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Al-Zaiti S, Martin-Gill C, Zégre-Hemsey J, Bouzid Z, Faramand Z, Alrawashdeh M, Gregg R, Helman S, Riek N, Kraevsky-Phillips K, Clermont G, Akcakaya M, Sereika S, Van Dam P, Smith S, Birnbaum Y, Saba S, Sejdic E, Callaway C. Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact. RESEARCH SQUARE 2023:rs.3.rs-2510930. [PMID: 36778371 PMCID: PMC9915770 DOI: 10.21203/rs.3.rs-2510930/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
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2
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Mahmoudinejad SA, Safdarian N. Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification-Based Feature Selection. JOURNAL OF MEDICAL SIGNALS & SENSORS 2021; 11:79-91. [PMID: 34268096 PMCID: PMC8253315 DOI: 10.4103/jmss.jmss_12_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/12/2020] [Accepted: 07/25/2020] [Indexed: 11/15/2022]
Abstract
Background: Cardiovascular disease (CVD) is the first cause of world death, and myocardial infarction (MI) is one of the five primary disorders of CVDs which the patient electrocardiogram (ECG) analysis plays a dominant role in MI diagnosis. This research aims to evaluate some extracted features of ECG data to diagnose MI. Methods: In this paper, we used the Physikalisch-Technische Bundesanstalt database and extracted some morphological features, such as total integral of ECG, integral of the T-wave section, integral of the QRS complex, and J-point elevation from a cycle of normal and abnormal ECG waveforms. Since the morphology of healthy and abnormal ECG signals is different, we applied integral to different ECG cycles and intervals. We executed 100 of iterations on a 10-fold and 5-fold cross-validation method and calculated the average of statistical parameters to show the performance and stability of four classifiers, namely logistic regression (LR), simple decision tree, weighted K-nearest neighbor, and linear support vector machine. Furthermore, different combinations of proposed features were employed as a feature selection procedure based on classifier's performance using the aforementioned trained classifiers. Results: The results of our proposed method to diagnose MI utilizing all the proposed features with an LR classifier include 90.37%, 94.87%, and 86.44% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of 0.006. Conclusion: Our proposed classification-based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.
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Affiliation(s)
- Seyed Ataddin Mahmoudinejad
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Naser Safdarian
- School of Medicine, Dezful University of Medical Sciences, Dezful, Iran.,Young Researchers and Elite Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran
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3
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Bentes PCL, Nadal J. A telediagnosis assistance system for multiple-lead electrocardiography. Phys Eng Sci Med 2021; 44:473-485. [PMID: 33797700 DOI: 10.1007/s13246-021-00996-2] [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: 09/30/2020] [Accepted: 03/22/2021] [Indexed: 11/28/2022]
Abstract
The diffusion of telemedicine opens-up a new perspective for the development of technologies furthered by Biomedical Engineering. In particular, herein we deal with those related to telediagnosis through multiple-lead electrocardiographic signals. This study focuses on the proof-of-concept of an internet-based telemedicine system as a use case that attests to the feasibility for the development, within the university environment, of techniques for remote processing of biomedical signals for adjustable detection of myocardial ischemia episodes. At each signal lead, QRS complexes are detected and delimited with the J-point marking. The same procedure to detect the complex is used to identify the respective T wave, then the area over the ST segment is applied to detect ischemia-related elevations. The entire system is designed on web-based telemedicine services using multiuser, remote access technologies, and database. The measurements for sensitivity and precision had their respective averages calculated at 11.79 and 24.21% for the leads of lower noise. The evaluations regarding the aspects of user friendliness and the usefulness of the application, resulted in 88.57 and 89.28% of broad or total acceptance, respectively. They are robust enough to enable scalability and can be offered by cloud computing, besides enabling the development of new biomedical signal processing techniques within the concept of distance services, using a modular architecture with collaborative bias.
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Affiliation(s)
| | - Jurandir Nadal
- Instituto Alberto Luiz Coimbra de Pós Graduação e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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4
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Bouzid Z, Faramand Z, Gregg RE, Frisch SO, Martin-Gill C, Saba S, Callaway C, Sejdić E, Al-Zaiti S. In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department. J Am Heart Assoc 2021; 10:e017871. [PMID: 33459029 PMCID: PMC7955430 DOI: 10.1161/jaha.120.017871] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.
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Affiliation(s)
- Zeineb Bouzid
- Department of Electrical & Computer Engineering Swanson School of EngineeringUniversity of Pittsburgh PA
| | - Ziad Faramand
- Department of Acute & Tertiary Care Nursing University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Richard E Gregg
- Advanced Algorithm Research Center Philips Healthcare Andover MA
| | - Stephanie O Frisch
- Department of Biomedical Informatics at School of Medicine University of Pittsburgh PA.,Department of Acute & Tertiary Care Nursing University of Pittsburgh PA
| | - Christian Martin-Gill
- Department of Emergency Medicine University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Samir Saba
- Division of Cardiology University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Clifton Callaway
- Department of Emergency Medicine University of Pittsburgh PA.,University of Pittsburgh Medical Center Pittsburgh PA
| | - Ervin Sejdić
- Department of Electrical & Computer Engineering Swanson School of EngineeringUniversity of Pittsburgh PA.,Department of Bioengineering Swanson School of EngineeringUniversity of Pittsburgh PA.,Department of Biomedical Informatics at School of Medicine University of Pittsburgh PA.,Intelligent Systems Program at School of Computing and Information University of Pittsburgh PA
| | - Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing University of Pittsburgh PA.,Department of Emergency Medicine University of Pittsburgh PA.,Division of Cardiology University of Pittsburgh PA
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5
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Johnston BM, Johnston PR. Differences between models of partial thickness and subendocardial ischaemia in terms of sensitivity analyses of ST-segment epicardial potential distributions. Math Biosci 2019; 318:108273. [PMID: 31647934 DOI: 10.1016/j.mbs.2019.108273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 10/15/2019] [Accepted: 10/15/2019] [Indexed: 01/24/2023]
Abstract
Mathematical modelling is a useful technique to help elucidate the connection between non-transmural ischaemia and ST elevation and depression of the ECG. Generally, models represent non-transmural ischaemia using an ischaemic zone that extends from the endocardium partway to the epicardium. However, recent experimental work has suggested that ischaemia typically arises within the heart wall. This work examines the effect of modelling cardiac ischaemia in the left ventricle using two different models: subendocardial ischaemia and partial thickness ischaemia, representing the first and second scenarios, respectively. We found that it is possible, only in the model of subendocardial ischaemia, to see a single minimum on the epicardial surface above the ischaemic region, and this only occurs for low ischaemic thicknesses. This may help to explain the rarity of ST depression that is located over the ischaemic region. It was also found that, in both models, the epicardial potential distribution is most sensitive to the proximity of the ischaemic region to the epicardium, rather than to the thickness of the ischaemic region. Since proximity does not indicate the thickness of the ischaemic region, this suggests a reason why it may be difficult to determine the degree of ischaemia using the ST segment of the ECG.
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Affiliation(s)
- Barbara M Johnston
- School of Environment and Science, and Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, Queensland 4111, Australia.
| | - Peter R Johnston
- School of Environment and Science, and Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, Queensland 4111, Australia
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6
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Ledezma CA, Zhou X, Rodríguez B, Tan PJ, Díaz-Zuccarini V. A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG. PLoS One 2019; 14:e0220294. [PMID: 31404081 PMCID: PMC6690680 DOI: 10.1371/journal.pone.0220294] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 07/12/2019] [Indexed: 11/19/2022] Open
Abstract
Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the development of these methods is the absence of relevant datasets for their training and validation. Here, the ten Tusscher-Panfilov 2006 model and the O’Hara-Rudy model for human myocytes were used to create two populations of models that were in concordance with data obtained from healthy individuals (control populations) and included inter-subject variability. The effects of ischemia were subsequently included in the control populations to simulate the effects of mild and severe ischemic events on single cells, full ischemic cables of cells and cables of cells with various sizes of ischemic regions. Action potential and pseudo-ECG biomarkers were measured to assess how the evolution of ischemia could be quantified. Finally, two neural network classifiers were trained to identify the different degrees of ischemia using the pseudo-ECG biomarkers. The control populations showed action potential and pseudo-ECG biomarkers within the physiological ranges and the trends in the biomarkers commonly identified in ischemic patients were observed in the ischemic populations. On the one hand, inter-subject variability in the ischemic pseudo-ECGs precluded the detection and classification of early ischemic events using any single biomarker. On the other hand, the neural networks showed sensitivity and positive predictive value above 95%. Additionally, the neural networks revealed that the biomarkers that were relevant for the detection of ischemia were different from those relevant for its classification. This work showed that a computational approach could be used, when data is scarce, to validate proof-of-concept machine learning methods to detect ischemic events.
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Affiliation(s)
- Carlos A. Ledezma
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Xin Zhou
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Blanca Rodríguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - P. J. Tan
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Vanessa Díaz-Zuccarini
- Department of Mechanical Engineering, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Department of Medical Physics and Biomedical Engineering, University College London, W1W 7TS, UK
- * E-mail:
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7
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Burton BM, Aras KK, Good WW, Tate JD, Zenger B, MacLeod RS. A Framework for Image-Based Modeling of Acute Myocardial Ischemia Using Intramurally Recorded Extracellular Potentials. Ann Biomed Eng 2018; 46:1325-1336. [PMID: 29786776 DOI: 10.1007/s10439-018-2048-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 05/10/2018] [Indexed: 10/16/2022]
Abstract
The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endocardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice, providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease-inadvertently leading to errors in ECG-based diagnoses. Furthermore, recent experimental studies have brought into question the subendocardial ischemia paradigm suggesting instead a more distributed pattern of tissue injury. These findings come from experiments and so have both the impact and the limitations of measurements from living organisms. Computer models have often been employed to overcome the constraints of experimental approaches and have a robust history in cardiac simulation. To this end, we have developed a computational simulation framework aimed at elucidating the effects of ischemia on measurable cardiac potentials. To validate our framework, we simulated, visualized, and analyzed 226 experimentally derived acute myocardial ischemic events. Simulation outcomes agreed both qualitatively (feature comparison) and quantitatively (correlation, average error, and significance) with experimentally obtained epicardial measurements, particularly under conditions of elevated ischemic stress. Our simulation framework introduces a novel approach to incorporating subject-specific, geometric models and experimental results that are highly resolved in space and time into computational models. We propose this framework as a means to advance the understanding of the underlying mechanisms of ischemic disease while simultaneously putting in place the computational infrastructure necessary to study and improve ischemia models aimed at reducing diagnostic errors in the clinic.
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Affiliation(s)
- Brett M Burton
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA. .,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA. .,Cardiovascular Research & Training Institute, University of Utah, Salt Lake City, UT, USA.
| | - Kedar K Aras
- George Washington University, Washington, DC, USA
| | - Wilson W Good
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.,Cardiovascular Research & Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Jess D Tate
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.,Cardiovascular Research & Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Brian Zenger
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.,Cardiovascular Research & Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Rob S MacLeod
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.,Cardiovascular Research & Training Institute, University of Utah, Salt Lake City, UT, USA
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