Detection Of Myocardial Infarction Research Articles

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.

GRACE scores or high-sensitivity troponin for timing of coronary angiography in non-ST-elevation acute coronary syndromes

BackgroundThe GRACE risk score is generically recommended by guidelines for timing of invasive coronary angiography without stating which score should be used. The aim was to determine the diagnostic performance of different GRACE risk scores in comparison to the ESC 0/1 h-algorithm using high-sensitivity cardiac troponin (hs-cTn).MethodsProspectively enrolled patients presenting with symptoms suggestive of myocardial infarction (MI) in two large studies testing biomarker diagnostic strategies were included. Five GRACE risk scores were calculated. The amount of risk reclassification and the theoretical impact on guideline-recommended timing of invasive coronary angiography was studied.ResultsOverall, 8,618 patients were eligible for analyses. Comparing different GRACE risk scores, up to 63.8% of participants were reclassified into a different risk category. The proportion of MIs identified (i.e., sensitivity) dramatically differed between GRACE risk scores (range 23.8–66.5%) and was lower for any score than for the ESC 0/1 h-algorithm (78.1%). Supplementing the ESC 0/1 h-algorithm with a GRACE risk score slightly increased sensitivity (P < 0.001 for all scores). However, this increased the number of false positive results.ConclusionThe substantial amount of risk reclassification causes clinically meaningful differences in the proportion of patients meeting the recommended threshold for pursuing early invasive strategy according to the different GRACE scores. The single best test to detect MIs is the ESC 0/1 h-algorithm. Combining GRACE risk scoring with hs-cTn testing slightly increases the detection of MIs but also increases the number of patients with false positive results who would undergo potential unnecessarily early invasive coronary angiography.Graphical abstract

Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network

Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model.

LVSnake: Accurate and robust left ventricle contour localization for myocardial infarction detection

Background and Objective:Localization of the left ventricle (LV) in echocardiography is important for the diagnosis of cardiovascular diseases. Previous deep learning approaches leverage semantic segmentation models to perform LV or myocardium segmentation and extract important LV parameters from the binary segmentation masks. Although these algorithms yield promising segmentation results, their per-pixel classification characteristic and complex postprocessing process of the binary masks compromise their robustness and speed. Methods:In this paper, inspired by the DeepSnake algorithm in the computer vision community, we develop a deep learning model called LVSnake for left ventricle localization. Our model generates the initial contour by connecting three key landmarks of the left ventricle output by our model including the start point, the apex point, and the end point of the endocardium. Then, a contour adjustment module is designed to refine the initial contour coordinates so as to obtain the final position of the endocardium. The extracted endocardium coordinates are used to perform Myocardial Infarction (MI) detection. Results:Experiment results on the HMC-QU dataset indicate that our model performs more favorably against previous approaches in terms of localization accuracy and robustness. The overall Endpoint-Error (EPE) of our model reaches 3.4 pixels. Using ventricular endocardium location extracted by our method to perform MI detection, the classification accuracy reaches 83.1% which exceeds that of existing studies (79.2%). Conclusions:Our proposed LVSnake can robustly and accurately extract LV positions from echocardiography, which can be used for MI detection.

Parametrized pre-trained network (PPNet): A novel shape classification method using SPHARMs for MI detection

PurposeShape information contains descriptive knowledge for classification problems. A novel methodology for combining the valuable 3D and 3D + t shape information with the advantages of deep transfer learning is introduced. This method produces informative images from shape information, which are interpretable for deep CNNs previously trained using a large-scale image dataset. MethodsOur proposed structure can (1) generate 3D surfaces with optimal spatial resolution, (2) parametrize them using spherical harmonics shape descriptors (SPHARMs) with optimal frequency resolution, (3) employ registration to establish surface correspondences, and (4) convert the 3D corresponding point clouds to 2D images by spreading out the latitude and the longitude values of spherical components in the form of a flat coordinate system. The generated 2D images have the potential to feed into the pre-trained deep CNNs. The efficiency of the proposed method was assessed in myocardial infarction (MI) classification using two different datasets. ResultsFive well-known CNN architectures as fixed feature extractors were evaluated. Deep features derived from the output of convolutional and pooling layers were fed into the SVM classifier. We also explored the feasibility of adding additional information by assigning a color to each RGB channel. Subsequently, EfficinetNet-b0 reached the best performance, with an accuracy of 97.92% ± 1.47 using colored images containing additional information, respectively. ConclusionOur suggested approach by incorporating the transformed shape information and CNNs can be adopted as a robust computer-aided diagnosis system to assist clinicians in detecting various diseases such as MI.

Myocardial Infarction Signs and Symptoms: Females vs. Males.

Cardiovascular disease is the number one killer of females in the United States today, and myocardial infarction (MI) plays a role in many of these deaths. Females also present with more "atypical" symptoms than males and appear to have differences in pathophysiology underlying their MIs. Despite both differences in symptomology and pathophysiology being present in females versus males, a possible link between the two has not been studied extensively. In this systematic review, we analyzed studies examining differences in symptoms and pathophysiology of MI in females and males and evaluated possible links between the two. A search was performed for sex differences in MI in the databases PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature) Complete, Biomedical Reference Collection: Comprehensive, Jisc Library Hub Discover, and Web of Science. Seventy-four articles were ultimately included in this systematic review. Typical symptoms for both ST-elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) such as chest, arm, or jaw pain were more common in both sexes, but females presented on average with more atypical symptoms such as nausea, vomiting, and shortness of breath. Females with MI also presented with more prodromal symptoms such as fatigue in days leading up to MI, had longer delays in presentation to the hospital after symptom onset, and were older with more comorbidities than males. Males on the other hand were more likely to have a silent or unrecognized MI, which concurs with their overall higher rate of MI. As they age, females have a decrease in antioxidative metabolites and worsened cardiac autonomic function than male. In addition, at all ages, females have less atherosclerotic burden than mles, have higher rates of MI not related to plaque rupture or erosion, and have increased microvasculature resistance when they have an MI. It has been proposed that this physiological difference is etiologic for the male-female difference in symptoms, but this has not been studied directly and is a promising area of future research. It is also possible that differences in pain tolerance between males and females may play a role in differing symptom recognition, but this has only been studied one time where females with higher pain thresholds were more likely to have unrecognized MI. Again, this is a promising area for future study for the early detection of MI. Finally, differences in symptoms for patients with different atherosclerotic burden and for patients with MI due to a cause other than plaque rupture or erosion has not been studied and are both promising avenues to improve detection and patient care in the future.

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images.

Myocardial infarction (MI) occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is cardiovascular magnetic resonance imaging (MRI) with intravenously administered gadolinium-based contrast (with damaged areas apparent as late gadolinium enhancement [LGE]). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. It has the potential to reduce uncertainty due to technical variability across labs and the inherent problems of data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by atrous spatial pyramid pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: (i) background, (ii) heart muscle, (iii) blood and (iv) LGE areas. Our experiments show that the model named MI-ResNet50-AC provides the best global accuracy (97.38%), mean accuracy (86.01%), weighted intersection over union (IoU) of 96.47%, and bfscore of 64.46% for the global segmentation. However, in detecting only LGE tissue, a smaller model, MI-ResNet18-AC, exhibited higher accuracy (74.41%) than MI-ResNet50-AC (64.29%). New models were compared with state-of-the-art models and manual quantification. Our models demonstrated favorable performance in global segmentation and LGE detection relative to the state-of-the-art, including a four-fold better performance in matching LGE pixels to contours produced by clinicians.

Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG

Myocardial Infarction (MI) is an emergency condition that requires immediate medical treatment. The rapid and accurate diagnosis of MI using a 12-lead electrocardiogram (ECG) is extremely important in a clinical study to save the patient’s life. The manual interpretation of MI using a 12-lead ECG is tedious and time-consuming. Therefore, a patient-specific software-based computer-aided diagnosis framework is helpful to detect and localize MI disease accurately. This paper proposes a patient-specific higher-order tensor-based approach to detect and localize MI automatically using 12-lead ECG recordings. The 12-lead ECG recordings are segmented into 12-lead ECG beats using the multi-lead fusion-based QRS detection algorithm. The fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) based multiscale analysis method decomposes 12-lead ECG beat into a third-order tensor containing the information from the samples, beat, and intrinsic mode functions (IMFs). Furthermore, a fourth-order tensor is formulated by considering beats, samples, lead, and IMFs information of 12-lead ECG recording. The multilinear singular value decomposition (MLSVD) extracts features from the fourth-order tensors and third-order tensors of 12-lead ECG. The K-nearest neighbor (KNN), support vector machine (SVM), and stacked autoencoder-based deep neural network (SAE-DNN) models are used for the detection and localization of MI using fourth-order and third-order tensor domain features. The proposed approach is evaluated using 73 healthy control (HC) and 100 different types of MI-based 12-lead ECG recordings from a public database. The proposed approach has obtained the classification accuracy values of (98.84%, 98.27%, 98.27%) and (86.64%, 83.17%, and 81.98%) using (KNN, SVM, and SAE-DNN) models for MI detection, and localization, respectively using 30-min duration of 12-lead ECG recordings. For MI detection and localization, the suggested approach has obtained accuracy values of 96.53% and 93.32%, respectively, using the 4-s duration of 12-lead ECG recordings. Our approach outperformed existing MI detection and localization methods using 12-lead ECG recordings regarding classification performance.

Recent Strategies for Cardiac Rehabilitation in Post-Myocardial Infarction Patients: A Narrative Review

Myocardial Infarction (MI) is a severe cardiac crisis that can result in significant morbidity and death. ST segment Elevated Myocardial Infarction (STEMI) is irreparable myocardial damage caused by persistent ischaemia, and while the adage “time is muscle” remains true, rapid and prompt detection of MI continues to be a key concern. Cardiac Rehabilitation (CR) is the most significant evidencebased intervention for secondary prevention following STEMI. However, only a small proportion of patients have access to a cardiovascular recovery programme. Recent research implies that exercise-based CR provides significant benefits to patients with cardiovascular disease, including a lower risk of MI, a likely slight decline in all causes of mortality, a massive reduction in all-cause hospitalisation, along with associated medical costs, and enhanced Health-Related Quality of Life (HRQoL) upto a year of followup. Adapting rehabilitation programmes to existing abilities based on experience in the field of activity is becoming a major solution in the current CR training programme selection. The effectiveness of both centre-based and home-based therapy is proven. Telerehabilitation and home-based rehabilitation strategies have gained much importance during the Coronavirus Disease-2019 (COVID-19) pandemic. Further research is needed to standardise the treatment quality for patients with home-based CR.

Detection of Myocardial Infarction From 12-Lead ECG Trace Images Using Eigendomain Deep Representation Learning

Myocardial infarction (MI) is a life-debilitating emergency in which there is a lack of blood flow in the heart muscle, resulting in permanent damage to the myocardium and sudden cardiac death. The 12-lead electrocardiogram (ECG) is a standardized diagnostic test conducted in hospitals to detect and localize MI-based heart disease. To diagnose MI, the cardiologist visualizes the alternations in the patterns of the 12-lead-based ECG trace image. The automated detection of MI from the 12-lead-based ECG trace image using artificial intelligence (AI) based approaches is important in the clinical study for the accurate diagnosis of MI disease. This paper proposes a novel eigendomain-based deep representation learning approach to automatically detect MI using 12-lead ECG trace images. The singular value decomposition (SVD) and eigendomain grouping are employed to evaluate five modes or components from the 12-lead ECG trace image. The EfficientNetV2B2-based transfer learning model extracts feature maps from the 12-lead ECG trace image and all five modes. The global average pooling (GAP), batch normalization (BN), drop-out, and soft-max layers are used for each feature map to obtain the probability scores. The concatenated probability scores of all feature maps, followed by the dense layer and output layer, are used to detect MI. A public database containing the 12-lead ECG trace images is used to evaluate the performance of the proposed approach. The results show that for the MI class, the proposed approach has achieved the accuracy value of 100%. Similarly, for normal versus MI versus other cardiac arrhythmia-based disease classification schemes, the proposed approach has obtained the overall accuracy, F1-score, specificity, and sensitivity values of 99.03%, 99.01%, 99.49%, and 98.96%, respectively using 5-fold cross-validation (CV). The suggested approach has demonstrated higher overall accuracy than twenty-four existing transfer learning-based models to detect MI using 12-lead ECG trace images.