Clinical Diagnosis Of Cardiovascular Diseases Research Articles

An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review.

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

Small but strong: the emerging role of small nucleolar RNA in cardiovascular diseases.

Cardiovascular diseases (CVDs) are the leading cause of mortality and disability worldwide. Numerous studies have demonstrated that non-coding RNAs (ncRNAs) play a primary role in CVD development. Therefore, studies on the mechanisms of ncRNAs are essential for further efforts to prevent and treat CVDs. Small nucleolar RNAs (snoRNAs) are a novel species of non-conventional ncRNAs that guide post-transcriptional modifications and the subsequent maturation of small nuclear RNA and ribosomal RNA. Evidently, snoRNAs are extensively expressed in human tissues and may regulate different illnesses. Particularly, as the next-generation sequencing techniques have progressed, snoRNAs have been shown to be differentially expressed in CVDs, suggesting that they may play a role in the occurrence and progression of cardiac illnesses. However, the molecular processes and signaling pathways underlying the function of snoRNAs remain unidentified. Therefore, it is of great value to comprehensively investigate the association between snoRNAs and CVDs. The aim of this review was to collate existing literature on the biogenesis, characteristics, and potential regulatory mechanisms of snoRNAs. In particular, we present a scientific update on these snoRNAs and their relevance to CVDs in an effort to cast new light on the functions of snoRNAs in the clinical diagnosis of CVDs.

Calculation of left ventricular ejection fraction using an 8-layer residual U-Net with deep supervision based on cardiac CT angiography images versus echocardiography: a comparative study.

Accurate segmentation of the left ventricle (LV) is an important step in assessing cardiac function. Cardiac CT angiography (CCTA) has become an important means of clinical diagnosis of cardiovascular diseases (CVDs) because of its advantages of non-invasive, short examination time and low cost. In order to obtain the segmentation of LV in CCTA scans, we propose a deep learning method based on 8-layer residual U-Net with deep supervision. In this study we compared the left ventricular ejection fraction (LVEF) calculated by deep learning (DL) method (AccuLV) from CCTA to LVEF by conventional two-dimensional echocardiography (EC). This was a retrospective cross-sectional study, and consecutive patients who had undergone CCTA and EC in our hospital from February 2021 to May 2021 were recruited. The current study included 180 patients who had undergone CCTA and EC. To obtain LVEF, we used an 8-layer residual U-Net with deep supervision to segment LV contours from CCTA images and compute LVEF (DL-LVEF). The EC and DL-LVEF measurements were compared. A 50% EC-LVEF cut-off value was used as a reference standard to assess the diagnostic performance of AccuLV in assessing LV function. The overall mean EC-LVEF and DL-LVEF values were 64.0% (52.3%, 69.0%) and 73.0% (52.3%, 77.0%), respectively. Three patient groups were studied: (I) hypertensive patients, (II) postmenopausal women, and (III) diabetes. EC-LVEF and DL-LVEF were found to be positively correlated for all of the included patients (r=0.82, P<0.001), with the detailed results for the three groups as follows: hypertensive patients (r=0.77, P<0.001), postmenopausal women (r=0.92, P<0.001) and diabetes (r=0.88, P<0.001). The diagnostic accuracy, sensitivity, and specificity of the DL method in predicting EC-LVEF <50% for all patients were 93.9%, 92.3%, and 94.3%, and for hypertensive patients were 95.4%, 93.8%, and 95.8%, for postmenopausal women were 87.0%, 100%, and 84.2%, for diabetes were 97.4%, 100%, and 96.6%. In comparison to echocardiography, which is commonly used in clinical setting, AccuLV may be a promising, fully automated tool for rapid and accurate quantification of LV function and thus for making reliable clinical decisions.

Clinical detection of total homocysteine in human serum using surface-enhanced Raman spectroscopy

Cardiovascular diseases cause enormous morbidity and mortality worldwide. Excessive blood total homocysteine (tHcy) is an independent risk factor for cardiovascular diseases. Therefore, it is imperative to measure the content of tHcy in human blood. However, the currently available analytical methods have the disadvantages such as being time-consuming and expensive. Herein, we propose a rapid clinical quantification of tHcy in human serum using high-performance Ag Nanopolyhedra (Ag NPOLY) as surface-enhanced Raman spectroscopy (SERS) substrates. Generally, 80% of homocysteine (Hcy) in serum is oxidized, and only 20% of Hcy is free. To understand this behavior, we explored the reduction effect of tris(2-carboxyethyl) phosphine (TCEP) on serum Hcy at different pH. The results confirmed that our method can detect 2 µM of Hcy in human serum. As a proof-of-concept, the developed method was applied to the actual clinical detection of human serum tHcy, and the serum of 5 hyperhomocysteinemia (HHcy) patients was verified. Moreover, the obtained, results were consistent with high-performance liquid chromatography (HPLC) results. The linear correlation coefficient of the two methods was 0.9766. Importantly, our analysis can be completed within 15 min, while HCLP requires more than 1 h. Overall, we have successfully developed a rapid SERS-based quantitative method for human serum tHcy, which can provide a new solution for the clinical diagnosis of cardiovascular diseases in the future.

GDF-Net: A multi-task symmetrical network for retinal vessel segmentation

Retinal fundus vessels contain rich geometric features, including both thick and thin vessels, which is particularly important for accurate clinical diagnosis of cardiovascular diseases. Nowadays, deep convolution neural networks (DCNNs) have shown remarkable performance on fundus vessel segmentation combined with effective contextual feature expression ability, especially U-Net network and its variants. However, due to multiple convolution and pooling operations, existing methods using DCNNs will cause the information loss of details or micro objects, which impacts the accuracy of thin vascular detection and cause poor final segmentation performance. To address this problem, a multi-task symmetric network, called GDF-Net, is proposed for accurate retinal fundus image segmentation, which is composed by two symmetrical segmentation networks (global segmentation network branch and detail enhancement network branch) and a fusion network branch. To address the information loss issue, two symmetrical segmentation networks are proposed to extract global contextual features and detail features respectively. To strength both advantages of two symmetric segmentation networks, this paper presented a fusion network to perform feature integration to improve the segmentation accuracy for retinal fundus vessels. To better validate the usefulness and excellence of the GDF-Net, experiments demonstrate that the GDF-Net has achieved a competitive performance on retinal vessel segmentation.

Highly adhesive stretchable polymer and highly dynamic stable human electrophysiological monitoring

<p indent="0mm">Highly dynamic and stable human electrophysiological monitoring has broad application prospects in intelligent wearable health monitoring, clinical diagnosis of cardiovascular diseases, treatment of nervous system diseases and intelligent human–computer interaction. As a signal interaction bridge between human and the external environment, on-skin electrodes can be attached to the skin surface to monitor and collect various electrophysiological signals in a non-invasive way, making it an ideal platform for highly dynamic and stable electrophysiological monitoring. From the material point of view, the highly dynamic stability of on-skin electrodes depends on the high adhesion and stretchability of the substrate. Therefore, it is an important research direction in the field of flexible intelligent health monitoring to prepare highly adhesive stretchable polymer and construct highly conformal on-skin electrodes that adhere to human skin surface through rational design of polymer structure, so as to realize highly dynamic and stable human electrophysiological monitoring. In this paper, we summarized the recent research progress in our group on the preparation of highly adhesive stretchable polymer and their applications in electrophysiological monitoring with highly dynamic stability, and discussed the opportunities and challenges of highly adhesive stretchable polymer in the next generation of flexible intelligent health monitoring.

Cardiac Segmentation Method Based on Domain Knowledge.

Echocardiography plays an important role in the clinical diagnosis of cardiovascular diseases. Cardiac function assessment by echocardiography is a crucial process in daily cardiology. However, cardiac segmentation in echocardiography is a challenging task due to shadows and speckle noise. The traditional manual segmentation method is a time-consuming process and limited by inter-observer variability. In this paper, we present a fast and accurate echocardiographic automatic segmentation framework based on Convolutional neural networks (CNN). We propose FAUet, a segmentation method serially integrated U-Net with coordinate attention mechanism and domain feature loss from VGG19 pre-trained on the ImageNet dataset. The coordinate attention mechanism can capture long-range dependencies along one spatial direction and meanwhile preserve precise positional information along the other spatial direction. And the domain feature loss is more concerned with the topology of cardiac structures by exploiting their higher-level features. In this research, we use a two-dimensional echocardiogram (2DE) of 88 patients from two devices, Philips Epiq 7C and Mindray Resona 7T, to segment the left ventricle (LV), interventricular septal (IVS), and posterior left ventricular wall (PLVW). We also draw the gradient weighted class activation mapping (Grad-CAM) to improve the interpretability of the segmentation results. Compared with the traditional U-Net, the proposed segmentation method shows better performance. The mean Dice Score Coefficient (Dice) of LV, IVS, and PLVW of FAUet can achieve 0.932, 0.848, and 0.868, and the average Dice of the three objects can achieve 0.883. Statistical analysis showed that there is no significant difference between the segmentation results of the two devices. The proposed method can realize fast and accurate segmentation of 2DE with a low time cost. Combining coordinate attention module and feature loss with the original U-Net framework can significantly increase the performance of the algorithm.

Highly Conformal Polymers for Ambulatory Electrophysiological Sensing.

Stable ambulatory electrophysiological sensing is widely used for smart e-healthcare monitoring, clinical diagnosis of cardiovascular diseases, treatment of neurological diseases, and intelligent human-machine interaction. As the favorable signal interaction platform of electrophysiological sensing, the conformal property of on-skin electrodes is an extremely crucial factor that can affect the stability of long-term ambulatory electrophysiological sensing. From the perspective of materials, to realize conformal contact between electrodes and skin for stable sensing, highly conformal polymers are in great demand and attracting ever-growing attention. This review focuses on the recent progress of highly conformal polymers for ambulatory electrophysiological sensing, including their synthetic methods, conformal property, and potential applications. Specifically, three main types of highly conformal polymers for stable long-term electrophysiological signals monitoring are proposed, including nature silk fibroin based conformal polymers, marine mussels bioinspired conformal polymers, and other conformal polymers such as zwitterionic polymers and polyacrylamides. Furthermore, the future challenges and opportunities in preparing highly conformal polymers for on-skin electrodes are also highlighted.

Multilabel 12-Lead ECG Classification Based on Leadwise Grouping Multibranch Network

The 12-lead electrocardiogram (ECG) is widely used in the clinical diagnosis of cardiovascular disease, and deep learning has become an effective approach to automatic ECG classification. Generally, current research simply regards the 12-lead ECG signal as an ordinary 2-D array and does not specifically consider the intrinsic relationship between different leads when building the neural networks. However, ECG classes, from the biomedical perspective, mainly show specific patterns on one or several leads rather than all 12 leads, which suggests that it would be more efficient to learn class-specific intrinsic features from corresponding leads. To make use of such domain knowledge, in this study, we present a multilabel 12-lead ECG classification method based on the leadwise grouping multibranch network. A simple yet effective leadwise grouping strategy is proposed to incorporate domain knowledge to ECG classification models. Meanwhile, a multibranch network is designed accordingly, and spatial and temporal features are extracted by a BranchNet for each branch, which corresponds to one lead group. Moreover, an extended focal loss is presented to solve the class imbalance problem for multilabel classification. The proposed method was evaluated on two large-scale real-world ECG databases and yielded values of 0.9599, 0.7920, 0.7490, 0.5537, 0.1282, and 1.5354 for the area under receiver operating characteristic curve (AUROC), area under precision–recall curve (AUPRC), F1, accuracy (Acc), one-error (OE), and coverage (Cove), respectively, on the PhysioNet/Computing in Cardiology Challenge 2020 (CinC2020) database and values of 0.9531, 0.8975, 0.8102, 0.7484, 0.1791, and 0.5392 on the Shandong Provincial Hospital (SPH) database. The results are better than the existing work and are reached with fewer parameters and lower computational cost, demonstrating the effectiveness of the proposed method and leadwise grouping strategy.

ECG signal classification based on adaptive multi-channel weighted neural network

The intelligent diagnosis of cardiovascular diseases is a topic of great interest. Many electrocardiogram (ECG) recognition technologies have emerged, but most of them have low recognition accuracy and poor clinical application. To improve the accuracy of ECG classification, this paper proposes a multi-channel neural network framework. Concretely, a multi-channel feature extractor is constructed by using four types of filters, which are weighted according to their importance, as measured by kurtosis. A bidirectional long short-term memory (BLSTM) network structure based on attention mechanism is constructed, and the extracted features are taken as the input of the network, and the algorithm is optimized by attention mechanism. An experiment conducted on the MIT-BIH arrhythmia database shows that the proposed algorithm obtains excellent results, with 99.20 % specificity, 99.87 % sensitivity, and 99.89 % accuracy. Therefore, the algorithm is practical and effective in the clinical diagnosis of cardiovascular diseases.

Subclinical cardiovascular disease and risk of incident frailty: The British Regional Heart Study.

Subclinical cardiovascular disease (CVD) is cross-sectionally associated with frailty, but the relationship between subclinical CVD and incident frailty has not been reported. We aimed to assess this prospective association. Longitudinal analysis of data from the British Regional Heart Study, a prospective cohort study. 1057 men, aged 71-92years, robust or pre-frail at baseline, and without a clinical diagnosis of CVD. Participants underwent baseline measurement of carotid-femoral pulse wave velocity (cfPWV), carotid intima-media thickness (CIMT), carotid distensibility coefficient (DC), and ankle-brachial pressure index (ABPI), and had questionnaire-based frailty assessment after three years. Frailty status was based on the Fried phenotype. Multivariate logistic regressions examined associations between incident frailty and tertile of cfPWV, CIMT, DC, and ABPI group (<0.9, 0.9-1.4, ≥1.4). 865 men were examined and completed the 3year follow-up questionnaire, of whom 78 became frail. Adjusted for age, prefrailty, body mass index, diabetes, smoking, atrial fibrillation, blood pressure, renal function, and incident CVD, higher CIMT was associated with greater odds of incident frailty (2nd tertile OR 1.62, 95% CI 0.78-3.35, 3rd tertile OR 2.61, 95% CI 1.30-5.23, p=0.007, trend p=0.006). cfPWV showed a weaker, non-significant association (2nd tertile OR 1.79, 95% CI 0.85-3.78, 3rd tertile OR 1.73, OR 0.81-3.72, p=0.16, trend p=0.20). There was no clear association between incident frailty and DC or ABPI. In subgroup analyses, CIMT was significantly associated with incident frailty in men ≥80years (3rd tertile OR 6.99, 95%CI 1.42-34.5), but not in men aged 75-80 or<75years. Subclinical CVD, as measured by CIMT, is associated with greater risk of incident frailty in older men over three year follow-up, independent of the development of clinically-apparent stroke, heart failure, or myocardial infarction, and may be a modifiable risk factor for frailty. This association may be stronger in very old age.

Multi-label correlation guided feature fusion network for abnormal ECG diagnosis

Electrocardiographic (ECG) abnormalities are the most intuitive manifestation in the clinical diagnosis of cardiovascular disease. Although significant progress has been achieved in diagnosing ECG abnormalities, current convolutional neural network (CNN) based methods still show a relatively high rate of misdiagnosis. On the one hand, the ECG data an abnormality imbalance problem. On the other hand, the previous work did not consider the multi-label correlations of the ECG abnormalities. The main objective of this paper is to design an ECG abnormal event detection model based on multi-label correlation guided feature fusion. First, the proposed method calculates the correlation of different ECG abnormality labels on the basis of their frequency and Bayesian conditional probability and divides the ECG abnormalities into groups of different levels. The first-level ECG abnormalities are those that have the highest frequency and the second-level abnormalities are those that have a higher probability of other ECG abnormalities given the first-level ECG abnormalities, and so on. Then, the feature fusion strategy uses the multi-label correlation to guide the fusion of features of different levels. Specifically, the features of the first-level ECG signals are integrated into the features of the second-level ECG signals and the features of the former two-level ECG signals are integrated into the features of the third-level ECG signals. Furthermore, a multi-scale receptive field fusion module is proposed to enhance the multi-scale representation ability of the features. To evaluate the performance of the proposed method, it is validated against the 1st China physiological signal challenge dataset, and it achieved better results compared with other state-of-the-art methods. This proposed method achieves a precision score of 0.816, a sensitivity score of 0.845 and an F1-score of 0.827, which are better than other methods. The comparative analysis shows that the proposed method demonstrates the effectiveness in abnormal ECG diagnosis.

Colorimetric Detection of Cholesterol Based on the Peroxidase‐Like Activity of Metal‐Organic Framework MIL‐101(Cr)

AbstractEfficient quantitative determination of cholesterol has important application value in drug testing and clinical diagnosis of cardiovascular diseases. In this work, a metal‐organic framework MIL‐101(Cr) (MIL=Materials of Institute Lavoisier) was synthesized and developed as a peroxidase mimic for colorimetric detection of cholesterol. Kinetic analysis indicated that the MIL‐101(Cr) nanoparticles showed a higher Vmax (2.27*10−7 MS−1) and a lower Km (0.07 mM) than the natural horseradish peroxidase (HRP). An effective colorimetric sensor has been proven to detect cholesterol. Based on the outstanding catalytic activity, the colorimetric sensor displayed a linear response for cholesterol in the concentration range from 1.1 μM to 22.9 μM and the detection limit as low as 0.86 μM. Furthermore, the proposed method based on MIL‐101(Cr) nanoparticles showed a favorable reproducibility and recovery. The sensor had been successfully applied to determine serum cholesterol level in mice and human, respectively.

Recent Development in Nanomaterial-Based Electrochemical Sensors for Cholesterol Detection

Functional nanomaterials have attracted significant attention in a variety of research fields (in particular, in the healthcare system) because of the easily controllable morphology, their high chemical and environmental stability, biocompatibility, and unique optoelectronic and sensing properties. The sensing properties of nanomaterials can be used to detect biomolecules such as cholesterol. Over the past few decades, remarkable progress has been made in the production of cholesterol biosensors that contain nanomaterials as the key component. In this article, various nanomaterials for the electrochemical sensing of cholesterol were reviewed. Cholesterol biosensors are recognized tools in the clinical diagnosis of cardiovascular diseases (CVDs). The function of nanomaterials in cholesterol biosensors were thoroughly discussed. In this study, different pathways for the sensing of cholesterol with functional nanomaterials were investigated.

A Unified Automated Segmentation Technique for the Left Ventricle Segmentation in Cardiac MRI

Though the technology updated for every minute of time to aid all kinds of problems facing in every day practical life, the technique requires amelioration to solve or support specific kinds of problems like early identification of cardiovascular disease. For the past decades, cardiovascular diseases (CVD) are the major reason for death. Cardiac MRI is useful for acquiring the anatomical data of the alive heart for the clinical diagnosis of cardiovascular diseases. F rom the LV Segmentation on cardiac MRI, the important parameters estimated to diagnose are ejection fraction, left ventricle myocardium mass, stroke volume, etc. Hence segmenting the LV automatically plays a significant role in helping the physician to test cardiac functions quickly since manual segmentation is a time-consuming work. This Automatic Segmentation also eliminates manual errors during evaluation. Therefore, to discuss the problem we propose enhanced techniques that is a unified method to segment the left ventricle automatically from the cardiac MRI image. The algorithm demonstrated in this work is to combine existing segmentations in an efficient way to give even more efficient and accurate results. two performance evaluation techniques namely APD and DICE to quantify the result. Outcomes obtained are then compared with recent segmentation methods to show the efficiency of the unified segmentation technique.

HEARTBEAT CLASSIFICATION ALGORITHM BASED ON ONE-DIMENSIONAL CONVOLUTION NEURAL NETWORK

The morbidity of cardiovascular disease increasingly rises, which makes great impact upon people’s health and life. Electrocardiogram (ECG) beat classification is of great significance to clinical diagnosis of cardiovascular diseases. Traditional ECG signal classification algorithm relies heavily on the accuracy of feature extraction or increases the complexity of the calculation process by means of the correlation characteristic coefficient transformation, which results in that the ECG beat classification effect is still not satisfactory. Aimed at this problem, a novel method based on convolution neural network (CNN) is presented in this paper. First, ECG signal is preprocessed to suppress the noise and to locate the R peaks, and five kinds of ECG beat waveform data are obtained. Then taking ECG beat sampling points as input, four layers of one-dimensional CNN are constructed for feature extraction and classification. Finally, experimental verification is carried out on the data from MIT-BIH database, and the accuracy of recognition and classification of the presented method reaches 99.10%. Comparison with the methods based on artificial features, this method shows better performance, which avoids serious dependence on the accuracy of feature extraction, skips the steps of feature extraction and selection, and reduces the complexity of computational process.

Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

BackgroundElectronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency.MethodsThis paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction.ResultsOn the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods.ConclusionsThe character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What’s more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training.

Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach

In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentation method by integrating the convolutional neural network (CNN) with the level set approach. Firstly, a CNN based myocardial central-line detection algorithm was proposed to replace the manual initialization process for traditional level set approaches. Secondly, we present a novel central-line guided level set approach (CGLS) for delineating the myocardium region. In particular, we incorporate the myocardial central-line into the level set energy formulation as a constraint term. It plays two important roles in the iterative process: restricting the zero-level contour to stay around the myocardial central-line and preserving the anatomical geometry of myocardium segmentation result. In experiments, our method yields results as below: (1) 1.74 mm and 2.06 mm in terms of epicardium and endocardium perpendicular distance on MICCAI 2009 dataset, (2) 0.955 and 0.853 in terms of LV and myocardium Dice metric at the end-diastole on ACDC MICCAI 2017 dataset. The experimental data demonstrate that our method outperforms some state-of-the-art methods and achieves a good agreement with the manual segmentation results.

OCT Image Recognition of Cardiovascular Vulnerable Plaque Based on CNN

Optical coherence tomography (OCT) shows an important role in the diagnosis of cardiovascular diseases and the detection and intervention of vulnerable plaques. Clinical diagnosis of vulnerable plaques in cardiovascular system based on optical coherence tomography mainly relies on the manual analysis of vulnerable plaques in images by cardiovascular physicians. This analysis process is prone to subjective misjudgments and heavy workload. Studying the recognition technology of cardiovascular vulnerable plaque image will greatly improve the accuracy of diagnosis and reduce the workload of cardiovascular physicians, which is an effective way to achieve efficient diagnosis and treatment. In view of the recognition of cardiovascular vulnerable plaque medical images, a model based on convolution neural network (CNN) recognition is constructed. The CNN is used to learn the features of different levels from the original input OCT images. At the same time, several decision-making levels are designed. These decision-making levels can classify OCT images according to different feature maps, and finally make final recognition decisions according to the classification results. The experimental results on the clinical data set labeled by doctors show that the classification and recognition model of cardiovascular vulnerable plaque OCT image based on CNN has a high recognition rate. Making full use of the multilevel features of convolutional neural networks can effectively classify and recognize the OCT images of vulnerable cardiovascular plaques, provide support for the clinical diagnosis of cardiovascular diseases, and have great significance for the early intervention and prevention of cardiovascular diseases.

ECG Classification using Machine Learning

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.