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Sun Q, Xu Z, Liang C, Zhang F, Li J, Liu R, Chen T, Ji B, Chen Y, Wang C. A dynamic learning-based ECG feature extraction method for myocardial infarction detection. Physiol Meas 2023; 43. [PMID: 36595315 DOI: 10.1088/1361-6579/acaa1a] [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: 08/09/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.
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Affiliation(s)
- Qinghua Sun
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Zhanfei Xu
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Chunmiao Liang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Fukai Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Jiali Li
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Rugang Liu
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Tianrui Chen
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Yuguo Chen
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Cong Wang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
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Borràs M, Chamorro-Servent J. Electrocardiographic Imaging: A Comparison of Iterative Solvers. Front Physiol 2021; 12:620250. [PMID: 33613311 PMCID: PMC7886787 DOI: 10.3389/fphys.2021.620250] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/08/2021] [Indexed: 11/13/2022] Open
Abstract
Cardiac disease is a leading cause of morbidity and mortality in developed countries. Currently, non-invasive techniques that can identify patients at risk and provide accurate diagnosis and ablation guidance therapy are under development. One of these is electrocardiographic imaging (ECGI). In ECGI, the first step is to formulate a forward problem that relates the unknown potential sources on the cardiac surface to the measured body surface potentials. Then, the unknown potential sources on the cardiac surface are reconstructed through the solution of an inverse problem. Unfortunately, ECGI still lacks accuracy due to the underlying inverse problem being ill-posed, and this consequently imposes limitations on the understanding and treatment of many cardiac diseases. Therefore, it is necessary to improve the solution of the inverse problem. In this work, we transfer and adapt four inverse problem methods to the ECGI setting: algebraic reconstruction technique (ART), random ART, ART Split Bregman (ART-SB) and range restricted generalized minimal residual (RRGMRES) method. We test all these methods with data from the Experimental Data and Geometric Analysis Repository (EDGAR) and compare their solution with the recorded epicardial potentials provided by EDGAR and a generalized minimal residual (GMRES) iterative method computed solution. Activation maps are also computed and compared. The results show that ART achieved the most stable solutions and, for some datasets, returned the best reconstruction. Differences between the solutions derived from ART and random ART are almost negligible, and the accuracy of their solutions is followed by RRGMRES, ART-SB and finally the GMRES (which returned the worst reconstructions). The RRGMRES method provided the best reconstruction for some datasets but appeared to be less stable than ART when comparing different datasets. In conclusion, we show that the proposed methods (ART, random ART, and RRGMRES) improve the GMRES solution, which has been suggested as inverse problem solution for ECGI.
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Affiliation(s)
- Marta Borràs
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Judit Chamorro-Servent
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Beadle R, McDonnell D, Ghasemi Roudsari S, Unitt L, Parker S, Varcoe BTH. Assessing heart disease using a novel magnetocardiography device. Biomed Phys Eng Express 2021; 7. [PMID: 33578399 DOI: 10.1088/2057-1976/abe5c5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 02/12/2021] [Indexed: 11/12/2022]
Abstract
The aim of this paper is to present the use of a portable, unshielded magnetocardiograph (MCG) and identify key characteristics of MCG scans that could be used in future studies to identify parameters that are sensitive to cardiac pathology. We recruited 50 patients with confirmed myocardial infarction (MI) within the past 12 weeks and 46 volunteers with no history of cardiac disease. A set of 38 parameters were extracted from MCG features including both signals from the sensor array and from magnetic images obtained from the device and principal component analysis was used to concentrate the information contained in these parameters into uncorrelated predictors. Linear fits of these parameters were then used to examine the ability of MCG to distinguish between sub-groups of patients. In the fist instance, the primary aim of this study was to ensure that MCG has a basic ability to separate a highly polarised patient group (young controls from post infarction patients) and to identify parameters that could be used in future studies to build a formal diagnostic tool kit. Parameters that parameterised left ventricular ejection fraction (LVEF) were identified and an example is presented to show differential low and high ejection fractions.
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Affiliation(s)
- Roger Beadle
- Department of Cardiology, South Warwickshire NHS Foundation Trust, Lakin Road Warwick CV34 5BW, Warwick, Warwickshire, CV34 5BW, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Donna McDonnell
- Department of Cardiology, South Warwickshire NHS Foundation Trust, Lakin Road Warwick CV34 5BW, Warwick, Warwickshire, CV34 5BW, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Shima Ghasemi Roudsari
- Creavo Medical Technologies, Westwood Way Westwood Business Park, Coventry, CV4 8HS, Coventry, CV4 8HS, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Lynda Unitt
- Creavo Medical Technologies, Westwood Way Westwood Business Park, Coventry, CV4 8HS, Coventry, CV4 8HS, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Steve Parker
- Creavo Medical Technologies, Westwood Way Westwood Business Park, Coventry, CV4 8HS, Coventry, CV4 8HS, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Benjamin T H Varcoe
- School of Physics and Astronomy, University of Leeds, Woodhouse Lane, Leeds, West Yorkshire, LS2 9JT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Ansari S, Farzaneh N, Duda M, Horan K, Andersson HB, Goldberger ZD, Nallamothu BK, Najarian K. A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records. IEEE Rev Biomed Eng 2017; 10:264-298. [PMID: 29035225 DOI: 10.1109/rbme.2017.2757953] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors-information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.
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