Detection Of Myocardial Infarction Research Articles

Abstract 4137448: Systemic Heparin Administration Does Not Inhibit microRNA Amplification in Patients with Myocardial Infarction

Purpose: MicroRNAs (miRs) have been proposed as biomarkers of myocardial infarction (MI), but prior evidence has demonstrated that heparin, commonly administered to patients presenting with MI, inhibits amplification during quantitative PCR (qPCR). The purpose of this study was to determine whether qPCR amplification of miRs is reduced in plasma of patients receiving heparin and if heparinase reverses this effect. Methods: Blood samples were collected and plasma recovered from patients with normal coronary arteries who had undergone CT coronary angiography ( N=5 ) or who had received intravenous heparin at the time of invasive coronary angiography( N=8 ). Blood was also collected at the time of invasive coronary angiography from patients presenting with ST elevation MI ( N=9 ). RT-qPCR was performed to quantify levels of 84 cardiac miRs, and Cel-miR-39-3p was utilized as a spike-in control. MiR qPCR was also performed following the addition of heparinase to plasma from 2 patients without MI treated with heparin and 2 patients presenting with MI who had received heparin. Results: Mean age of patients was 57 years, and 55% of patients were male. As compared with control patients, the cycle number for cel-miR-39-3p was significantly increased for patients receiving heparin (Figure 1a). No difference was seen in levels of cel-39-3p in heparin-treated patients with respect to the presence or absence of MI. Cel-miR-39-3p cycle times decreased when heparinase was added to samples from heparin-treated patients (Figure 1b). In contrast, heparin did not increase cycle times in any of the cardiac miRs tested, and cycle times decreased even further for miRs 499a-5p and 208b-3p in patients with MI (Figure 2). Conclusions: Systemic heparin treatment inhibits qPCR amplification of cel-mir-39-3p but does not inhibit endogenous miR amplification under these conditions. MiRs 499a-5p and 208b-3p are potentially important candidates for MI detection and quantification.

Abstract 4134729: Quantitative, time-efficient viability cardiac magnetic resonance with delayed-phase dynamic contrast enhancement model: a pilot study in dogs with myocardial infarction

Introduction: Late gadolinium enhancement (LGE) is the gold standard for myocardial viability imaging, recommended by all major cardiology societies. In this study, we aimed to develop a time-efficient viability imaging technique utilizing a dynamic contrast enhancement(dDCE) model to shorten the LGE wait time, provide quantitative measures of the contrast washout procedure, and potentially enable a fast and quantitative mean for scar imaging. Methods: A canine study(n=10) was conducted on reperfused myocardial infarction(MI). A dDCE model using dynamic post-contrast T1 maps was adopted to depict the contrast washout process. dDCE maps were reconstructed using the whole dataset(dDCE 30min ) and a 5-minute subset of the T1 maps(dDCE 5min ). To test the dDCE models for shortening the LGE wait time, LGE dDCE images were synthesized using the dDCE 5min maps and compared to the clinical LGE images. Remote and MI dDCE parameters on dDCE 30min and dDCE 5min maps were compared to test the model's ability to extract physiological features. Results: dDCE 30min and dDCE 5min map showed a good visuospatial difference between remote and MI(Fig. 1A) and no quantitative difference (Fig.1B). v e and PS showed a significant elevation in MI than in remote (61.12±13.65% vs 13.43±5.00%, P =0.018; 44.62±21.40mL/g/min vs 0.42±0.55mL/g/min, P =0.018, respectively). v p and F p were significantly decreased in MI than in remote (4.17±2.06% vs 10.85±3.77%, P =0.02; 1.11±0.32mL/g/min vs 1.51±0.42mL/g/min, P =0.04, respectively). Representative LGE and LGE dDCE images showed high agreement in the presence of MI (Fig. 2A). Bland-Altman analysis showed good agreement between LGE and LGE dDCE images for the measurement of infarct area (bias, -1.74 ± 6.60 %, Fig. 2B) and transmuraltiy (bias, 1.86 ± 2.73 %, Fig. 2C). The simple liner regression showed strong correlations between LGE and LGE dDCE images for the infarct area (R 2 , 0.95; slope, 0.93, P <0.01; Fig. 2D) and transmurality (R 2 , 0.97; slope, 0.93, P <0.01; Fig. 2E). ROC analysis showed that the AUC was 0.97 (95% CI: 0.94 to 1.00) (Fig. 2F). Increased lesion-blood pool contrast by 10 folds on dDCE images improved subendocardial MI detection (Fig.3). Conclusions: The developed dDCE model provides comparable viability assessment ability to the standard LGE images without the prolonged wait time and reflects MI pathophysiological changes. It shows the potential for a rapid and quantitative myocardial viability imaging protocol using cardiac magnetic resonance.

Characterization of dynamic changes in cardiac microstructure after reperfused ST-elevation myocardial infarction by biphasic diffusion tensor cardiovascular magnetic resonance.

Microstructural disturbances underlie dysfunctional contraction and adverse left ventricular (LV) remodelling after ST-elevation myocardial infarction (STEMI). Biphasic diffusion tensor cardiovascular magnetic resonance (DT-CMR) quantifies dynamic reorientation of sheetlets (E2A) from diastole to systole during myocardial thickening, and markers of tissue integrity [mean diffusivity (MD) and fractional anisotropy (FA)]. This study investigated whether microstructural alterations identified by biphasic DT-CMR: (i) enable contrast-free detection of acute myocardial infarction (MI); (ii) associate with severity of myocardial injury and contractile dysfunction; and (iii) predict adverse LV remodelling. Biphasic DT-CMR was acquired 4 days (n = 70) and 4 months (n = 66) after reperfused STEMI and in healthy volunteers (HVOLs) (n = 22). Adverse LV remodelling was defined as an increase in LV end-diastolic volume ≥ 20% at 4 months. MD and FA maps were compared with late gadolinium enhancement images. Widespread microstructural disturbances were detected post-STEMI. In the acute MI zone, diastolic E2A was raised and systolic E2A reduced, resulting in reduced E2A mobility (all P < .001 vs. adjacent and remote zones and HVOLs). Acute global E2A mobility was the only independent predictor of adverse LV remodelling (odds ratio .77; 95% confidence interval .63-.94; P = .010). MD and FA maps had excellent sensitivity and specificity (all > 90%) and interobserver agreement for detecting MI presence and location. Biphasic DT-CMR identifies microstructural alterations in both diastole and systole after STEMI, enabling detection of MI presence and location as well as predicting adverse LV remodelling. DT-CMR has potential to provide a single contrast-free modality for MI detection and prognostication of patients after acute STEMI.

IOT BASED ECG: HYBRID CNN-BILSTM APPROACH FOR MYOCARDIAL INFARCTION CLASSIFICATION

Cardiovascular disease such as ischemic heart disease and stroke are the most dangerous diseases in the WHO stats. Myocardial Infarction (MI), an ischemic disease of the heart, occurs due to a sudden blockage in the coronary arteries that supply blood to the heart causing a lack of oxygen and nutrients. The MI patient needs continuous monitoring using electrocardiography, the latter is always at risk of developing complications such as arrhythmias. As a solution, we proposed an internet of things (IoT) based ECG system for monitoring, the application layer was reserved for the detection of MI and arrhythmias using artificial intelligence so that the patients can keep being monitored even outside health facilities. For this purpose, this paper proposed a hybrid Convolutional Neural Network (CNN) – Bidirectional Long Short-Term Memory (BiLSTM) approach to classify ECG signals and evaluates its performance by using raw and preprocessed data, and comparing the results to related studies. Two datasets have been used in this classification. The results were promising, the model has scored 99.00% accuracy on raw data classifying 4 classes, and 99.73% accuracy on a larger preprocessed data for 3 classes classification. The proposed model is suitable to serve in our monitoring task.

Enhancing Myocardial Infarction Diagnosis: LSTM-based Deep Learning Approach Integrating Echocardiographic Wall Motion Analysis

PurposeOwing to the increased mortality of heart diseases worldwide, especially myocardial infarction (MI), early detection is essential for improved diagnosis and treatment. The main purpose of this study is to develop a myocardial infarction detection method that combines deep learning and image processing, focusing on abnormalities in left ventricular (LV) wall motion.MethodsThe proposed method primarily uses the LV wall motion movement as a feature to train an LSTM network for MI detection. LV wall motion annotated by expert cardiologists was used as the ground truth. Accuracy, sensitivity, specificity, and area under the curve (AUC) were used to evaluate model performance. The proposed method primarily uses LV wall motion as a feature, combined with LV size and image pixels, to improve diagnostic accuracy over existing computer-aided design (CAD) systems.ResultsThe LSTM model achieved the highest diagnostic performance when trained on a combination of LV wall motion, LV size, and image pixel features with an accuracy of 95%, sensitivity of 96%, specificity of 94%, and an AUC value of 0.98. The LSTM model significantly outperformed models trained on individual feature sets or conventional machine learning algorithms. The inclusion of LV wall motion analysis improved accuracy by 10% compared to using only LV size and pixel data.ConclusionOur MI diagnosis system uses echocardiographic image analysis and LSTM-based deep learning to accurately detect LV wall motion issues related to MI. Compared with current CAD systems, the inclusion of LV wall motion analysis significantly improves diagnosis accuracy. The proposed system can help physicians detect MI early, thereby accelerating treatment and improving patient outcomes.

An interpretable ensemble trees method with joint analysis of static and dynamic features for myocardial infarction detection

Objective. In recent years, artificial intelligence-based electrocardiogram (ECG) methods have been massively applied to myocardial infarction (MI). However, the joint analysis of static and dynamic features to achieve accurate and interpretable MI detection has not been comprehensively addressed. Approach. This paper proposes a simplified ensemble tree method with a joint analysis of static and dynamic features to solve this issue for MI detection. Initially, the dynamic features are extracted by modeling the intrinsic dynamics of ECG via dynamic learning in addition to extracting classical static features. Secondly, a two-stage feature selection strategy is designed to identify a few significant features, which substitute the original variables that are employed in constructing the ensemble tree. This approach enhances the discriminative ability by selecting significant static and dynamic features. Subsequently, this paper presents an interpretable classification method named StackTree by introducing a stacked ensemble scheme to modify the ensemble tree simplification algorithm. The representative rules of the raw ensemble trees are selected as the intermediate training data that is used to retrain a decision tree with performance close to that of the source ensemble model. Using this scheme, the significant precision and interpretability of MI detection are thus comprehensively addressed. Main results. The effectiveness of our method in detecting MI is evaluated using the Physikalisch-Technische Bundesanstalt (PTB) and clinical database. The findings suggest that our algorithm outperforms the traditional methods based on a single type of feature. Additionally, it is comparable to the conventional random forest, achieving 97.1% accuracy under the inter-patient framework on the PTB database. Furthermore, feature subsets trained on PTB are validated using the clinical database, resulting in an accuracy of 84.5%. The chosen important features demonstrate that both static and dynamic information have crucial roles in MI detection. Crucially, the proposed method provides clear internal workings in an easy-to-understand visual manner.

Assessment and Comparison of N-Terminal-Probrain Natriuretic Peptide (NT-proBNP) in Saliva and Serum of Healthy Subjects, Periodontitis Patients, and Periodontitis Patients With Myocardial Infarction.

Introduction Periodontitis is a multifactorial oral disease causing destruction of the periodontium. Systemic diseases can exacerbate periodontal inflammation through immune dysregulation. N-terminal-probrain natriuretic peptide (NT-proBNP) a prohormone, released by myocardial cells is a known biomarker for cardiovascular disease (CVD). Existing literature discloses a bidirectional relationship between periodontitis and CVD. NT-proBNP release can be regulated by mediators of the systemic inflammation. Cardiocyte NT-proBNP release might get stimulated through proinflammatory cytokines. NT-proBNP levels can also be influenced by systemic inflammation in the absence of cardiac dysfunction. Accordingly, we postulated that the inflammation of periodontium could aid in increased levels of NT-proBNP in serum and saliva in participants without cardiovascular disorders. Saliva is said to be the mirror of the body. Assessing NT-pro BNP in saliva allows for a non-invasive method. The present research evaluated the salivary and serum concentrations of NT-proBNP in a healthy group, patients suffering from periodontal disease and periodontal disease along with myocardial infarction (MI). Material and method A total of 90 patients, 30 in each group i.e., healthy group, periodontitis patients and patients suffering from periodontitis with myocardial infarction, were enrolled. The periodontitis patients were selected according to the Classification of Periodontal and Peri-implant Diseases and Conditions 2017. Patients clinically diagnosed with MI by the physician were selected following World Health Organization criteria for detection of MI. Case history was recorded and periodontal parameter analysis like plaque index (PI), gingival index (GI), probing pocket depth (PPD) and clinical attachment loss (CAL) were measured. Salivary and serum samples were collected from the participants after obtaining informed consent. The samples were subjected to human NT-proBNP sandwich type enzyme-linked immunosorbent assay (ELISA) for quantitative evaluation. The obtained data was analysed and compared using ANOVA, Tukey's post hoc test and Pearson's correlation. The p-value<0.05 was considered statistically significant. Result PI and GI were highest in subjects with periodontitis only (p<0.05). Patients suffering from periodontitis with MI exhibited significantly higher PPD and CAL values (p<0.05). Salivary and serum concentrations of NT-proBNP were significantly higher with p-value=0.000 in subjects suffering from periodontitis with MI. The salivary NT-proBNP levels were significantly higher than serum NT-proBNP levels in periodontitis and periodontitis with MI patients. The levels of NT-proBNP in periodontitis along with MI patients were 1.570 pg/mL in serum and 1.694 pg/mL in saliva. Conclusion Salivary NT-proBNP levels were highest in subjects affected from periodontitis along with MI. Elevated salivary NT-proBNP levels can be due to systemic inflammation and cardiovascular stress linking periodontitis to MI. The positive correlation between periodontal parameters and NT-proBNP levels validates the biomarker's role in reflecting the extent of periodontal destruction and its association with cardiovascular stress. Salivary NT-proBNP can be used as a non-invasive diagnostic marker for diagnosing periodontitis and MI. Future research could explore targeted therapies for the shared inflammatory pathways between periodontitis and MI.

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.

Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.

Exosomal miRNA-21-5p and miRNA-21-3p as key biomarkers of myocardial infarction.

Coronary artery disease (CAD) is a debilitating condition that can lead to myocardial infarction (MI). Exosomal miRNAs (exo-miRNA) can be diagnostic biomarkers for detecting MI. Here, we conduct a study to evaluate the efficacy of exo-miRNA-21-5p/3p for early detection of MI. A total of 135 CAD patients and 150 healthy subjects participated in this study. Additionally, we randomly divided 26 male Wistar rats (12 weeks old) into two groups: control and induced MI. Angiographic images were used to identify patients and healthy individuals of all genders. In the following, serum exosomes were obtained, and exo-miRNA-21-5p/3p was measured by reverse-transcriptase polymerase chain reaction. We observed an upregulation of exo-miRNA-21-5p/3p in CAD patient and MI-induced animal groups compared to controls. Analysis of the ROC curves defined 82% and 88% of the participants' exo-miRNA-21-5p and exo-miRNA-21-3p diagnostic power, respectively, which in the animal model was 92 and 82. This study revealed that the mean expression levels of exo-miRNA-21-5p/3p were significantly increased in CAD patients and animal models of induced MI. Also, these results are associated with the atherogenic lipid profile of CAD patients, which may play an important role in the progression of the disease. Therefore, they can be considered as novel biomarkers.

An Improved Myocardial Infarction Detection using Convolutional Neural Network and Graph Neural Network Algorithm

Myocardial infarction (MI) is a crucial health problem and its mortality rate is higher than that of cancer. It is the damage and death of heart muscle from the sudden blockage of a coronary artery by a blood clot. Although lots of researches have been carried out with impressive performance record for detection of MI, however, existing approaches for MI detection can be improved upon for better performance. A vital piece of medical technology that aids in the diagnosis of a number of heart-related disorders in patients is an electrocardiogram (ECG). To find significant episodes in long-term ECG data, an automated diagnostic method is needed. Cardiologists face a very difficult problem when trying to quickly examine long-term ECG records. To pinpoint critical occurrences, a computer-based diagnosing tool is necessary. In this study we employ Convolutional Neural Network (CNN) algorithm with Graph Neural Network (GNN) to select best features and make appropriate classifications. The result of the study gave f1 score of 99.58%, precision of 99.5% and an accuracy of 99.72%. Our proposed model have shown a significant improvement in the detection of MI, this will aid in effectively addressing the challenge of performance drawback in this domain of research.

Explainable AI for myocardial infarction using the vectorcardiogram

Abstract Background and purpose Myocardial infarction (MI), a widespread cardiovascular ailment, often lacks immediate patient awareness being early and accurate MI diagnosis crucial for lifesaving interventions. In artificial intelligence studies, ECG signals are extensively explored, while fewer examine the potential of vectorcardiogram (VCG) which offers a three-dimensional cardiac dipole representation. Traditional Machine Learning methods manually extract features; some studies advocate VCG features. Deep Learning, especially Convolutional Neural Networks (CNN), automates feature extraction. Although ECG contains redundant information from the VCG's 12 projections, no studies directly apply CNNs to VCG, nor assess the impact of redundancy on predictive ability. This study aims to bridge this gap by investigating CNNs' diagnostic capabilities with VCG inputs, specifically focusing on improving MI classification. Methods The PTB-XL database was used for this study, containing 10-seconds ECG data from various pathologies, including MI. The database encompasses 9,481 records from healthy patients and 4,129 records from patients with MI. Data was divided into 80% for training and 20% for testing. Consequently, the dataset was expanded to have 52% representing MI and 48% representing healthy cases. We designed a 1D-CNN classifier (Figure 1.A) with three convolutional layers. The network was trained using both ECG and VCG reconstruction through the Inverse Dower Transform. Results The trained model achieved accuracy values of 0.895 and 0.914 for the test set when utilizing ECG and VCG, respectively. These results underscore how the information condensation in the VCG contributes to an enhanced predictive capacity of the model. In a qualitative analysis, activation maps extracted from the CNN illustrate heightened activation in the QRS complex when the disease is present, contrasting with diminished activation in its absence. These maps depict the weight assigned to each signal segment, enabling the classification of signals into healthy and diseased categories. The presented figure exemplifies a notable activation in the QRS loop in the case of myocardial infarction (MI), aligning with the observed alteration in the cardiac signal associated with this pathology (Figure 1.B). Conclusion In this study, an interpretable CNN has been designed for MI detection, with the VCG yielding better results than the ECG for this task due to information compression. This provides a new insight for enhancing classification capability in Deep Learning models, allowing for advancements in the MI detection.

MT-MV-KDF: A novel Multi-Task Multi-View Knowledge Distillation Framework for myocardial infarction detection and localization

Myocardial infarction (MI) is a prevalent and life-threatening cardiovascular ailment, diagnosed primarily through the twelve-lead electrocardiogram (ECG). These leads provide unique insights into electrical activity across diverse heart regions, crucial for MI differentiation and assessment. However, prior studies predominantly focus on single-perspective 12-lead ECG analysis, overlooking inter-lead disparities. Moreover, prevailing methods often lack versatility, being tailored to specific tasks. Therefore, this paper presents the Multi-Task Multi-View Knowledge Distillation Framework (MT-MV-KDF) for MI detection and localization. The framework combines multi-view learning, multi-task learning, and knowledge distillation to enhance model performance and efficiency. Specifically, The introduction of multi-view learning aims to learn ECG feature representations from different views. Additionally, multi-task learning enables the model to learn the similarities and differences of multiple related tasks simultaneously. Finally, knowledge distillation is used to transfer latent knowledge from the complex teacher model to the simpler student model to improve efficiency. The MT-MV-KDF consists of MT-MVT-net and MT-SVS-net, which use a multi-layer CNN architecture to extract ECG different scale features. An attention module is used to capture inter-channel information and spatial cues. Through knowledge distillation, the MT-DSVS-net (0.40M) inherits insights from MT-MVT-net while achieving comparable performance with fewer parameters. Evaluation on the PTB-XL database demonstrates MT-DSVS-net has an Acc, AUC of 93.78% and 92.23% for MI detection and 83.45% and 78.26% for MI localization, respectively. Additionally, Robustness validation on CPSC2018 and Chapman datasets further confirms the efficacy of MT-MV-KDF. This study highlights the MT-MV-KDF effectiveness in MI detection and localization, particularly in improving inter-patient MI localization.

Detection of myocardial infarction using Shannon energy envelope, FA-MVEMD and deterministic learning

Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

Objective. Myocardial infarction (MI) is a serious cardiovascular disease that can cause irreversible damage to the heart, making early identification and treatment crucial. However, automatic MI detection and localization from an electrocardiogram (ECG) remain challenging. In this study, we propose two models, MFB-SENET and MFB-DMIL, for MI detection and localization, respectively. Approach. The MFB-SENET model is designed to detect MI, while the MFB-DMIL model is designed to localize MI. The MI localization model employs a specialized attention mechanism to integrate multi-instance learning with domain knowledge. This approach incorporates handcrafted features and introduces a new loss function called lead-loss, to improve MI localization. Grad-CAM is employed to visualize the decision-making process. Main Results. The proposed method was evaluated on the PTB and PTB-XL databases. Under the inter-patient scheme, the accuracy of MI detection and localization on the PTB database reached 93.88% and 67.17%, respectively. The accuracy of MI detection and localization on the PTB-XL database were 94.89% and 85.83%, respectively. Significance. Our method achieved comparable or better performance than other state-of-the-art algorithms. The proposed method combined deep learning and medical domain knowledge, demonstrates effectiveness and reliability, holding promise as an efficient MI diagnostic tool to assist physicians in formulating accurate diagnoses.

MI-OPTNET: AN OPTIMIZED DEEP LEARNING FRAMEWORK FOR MYOCARDIAL INFARCTION DETECTION

The conventional means of myocardial infarction (MI) detection using a 12-lead electrocardiogram (ECG) system include a pretrained network and machine learning interpretation of the complex ECG signals. They are computationally inefficient and demand high-performance hardware. Here, for the first time, we introduce an effective framework (MI-OptNet) using the particle swarm optimization model (PSO) in the design of a lightweight hybrid network combining convolutional neural network (CNN)-long short terms memory (LSTM) for MI and normal ECG detection. We optimized important design and training parameters based on limb leads’ signals and identified leads III and VI as the best ECG leads for the task based on their high classification performance ranging between 80 – 90 %, suggesting that they may provide more information about MI than the others. The other strategy of fusing the scores from all models at the decision level achieved the best result with a 10 % increase in the evaluated metrics. Our findings support the flexibility and adaptability of our framework for the design process using minimal computer efforts. We concluded that this approach may be used for other classification problems to assist engineers and designers in efficient decision-making and to solve complex signal classification and recognition problems.

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning

Objective. Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary tool for clinical diagnosis. However, detecting MI accurately through ECG remains challenging due to the complex and subtle pathological ECG changes it causes. To enhance the accuracy of ECG in detecting MI, a more thorough exploration of ECG signals is necessary to extract significant features. Approach. In this paper, we propose an interpretable shapelet-based approach for MI detection using dynamic learning and deep learning. Firstly, the intrinsic dynamics of ECG signals are learned through dynamic learning. Then, a deep neural network is utilized to extract and select shapelets from ECG dynamics, which can capture locally specific ECG changes, and serve as discriminative features for identifying MI patients. Finally, the ensemble model for MI detection is built by integrating shapelets of multi-dimensional ECG dynamic signals. Main results. The performance of the proposed method is evaluated on the public PTB dataset with accuracy, sensitivity, and specificity of 94.11%, 94.97%, and 90.98%. Significance. The shapelets obtained in this study exhibit significant morphological differences between MI and healthy subjects.

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT’s limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.

Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature.

This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06). Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection.

AI-Enhanced ECG diagnosis system for acute myocardial infarction with LBBB: Constant-Q transform and ResNet-50 integration

This study introduces an advanced Electrocardiogram (ECG) diagnostic framework that melds signal processing techniques with deep learning models to significantly boost accuracy in identifying acute myocardial infarction (MI) and MI related to left bundle branch block (LBBB). By merging the Constant-Q Transform (CQT) with a pre-trained model, this system showcases exceptional performance, an impressive 98.99% accuracy and a remarkably low 0.0029% training loss after 100 trained epochs. Rigorous 10-fold cross-validation substantiates and fortifies these findings. This novel approach streamlines the complexities of diagnostics by consolidating 12-lead ECG data and harnessing CQT for precise time-frequency domain analysis. Notably, this methodology not only enhances MI detection accuracy but also presents potential for enhancing healthcare outcomes. It holds promise in minimizing misdiagnoses, thereby propelling advancements in patient care for critical cardiac conditions. This paradigm shift marks a significant stride in ECG-based diagnostic systems, offering far-reaching implications for improved medical practices and patient well-being.

Clinical Utility of Soluble Lectin Type Oxidized Low-Density Lipoprotein Receptor as a Biomarker for Myocardial Infarction and Stable Angina.

Background and objectives Endothelial soluble lectin-type oxidized low-density lipoprotein receptor 1 (sLOX-1) recognizes oxidized low-density lipoprotein (LDL) and triggers downstream signaling leading to atherosclerosis. The objective of this study was to demonstrate the utility of sLOX-1 as a biomarker for detecting acute myocardial infarction (MI) and stable angina (SA) and to develop a diagnostic algorithm for distinguishing coronary vasospasm from coronary plaque rupture. Methods We enrolled 62 patients who underwent diagnostic coronary angiography (CAG) and 30 healthy controls (21 men and nine women) and measured sLOX-1, troponin I, and cardiac myosin-binding protein C (c-MyBPC) using commercial kits. Results Patients with MI exhibited higher sLOX-1 levels (301.55 ± 196.16 pg/ml) than patients with stable angina (220.76 ± 103.65 pg/ml) and healthy controls (121.14 ± 77.10, F: 10.55, p<0.001). Although higher troponin I levels were detected in MI patients (263.00 ± 493.00 vs. 3.19 ± 2.15 ng/ml, p=0.0019), no significant elevation was observed in SA patients (1.91 ± 0.79 ng/ml). Plasma sLOX-1 levels showed a positive association with age (r=0.37, p=0.003), but not with gender (r=0.04, p=0.75). Troponin I showed no association with age (r=0.12, p=0.36) or gender (r=0.06, p=0.62). Receiver operating characteristic (ROC) curves revealed that among the three biomarkers, troponin-I showed a higher area under the curve (AUC) (AUC: 0.941), followed by sLOX-1 (AUC: 0.888), while c-MyBPC showed no clinical utility in the detection of MI (AUC: 0.666). Conclusions A marked elevation of sLOX-1 can detect MI and differentiate the presence or absence of plaque rupture, along with diagnosing stable angina.