Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram

Kojuri, Javad; Boostani, Reza; Dehghani, Pooyan; Nowroozipour, Farzad; Saki, Nasrin

doi:10.5530/jcdr.2015.2.2

Cited by 46 publications

(20 citation statements)

References 27 publications

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“…Our results show some similar features between unstable angina and ischemic ECG yet 15,19,20 . Our results using direct waveform input are consistent with these previous studies.…”

Section: Clinical Interpretationsupporting

confidence: 77%

Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning

Wang

Huang

et al. 2020

Anesthesia &Amp; Analgesia

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Background: Cardiovascular waveforms contain information for clinical diagnosis. By "learning" and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimensional structure and display it as a novel three-dimensional (3D) image. We hypothesize that the shape of this structure conveys clinically relevant inner dynamics information. Methods:To validate this hypothesis, we investigate the electrocardiography (ECG) waveform for ischemic heart disease and arterial blood pressure (ABP) waveform in dynamic vasoactive episodes. We model each beat or pulse to be a point lying on a manifold, like a surface, and use the diffusion map (DMap) to establish the relationship among those pulses. The output of the DMap is converted to a 3D image for visualization. For ECG datasets, first we analyzed the non-ST-elevation ECG waveform distribution from unstable angina to healthy control in the 3D image, and we investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG waveform changes. For ABP datasets, we analyzed waveforms collected under endotracheal intubation and administration of vasodilator. To quantify the dynamic separation, we applied the support vector machine (SVM) analysis and reported the total accuracy and macro F1 score. We further carried out the trajectory analysis and derived the moving direction of successive beats (or pulses) as vectors in the high-dimensional space.Results: For the non-ST-elevation ECG, a hierarchical tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented in the 3D image (accuracy = 97.6%, macro-F1=96.1%). The DMap helps quantify and visualize the evolving direction of intraoperative ST-elevation myocardial episode in a 1-hour period (accuracy=97.58%, macro-F1=96.06%). The ABP waveform analysis of Nicardipine administration shows inter-individual difference (accuracy=95.01%, macro-F1=96.9%) and their common directions from intra-individual moving trajectories. The dynamic change of the ABP waveform during endotracheal intubation shows a loop-like trajectory structure, which can be further divided using the manifold learning knowledge obtained from Nicardipine. Conclusions:The DMap and the generated 3D image of ECG or ABP waveforms provides clinically relevant inner dynamics information. It provides clues of acute coronary syndrome diagnosis, shows clinical course in myocardial ischemic episode and reveals underneath physiological mechanism under stress or vasodilators.Keywords: manifold learning, diffusion map, electrocardiography, myocardial ischemia, arterial 10,583 ECG beats, including clinical unstable angina (red), ischemic ECG pattern (green), and healthy control (blue). Video F3CCorresponding to Figure 3 panel C, it shows the 3D animation of 1-min ECG beats during the intra-operative ST-elevation event. Video F3DCorresponding to Figure 3 panel D, it shows the 3D animation of 4,299 consecutive ECG 31 beats from the IS...

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“…Our results show some similar features between unstable angina and ischemic ECG yet 15,19,20 . Our results using direct waveform input are consistent with these previous studies.…”

Section: Clinical Interpretationsupporting

confidence: 77%

Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning

Wang

Huang

et al. 2020

Anesthesia &Amp; Analgesia

View full text Add to dashboard Cite

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“…LS-SVM [ 43 ] utilized lead II datasets that include patients diagnosed with MI and healthy people only, excluding other clinical datasets from the Physionet MIT-BIH [ 40 ] Physikalisch-Technische Bundesanstalt database. Multi-layer perceptron neural networks (MLP-NN) [ 44 ] utilizes a software program offered by an electrocardiograph for annotated waveforms, a clinical survey, and a genetic algorithm for network training. The experimental results for [ 44 ] show that the evaluation accuracy for patients with mention of MI in clinical records is 96%, while that for patients without any mention of MI in the clinical records is 84.5%.…”

Section: Resultsmentioning

confidence: 99%

“…Multi-layer perceptron neural networks (MLP-NN) [ 44 ] utilizes a software program offered by an electrocardiograph for annotated waveforms, a clinical survey, and a genetic algorithm for network training. The experimental results for [ 44 ] show that the evaluation accuracy for patients with mention of MI in clinical records is 96%, while that for patients without any mention of MI in the clinical records is 84.5%.…”

Section: Resultsmentioning

confidence: 99%

Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning

Park

Hwang

Shin

et al. 2020

BMC Med Inform Decis Mak

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Background: The Elliot wave principle commonly characterizes the impulsive and corrective wave trends for both financial market trends and electrocardiograms. The impulsive wave trends of electrocardiograms can annotate several wave components of heartbeats including pathological heartbeat waveforms. The stopping time inquires which ordinal element satisfies the assumed mathematical condition within a numerical set. The proposed work constitutes several algorithmic states in reinforcement learning from the stopping time decision, which determines the impulsive wave trends. Each proposed algorithmic state is applicable to any relevant algorithmic state in reinforcement learning with fully numerical explanations. Because commercial electrocardiographs still misinterpret myocardial infarctions from extraordinary electrocardiograms, a novel algorithm needs to be developed to evaluate myocardial infarctions. Moreover, differential diagnosis for right ventricle infarction is required to contraindicate a medication such as nitroglycerin. Methods: The proposed work implements the stopping time theory to impulsive wave trend distribution. The searching process of the stopping time theory is equivalent to the actions toward algorithmic states in reinforcement learning. The state value from each algorithmic state represents the numerically deterministic annotated results from the impulsive wave trend distribution. The shape of the impulsive waveform is evaluated from the interoperable algorithmic states via least-firstpower approximation and approximate entropy. The annotated electrocardiograms from the impulsive wave trend distribution utilize a structure of neural networks to approximate the isoelectric baseline amplitude value of the electrocardiograms, and detect the conditions of myocardial infarction. The annotated results from the impulsive wave trend distribution consist of another reinforcement learning environment for the evaluation of impulsive waveform direction. Results: The accuracy to discern myocardial infarction was found to be 99.2754% for the data from the comma-separated value format files, and 99.3579% for those containing representative beats. The clinical dataset included 276 electrocardiograms from the comma-separated value files and 623 representative beats.

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“…There are several studies utilizing the AI approach to develop predictive models for MI. [24][25][26][27][28][29][30][31][32] Recently, a group developed an ANN model to predict non-ST elevation myocardial infarction (NSTEMI) patients. 24 The model was trained for several risk attributes such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, aspartate aminotransferase, alanine aminotransferase, and cardiac troponin that are independently associated with stable NSTEMI.…”

Section: Ai Techniques and ML Algorithms For Prediction Of Myocardialmentioning

confidence: 99%

“…Deep learning has also been successfully applied to diagnosis and treatment of MI based on analysis of ECG images. [26][27][28] A group developed an ECG-based mortality prediction model using deep learning algorithms in identifying critically ill patients that could guide decision making. 27 They extracted predictor variables from ECG reports using text mining.…”

Section: Ai Techniques and ML Algorithms For Prediction Of Myocardialmentioning

confidence: 99%

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

Mishra

Ashraf

2019

Semin Thromb Hemost

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Thrombosis development in either arterial or venous system remains a major cause of death and disability worldwide. This poorly controlled in vivo clotting could result in many severe complications including myocardial infarction, venous thromboembolism, stroke, and cerebral venous thrombosis, to name a few. These conditions are collectively known as thromboembolic disorders (TEDs). Appropriate understanding of TEDs is challenging, as they are multifactorial and involve several and often different risk factors. Hence, it requires a collective effort and data from numerous research studies to fully comprehend molecular mechanisms for prediction, prevention, treatment, and overall management of these conditions. To accomplish this arduous feat, a comprehensive approach is required that can compile thousands of available experimental data and transform these into more applicable and purposeful findings. Thus, large datasets could be utilized to generate models that could be predictive of how an individual would respond when subjected to any kind of additional risk factors or surgery, hospitalization, etc., or in the presence of some susceptible genetic variations. Artificial intelligence-based methods harness the capabilities of computer software to imitate human behaviors such as language translation, visual perception, and, most importantly, decision making. These emerging tools, if appropriately explored, might assist in processing of large data and tackle the complexities of identifying novel or interesting pathways that could otherwise be hidden due to their enormity. This narrative review attempts to compile the applications of various subfields of artificial intelligence and machine learning in the context of thrombosis research to date. It further reflects on the potential of artificial intelligence in transforming enormous research data into translational application in the form of predictive computational models.

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Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram

Cited by 46 publications

References 27 publications

Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning

Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning

Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

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