMCID: PMC8463205 DOI: 10.1155/2021/2254594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 12/11/2022]
Abstract
The aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize it, and the magnetic resonance imaging (MRI) image super-resolution reconstruction model (SRCNN) was established. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were compared between SRCNN and other algorithms. Sixty patients with meniscus injury of knee joint were studied. Arthroscopic surgery was performed according to the patients' actual type of injury, and knee scores were evaluated for all patients. Then, postoperative scores and MRI results were analyzed. The results showed that the PSNR and SSIM values of the SRCNN algorithm were (42.19 ± 4.37) dB and 0.9951, respectively, which were significantly higher than those of other algorithms (P < 0.05). Among patients with meniscus injury, 17 cases (28.33%) were treated with meniscus suture, 39 cases (65.00%) underwent secondary resection, 3 cases (5.00%) underwent partial resection, and 1 case (1.67%) underwent full resection. After meniscus suture, secondary resection, partial resection, and total resection, the knee function scores of patients after treatment were (83.17 ± 8.63), (80.06 ± 7.96), (84.34 ± 7.74), and (85.52 ± 5.97), respectively. There was no great difference in knee function scores after different methods of treatment (P > 0.05), and there were considerable differences compared with those before treatment (P < 0.01). Compared with the results of arthroscopy, there was no significant difference in the grading of meniscus injury by MRI (P > 0.05). To sum up, the SRCNN algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries. Arthroscopic knee surgery had good results and had great clinical application and promotion value.
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23
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Liu W, Xiao Y, Wang X, Deng F. Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF. SENSORS (BASEL, SWITZERLAND) 2021; 21:5964. [PMID: 34502855 PMCID: PMC8434643 DOI: 10.3390/s21175964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/21/2022]
Abstract
This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N-1), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.
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Affiliation(s)
| | | | | | - Fangming Deng
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China; (W.L.); (Y.X.); (X.W.)
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24
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Mandal S, Roy AH, Mondal P. Automated detection of fibrillations and flutters based on fused feature set and ANFIS classifier. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Rieg T, Frick J, Baumgartl H, Buettner R. Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms. PLoS One 2020; 15:e0243615. [PMID: 33332440 PMCID: PMC7746264 DOI: 10.1371/journal.pone.0243615] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/24/2020] [Indexed: 11/18/2022] Open
Abstract
We present the results from a white-box machine learning approach to detect cardiac arrhythmias using electrocardiographic data. A C5.0 is trained to recognize four classes using common features. The four classes are (i) atrial fibrillation and atrial flutter, (ii) tachycardias (iii), sinus bradycardia and (iv) sinus rhythm. Data from 10,646 subjects, 83% of whom have at least one arrhythmia and 17% of whom exhibit a normal sinus rhythm, are used. The C5.0 is trained using 10-fold cross-validation and is able to achieve a balanced accuracy of 95.35%. By using the white-box machine learning approach, a clear and comprehensible tree structure can be revealed, which has selected the 5 most important features from a total of 24 features. These 5 features are ventricular rate, RR-Interval variation, atrial rate, age and difference between longest and shortest RR-Interval. The combination of ventricular rate, RR-Interval variation and atrial rate is especially relevant to achieve classification accuracy, which can be disclosed through the tree. The tree assigns unique values to distinguish the classes. These findings could be applied in medicine in the future. It can be shown that a white-box machine learning approach can reveal granular structures, thus confirming known linear relationships and also revealing nonlinear relationships. To highlight the strength of the C5.0 with respect to this structural revelation, the results of further white-box machine learning and black-box machine learning algorithms are presented.
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Affiliation(s)
- Thilo Rieg
- Machine Learning Research Group, Aalen University, Aalen, Germany
| | - Janek Frick
- Machine Learning Research Group, Aalen University, Aalen, Germany
| | | | - Ricardo Buettner
- Machine Learning Research Group, Aalen University, Aalen, Germany
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26
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Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology. Curr Cardiol Rep 2020; 22:161. [PMID: 33037949 DOI: 10.1007/s11886-020-01416-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography. RECENT FINDINGS ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.
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27
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Liang Y, Yin S, Tang Q, Zheng Z, Elgendi M, Chen Z. Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals. Front Physiol 2020; 11:569050. [PMID: 33117191 PMCID: PMC7566908 DOI: 10.3389/fphys.2020.569050] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/09/2020] [Indexed: 11/27/2022] Open
Abstract
Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.
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Affiliation(s)
- Yongbo Liang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Shimin Yin
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Qunfeng Tang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.,School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Zhenyu Zheng
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.,British Columbia Children's and Women's Hospital, Vancouver, BC, Canada
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
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28
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Pradhan BK, Pal K. Statistical and entropy-based features can efficiently detect the short-term effect of caffeinated coffee on the cardiac physiology. Med Hypotheses 2020; 145:110323. [PMID: 33032176 DOI: 10.1016/j.mehy.2020.110323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/19/2020] [Accepted: 09/26/2020] [Indexed: 11/18/2022]
Abstract
An electrocardiograph (ECG) is the most effective way to find the changes in cardiac physiology. It is the representation of the electrical activities of the heart and can be understood using different waves, peaks, and intervals. Several factors affect the functionality of the heart that includes lifestyle, stress, daily diet, etc. Coffee, the most widely consumed beverage in the world, is an integral part of everyday life. Caffeine, the prime constituent of coffee, is believed to affect the heart physiology. However, the effect of consumption of caffeinated coffee on the cardiac electrophysiological changes, estimated from the morphological features (e.g., peaks, waves, intervals), is controversial. This has led to the exploration of other feature extraction methods to detect the changes accurately. In recent years, the statistical and entropy-based features have emerged as an efficient method to extract hidden patterns from the ECG signal. These features have been successfully explored in arrhythmia detection, noise removal, biometric identification, etc. Hence, we hypothesized that the statistical and entropy-based features could be efficiently used in detecting the changes in the ECG signal after coffee consumption. For the evaluation of our hypothesis, 5-sec ECG segments were extracted from the recorded ECG signals from 14 volunteers in pre- and post-coffee consumption conditions. From each segment, the statistical and entropy-based features were computed. Then, the statistically significant features were extracted using Wilcoxon's signed-rank test. The results showed a significant difference in the statistical parameters post-consumption of coffee. Further, to validate our findings, several machine learning models were used for the automatic detection of these changes, and the results show the highest classification accuracy of 75%. The results support our hypothesis that the statistical and entropy-based features can efficiently detect the changes in the ECG signals, which is induced by coffee consumption. The findings of the proposed hypothesis may open up a new research arena of detecting the presence of different drugs and alcohol in the human body by analyzing the ECG signals.
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Affiliation(s)
- Bikash K Pradhan
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, India
| | - Kunal Pal
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, India.
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29
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Deep Learning-Based Approach for Atrial Fibrillation Detection. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7313287 DOI: 10.1007/978-3-030-51517-1_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhythm that can lead to heart-related complications. Remarkable interest has been given to ECG signals analysis for AF detection in an early stage. In this context, we propose an artificial neural network ANN application to classify ECG signals into three classes, the first presents Normal Sinus Rhythm NSR, the second depicts abnormal signal with Atrial Fibrillation (AF) and the third shows noisy ECG signals. Accordingly, we achieve 93.1% accuracy classification results, 95.1% of sensitivity, 90.5% of specificity and 98%. Furthermore, we yield a value of zero error and a low value of cross entropy, which prove the robustness of the proposed ANN model architecture. Thus, we outperform the state of the art by achieving high accuracy classification without pre-processing step and without high level of feature extraction, and then we enable clinicians to determine automatically the class of each patient ECG signal.
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Baydoun M, Safatly L, Abou Hassan OK, Ghaziri H, El Hajj A, Isma'eel H. High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:1900808. [PMID: 32166049 PMCID: PMC6876931 DOI: 10.1109/jtehm.2019.2949784] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/18/2019] [Accepted: 10/22/2019] [Indexed: 02/01/2023]
Abstract
Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of
heart diseases. However, most patterns of diseases are based on old datasets and stepwise
algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be
done by applying machine learning algorithms. This requires taking existing scanned or
printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time
(milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and
algorithm that converts a printed or scanned format of the ECG into a digitized ECG
signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing
method is first implemented for detecting the ECG regions of interest and extracting the
ECG signals. It is followed by serial steps that digitize and validate the results.
Results: The validation demonstrates very high correlation values of several standard ECG
parameters: PR interval 0.984 +/−0.021 (p-value < 0.001), QRS
interval 1+/− SD (p-value < 0.001), QT interval 0.981
+/− 0.023 p-value < 0.001, and RR interval 1 +/− 0.001
p-value < 0.001. Conclusion: Digitized ECG signals from existing paper or scanned
ECGs can be obtained with more than 95% of precision. This makes it possible to
utilize historic ECG signals in machine learning algorithms to identify patterns of heart
diseases and aid in the diagnostic and prognostic evaluation of patients with
cardiovascular disease.
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Affiliation(s)
| | - Lise Safatly
- 2Electrical and Computer Engineering DepartmentAmerican University of BeirutBeirutLebanon
| | | | - Hassan Ghaziri
- 1Beirut Research and Innovation CenterBeirut2052 6703Lebanon
| | - Ali El Hajj
- 2Electrical and Computer Engineering DepartmentAmerican University of BeirutBeirutLebanon
| | - Hussain Isma'eel
- 3Internal Medicine DepartmentAmerican University of BeirutBeirutLebanon
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Vylala A, Plakkottu Radhakrishnan B. Spectral feature and optimization- based actor-critic neural network for arrhythmia classification using ECG signal. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1652355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Anoop Vylala
- ECE Department, Jyothi Engineering College, Cheruthuruthy, Thrissur, India
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