Detection Of Myocardial Infarction Research Articles

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.

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.

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 ability for both cardiac contraction and relaxation on a large U.K. 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 suitability to act as a normality model of 3D cardiac mechanics and capture subpopulation-specific differences between normal subjects and myocardial infarction (MI) patients. Next, we highlight the PCD-Net's interpretability by visualizing 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.

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.

Diagnostic Potential of miRNA-15a-5p for the Early Detection of Myocardial Infarction

Myocardial infarction (MI), also known as a heart attack, is caused by reduced or complete cessation of blood flow to the part of the myocardium. MicroRNAs (miRNAs) are key regulators in various cellular mechanisms and have the potential to be used as diagnostic markers in certain diseases such as MI, atherosclerosis, heart failure coronary artery disease, hypertrophy, and fibrosis. The present study was conducted to assess the early diagnostic potential of circulatory miRNA-15a-5p. By using bioinformatics tools, it was found that BCL2, an antiapoptotic gene, is the putative target of miRNA-15a-5p. The blood was collected from the 50 MI patients recruited in Lady Reading Hospital Peshawar. qRT-PCR analysis showed significantly upregulated miRNA-15a-5p in MI patients’ blood and isoproterenol-induced MI rats’ blood and tissue samples. The decreased level of the antiapoptotic gene, BCL2, was confirmed by qRT-PCR in the circulation of both MI patients and MI rat model. The toxic potential of isoproterenol for MI induction in rats was confirmed by disrupted heart tissue histology, increased reactive oxygen species (ROS) and decreased antioxidative enzymes such as superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), reduced glutathione (GSH) and ascorbate peroxidase (APX) in rat serum and tissue homogenate samples. In serum samples, liver function tests, and lipid profiling showed elevated levels in the MI rat model. Concludingly, this study provides future insights for miRNA research that will advance the development of diagnostic markers for the early detection of MI in patients.

Automatic detection and localisation of myocardial infarction using multi-channel dense attention neural network

Myocardial infarction (MI) is a life-threatening cardiac disease. Electrocardiogram (ECG) is crucial in the clinical diagnosis of MI, facilitating early localisation and diagnosis, guiding timely intervention, and improving patient outcomes. However, most previous studies required additional denoising operations and did not propose an effective method to reduce differences between patients. Therefore, this paper presents a multi-channel dense attention neural network (MCDANN) for MI detection and localisation using 12-lead ECG signals without denoising preprocessing. MCDANN is composed of 12 parallel channels dedicated to the automatic extraction of heartbeat features. The extracted features from all channels are then aggregated for MI detection and localisation. Each channel consists primarily of the dense layers and the squeeze-and-excitation (SE) module. The dense layers enhance the reuse of feature information within the leads, whereas the SE module weakens less critical information in the ECG classification task. We conduct inter-patient and intra-patient experiments to evaluate the MCDANN model based on the PTB diagnostic database. For MI detection, MCDANN achieved 99.94% accuracy and 99.92% F1 score in intra-patient experiments and 98.27% accuracy and 96.84% F1 score in inter-patient experiments. For MI localisation, MCDANN achieved 99.85% accuracy and 99.84% F1 score in intra-patient experiments, and 81.70% accuracy and 80.73% F1 score in inter-patient experiments. Based on the experimental findings, it is clear that the MCDANN model exhibits remarkable performance in MI detection and localisation, both for intra-patient and inter-patient cases. Notably, it substantially enhances the performance of inter-patient MI localisation compared to other existing methods.

SRTNet: Scanning, Reading, and Thinking Network for myocardial infarction detection and localization

Objective:The incidence of myocardial infarction (MI) is increasing year by year and is characterized by a high mortality rate with rapid outbreaks. Electrocardiogram (ECG)-based detection and localization of myocardial infarction is susceptible to individual patient differences. We propose a new deep learning model to improve the detection and localization performance on different patient data. Methods:To address these issues, we propose SRTNet, a novel model with scanning, reading, and thinking modules, for MI detection and localization. The model learns ECG features from three different perspectives, namely, the scanning module for learning 12-lead ECG features, the reading module for learning the correlation features of different leads, and the thinking module for learning temporal dimension features. The model learned features contain both temporal and spatial ones that can alleviate overfitting individual differences. Results:In the experiments, we used 5-fold cross-validation in the PTB dataset to demonstrate the performance of SRTNet. The results show that SRTNet achieves the state-of-the-art performance. For detection performance, the accuracy is 99.25%, the sensitivity is 99.15%, the specificity is 98.58%, and the F1 score is 99.09%. For localization performance, the accuracy is 68.45%, the sensitivity is 61.82%, the specificity is 67.95% , and the F1 score is 60.50%. Conclusion:The experimental results show that SRTNet’s multi-level feature learning method can detect and locate MI more accurately. Significance:Furthermore, the design of SRTNet provided a new perspective on clinical MI detection and localization systems.

Diagnostic and prognostic value of the sex-specific 99th percentile of four high-sensitivity cardiac troponin assays in patients with suspected myocardial infarction.

High-sensitivity cardiac troponin (hs-cTn) assays are used for detection of myocardial infarction (MI). Ninety-ninth percentiles show wide inter-assay variation. The use of sex-specific cut-offs is recommended as definitory cut-off for MI. We compared diagnostic performance and prognostic value of sex-specific 99th percentiles of four hs-cTn assays in patients with suspected MI. Concentrations of four hs-cTn assays were measured at presentation and after 3 h in patients with suspected MI. Final diagnoses were adjudicated according to the 4th Universal Definition of MI. Unisex and sex-specific 99th percentiles were evaluated as diagnostic cut-offs following the ESC 0/3 h algorithm. These cut-offs were used in Cox-regression analyses to investigate the association with a composite endpoint of MI, revascularization, cardiac rehospitalization, and death. Non-ST-elevation MI was diagnosed in 368 of 2718 patients. Applying the unisex 99th percentile, Elecsys hs-cTnT provided highest negative predictive value (NPV) of 99.7 and a positive predictive value (PPV) of 75.9. The analysed hs-cTnI assays showed slightly lower NPVs and comparable PPVs [Architect (NPV 98.0, PPV of 71.4); Atellica (NPV 97.7, PPV of 76.1); Pathfast (NPV 97.7, PPV of 66.6)]. Application of sex-specific 99th percentiles did not significantly affect diagnostic performance. Concentrations above 99th percentile were independent predictors for impaired long-term outcome (hazard ratios 1.2-1.5, P < 0.001). We describe a good diagnostic accuracy of four hs-cTn assays using the assay-specific 99th percentile for detection of MI. Application of sex-specific 99th percentiles did neither affect diagnostic performance nor prognostic value significantly. Finally, values above the 99th percentile were associated with poor long-term outcome.

Diagnostic accuracy for CZT gamma camera compared to conventional gamma camera technique with myocardial perfusion single-photon emission computed tomography: Assessment of myocardial infarction and function

BackgroundThe solid-state cadmium-zinc-telluride (CZT) gamma camera for myocardial perfusion single-photon emission computed tomography (MPS) has theoretical advantages compared to the conventional gamma camera technique. This includes more sensitive detectors and better energy resolution. We aimed to explore the diagnostic performance of gated MPS with a CZT gamma camera compared to a conventional gamma camera for detection of myocardial infarct (MI) and assessment of left ventricular (LV) volumes and ejection fraction (LVEF), using cardiac magnetic resonance (CMR) as the reference method. MethodsSeventy-three patients (26% female) with known or suspected chronic coronary syndrome were examined with gated MPS using both a CZT gamma camera and a conventional gamma camera as well as with CMR. Presence and extent of MI on MPS and late gadolinium enhancement (LGE) CMR was evaluated. For LV volumes, LVEF and LV mass, gated MPS images and cine CMR images were evaluated. ResultsMI was found in 42 patients on CMR. The overall sensitivity, specificity, positive and negative predictive values for the CZT and the conventional gamma camera were the same (67%, 100%, 100% and 69%). For infarct size > 3% on CMR, the sensitivity was 82% for the CZT and 73% for the conventional gamma camera, respectively. LV volumes were significantly underestimated by MPS compared to CMR (P ≤ .002 for all measures). The underestimation was slightly less pronounced for the CZT compared to the conventional gamma camera (2-10 mL, P ≤ .03 for all measures). For LVEF, however, accuracy was high for both gamma cameras. ConclusionDifferences between a CZT and a conventional gamma camera for detection of MI and assessment of LV volumes and LVEF are small and do not appear to be clinically significant.

Early myocardial infarction detection over multi-view echocardiography

Myocardial infarction (MI) is the leading cause of mortality in the world. Its early diagnosis can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. The regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in that can be captured by echocardiography. However, assessing the motion only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 12 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the left ventricle wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the displacements, which are concatenated and fed into the classifiers to detect MI. The main contributions of this study are (1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, (2) improving the performance of the prior work of threshold-based APs by a machine learning based approach, and (3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. The proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography. The software implementation is shared at https://github.com/degerliaysen/MultiEchoAI.

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach.

Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.

Segmental and transmural motion of the rat myocardium estimated using quantitative ultrasound with new strategies for infarct detection.

Introduction: The estimation of myocardial motion abnormalities has great potential for the early diagnosis of myocardial infarction (MI). This study aims to quantitatively analyze the segmental and transmural myocardial motion in MI rats by incorporating two novel strategies of algorithm parameter optimization and transmural motion index (TMI) calculation. Methods: Twenty-one rats were randomly divided into three groups (n = 7 per group): sham, MI, and ischemia-reperfusion (IR) groups. Ultrasound radio-frequency (RF) signals were acquired from each rat heart at 1 day and 28 days after animal model establishment; thus, a total of six datasets were represented as Sham1, Sham28, MI1, MI28, IR1, and IR28. The systolic cumulative displacement was calculated using our previously proposed vectorized normalized cross-correlation (VNCC) method. A semiautomatic regional and layer-specific myocardium segmentation framework was proposed for transmural and segmental myocardial motion estimation. Two novel strategies were proposed: the displacement-compensated cross-correlation coefficient (DCCCC) for algorithm parameter optimization and the transmural motion index (TMI) for quantitative estimation of the cross-wall transmural motion gradient. Results: The results showed that an overlap value of 80% used in VNCC guaranteed a more accurate displacement calculation. Compared to the Sham1 group, the systolic myocardial motion reductions were significantly detected (p < 0.05) in the middle anteroseptal (M-ANT-SEP), basal anteroseptal (B-ANT-SEP), apical lateral (A-LAT), middle inferolateral (M-INF-LAT), and basal inferolateral (B-INF-LAT) walls as well as a significant TMI drop (p < 0.05) in the M-ANT-SEP wall in the MI1 rats; significant motion reductions (p < 0.05) were also detected in the B-ANT-SEP and A-LAT walls in the IR1 group. The motion improvements (p < 0.05) were detected in the M-INF-LAT wall in the MI28 group and the apical septal (A-SEP) wall in the IR28 group compared to the MI1 and IR1 groups, respectively. Discussion: Our results show that the MI-induced reductions and reperfusion-induced recovery in systolic myocardial contractility could be successfully evaluated using our method, and most post-MI myocardial segments could recover systolic function to various extents in the remodeling phase. In conclusion, the ultrasound-based quantitative estimation framework for estimating segmental and transmural motion of the myocardium proposed in our study has great potential for non-invasive, novel, and early MI detection.

Instantaneous detection of acute myocardial infarction and ischaemia from a single carotid pressure waveform in rats.

Myocardial infarction (MI) is one of the leading causes of death worldwide. It is well accepted that early diagnosis followed by early reperfusion therapy significantly increases the MI survival. Diagnosis of acute MI is traditionally based on the presence of chest pain and electrocardiogram (ECG) criteria. However, around 50% of the MIs are without chest pain, and ECG is neither completely specific nor definitive. Therefore, there is an unmet need for methods that allow detection of acute MI or ischaemia without using ECG. Our hypothesis is that a hybrid physics-based machine learning (ML) method can detect the occurrence of acute MI or ischaemia from a single carotid pressure waveform. We used a standard occlusion/reperfusion rat model. Physics-based ML classifiers were developed using intrinsic frequency parameters extracted from carotid pressure waveforms. ML models were trained, validated, and generalized using data from 32 rats. The final ML models were tested on an external stratified blind dataset from additional 13 rats. When tested on blind data, the best ML model showed specificity = 0.92 and sensitivity = 0.92 for detecting acute MI. The best model's specificity and sensitivity for ischaemia detection were 0.85 and 0.92, respectively. We demonstrated that a hybrid physics-based ML approach can detect the occurrence of acute MI and ischaemia from carotid pressure waveform in rats. Since carotid pressure waveforms can be measured non-invasively, this proof-of-concept pre-clinical study can potentially be expanded in future studies for non-invasive detection of MI or myocardial ischaemia.

Optimization of the cotransfection of SERCA2a and Cx43 genes for myocardial infarction complications

As our previous study showed, the therapeutic effect of two genes (SERCA2a and Cx43) on heart failure after myocardial infarction (MI) was greater than that of single gene (SERCA2a or Cx43) therapy for bone marrow stem cell (BMSC) transplantation. Based on previous research, the aim of this study was to investigate the optimal ratio of codelivery of SERCA2a and Cx43 genes for MI therapy after biotinylated microbubble (BMB) transplantation via ultrasonic-targeted microbubble destruction (UTMD). Forty rats underwent left anterior descending (LAD) ligation and BMSC injection into the infarct and border zones. Four weeks later, the genes SERCA2a and Cx43 were codelivered at different ratios (1:1, 1:2 and 2:1) into the infarcted heart via UTMD. Cardiac mechanoelectrical function was determined at 4 wks after gene delivery, and the hearts of the rats were harvested for measurement of MI size and detection of SERCA2a and Cx43 expression. Q-PCR analysis of the expression of Nkx2.5 and GATA4 in the myocardial infarct zone and measurement of neovascularization in infarcted hearts. After comparing the therapeutic effects of different cogene ratios, the SERCA2a/Cx43–1:2 group showed remarkable cardiac electrical stability and strengthened the role of anti-arrhythmia. In conclusion, the optimum ratio of the SERCA2a/Cx43 gene is 1:2, which is advantageous for maintaining cardiac electrophysiological stability.

Automated Myocardial Infarction Screening Using Morphology-Based Electrocardiogram Biomarkers.

Ischemic heart disease (IHD), a critical and dreadful cardiovascular disease, is a leading cause of death globally. The steady progress of IHD leads to an irreversible condition called myocardial infarction (MI). The detection of MI can be done by observing the altered electrocardiogram (ECG) characteristics. Often, automated ECG analysis is preferred in place of visual inspection to reduce time and ensure reliable detection even when the recording quality is not very good. This paper presents an automated approach to classify recent MI, past MI, and normal sinus rhythm (NSR) classes based on the morphological features of the ECG. In clinical practice, a standard 12-lead ECG setup is typically employed to identify MI. However, acquiring a 12-lead ECG is not always convenient. Hence, in this study, we have explored the possibility of using a minimal number of ECG leads by deriving the augmented limb leads using leads I and II. A well-known and widely used ensemble machine learning tool, the random forest (RF) classifier is trained using features extracted from the derived augmented limb leads and their combinations. An RF classifier built using features extracted from all limb leads has outperformed classifiers built on combinations of them with five-fold cross-validation training accuracy of 97.9 (±0.008) % and testing accuracy of 98 %.Clinical relevance- As high sensitivity is reported in identifying recent MI and past MI classes, the proposed approach is suitable for preventative healthcare applications since it is less likely that subjects with recent or past MI will be misclassified. Due to its low computational complexity, better interpretability, and comparable performance to the state-of-the-art results, the proposed approach can be employed in clinical and cardiac health screening applications. It also has the potential to be employed in remote monitoring with mobile and wearable devices because it is built on features extracted from only lead I and II ECG recordings.