scholarly journals A Robust Multilevel DWT Densely Network for Cardiovascular Disease Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4777 ◽  
Author(s):  
Gong Zhang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Di Wang
Keyword(s):  
Cardiovascular Disease ◽  
Class Imbalance ◽  
Noise Removal ◽  
Classification Performance ◽  
Disease Classification ◽  
Discrete Wavelet ◽  
Average Accuracy ◽  
Sample Frequency ◽  
Peak Location ◽  
Artery Disease

Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbeats appear much less regularly than normal heartbeats. Furthermore, most existing methods rely on data preprocessing steps, such as noise removal and R-peak location. In this study, we present a robust classification system using a multilevel discrete wavelet transform densely network (MDD-Net) for the accurate detection of normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF). First, the raw ECG signals from different databases are divided into same-size segments using an original adaptive sample frequency segmentation algorithm (ASFS). Then, the fusion features are extracted from the MDD-Net to achieve great classification performance. We evaluated the proposed method considering the intra-patient and inter-patient paradigms. The average accuracy, positive predictive value, sensitivity and specificity were 99.74%, 99.09%, 98.67% and 99.83%, respectively, under the intra-patient paradigm, and 96.92%, 92.17%, 89.18% and 97.77%, respectively, under the inter-patient paradigm. Moreover, the experimental results demonstrate that our model is robust to noise and class imbalance issues.

scholarly journals An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

2021 ◽  
Vol 36 (1) ◽  
pp. 443-450
Author(s):  
Mounika Jammula
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Plant Disease ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Classification Performance ◽  
Disease Classification ◽  
Gaussian Kernel ◽  
Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

Stentul JJ ureteral - care este optiunea mai buna? New Considerations Regarding Chronic Kidney Disease, Cardiovascular Disease and Dyslipidemia in Diabetic Patients

2018 ◽  
Vol 69 (8) ◽  
pp. 2064-2066
Author(s):  
Mircea Munteanu ◽  
Adrian Apostol ◽  
Viviana Ivan
Keyword(s):  
Cardiovascular Disease ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Total Cholesterol ◽  
Total Cholesterol Level ◽  
Vascular Complications ◽  
Hdl Cholesterol ◽  
Controlled Study ◽  
Diabetic Patients ◽  
Artery Disease

The aim of the present study is to investigate the prevalance of chronic kidney disease (CKD), of cardiovascular disease (CVD) and dyslipidemia in patients with diabetes mellitus (DM). We conducted a prospective, controlled study involving 420 diabetic patients (120 T1DM, 300 T2DM) and investigate the following aspects: the presence of vascular complications (stroke, coronary artery disease, peripheral artery disease), lipid profile (total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides), kidney function (glomerular filtration rate, albuminuria), blood pressure, HbA1C. The results that in diabetic patients with CKD there is an increased prevalence of CVD and of dislipidemia. Also we noticed a negative correlation between total cholesterol level and decease in eGFR in all patients, with or without CKD.

Statins and Inflammation in Cardiovascular Disease

2018 ◽  
Vol 23 (46) ◽  
pp. 7027-7039 ◽  
Author(s):  
Georgia Vogiatzi ◽  
Evangelos Oikonomou ◽  
Gerasimos Siasos ◽  
Sotiris Tsalamandris ◽  
Alexandros Briasoulis ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Lipid Lowering ◽  
Hmg Coa Reductase ◽  
Ample Evidence ◽  
Reductase Inhibitors ◽  
Immune System Activation ◽  
Hmg Coa Reductase Inhibitors ◽  
Anti Inflammatory ◽  
Artery Disease ◽  
Coa Reductase

Background: Chronic inflammation and immune system activation underlie a variety of seemingly unrelated cardiac conditions including not only atherosclerosis and the subsequent coronary artery disease but also peripheral artery disease, hypertension with target organ damage and heart failure. The beneficial effects of HMG-CoA reductase inhibitors or statins are mainly attributed to their ability to inhibit hepatic cholesterol biosynthesis. Beyond their lipid lowering activity, ample evidence exists in support of their potent anti-inflammatory properties which initiate from the inhibition of GTPase isoprenylation, activating a cataract of secondary pathways and extend to the inhibition and blocking of immune cell activation and interaction. </P><P> Objective: To summarize the anti-inflammatory mechanisms of statins in clinical and experimental settings in cardiovascular disease. </P><P> Methods: A systematic search of PubMed and the Cochrane Database was conducted in order to identify the majority of trials, studies, current guidelines and novel articles related to the subject. </P><P> Results: In vitro, statins have immuno-modulatory and anti-inflammatory effects, and they can exert antiatherosclerotic effects independently of their hypolipidemic actions. In addition, positive results have emerged from mechanistic and experimental studies on the active role of HMG-CoA reductase inhibitors in HF. By extrapolating those data in clinical setting, we further understand how HMG-CoA reductase inhibitors can beneficially affect not only systolic but also diastolic HF. </P><P> Conclusion: In this review article, we present the basic pathophysiologic data supporting the anti-inflammatory actions of statins in clinical and experimental settings and we link these mechanisms with confirmatory clinical data on the potent non lipid lowering effects of HMG-CoA reductase inhibitors.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals Impact of established cardiovascular disease on 10-year death after coronary revascularization for complex coronary artery disease

2021 ◽  
Author(s):  
Rutao Wang ◽  
Scot Garg ◽  
Chao Gao ◽  
Hideyuki Kawashima ◽  
Masafumi Ono ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Revascularization ◽  
Long Term Outcomes ◽  
Vital Status ◽  
Increased Risk ◽  
Artery Disease ◽  
The Impact

Abstract Aims To investigate the impact of established cardiovascular disease (CVD) on 10-year all-cause death following coronary revascularization in patients with complex coronary artery disease (CAD). Methods The SYNTAXES study assessed vital status out to 10 years of patients with complex CAD enrolled in the SYNTAX trial. The relative efficacy of PCI versus CABG in terms of 10-year all-cause death was assessed according to co-existing CVD. Results Established CVD status was recorded in 1771 (98.3%) patients, of whom 827 (46.7%) had established CVD. Compared to those without CVD, patients with CVD had a significantly higher risk of 10-year all-cause death (31.4% vs. 21.7%; adjusted HR: 1.40; 95% CI 1.08–1.80, p = 0.010). In patients with CVD, PCI had a non-significant numerically higher risk of 10-year all-cause death compared with CABG (35.9% vs. 27.2%; adjusted HR: 1.14; 95% CI 0.83–1.58, p = 0.412). The relative treatment effects of PCI versus CABG on 10-year all-cause death in patients with complex CAD were similar irrespective of the presence of CVD (p-interaction = 0.986). Only those patients with CVD in ≥ 2 territories had a higher risk of 10-year all-cause death (adjusted HR: 2.99, 95% CI 2.11–4.23, p < 0.001) compared to those without CVD. Conclusions The presence of CVD involving more than one territory was associated with a significantly increased risk of 10-year all-cause death, which was non-significantly higher in complex CAD patients treated with PCI compared with CABG. Acceptable long-term outcomes were observed, suggesting that patients with established CVD should not be precluded from undergoing invasive angiography or revascularization. Trial registration SYNTAX: ClinicalTrials.gov reference: NCT00114972. SYNTAX Extended Survival: ClinicalTrials.gov reference: NCT03417050. Graphic abstract

scholarly journals All-cause mortality and location of death in patients with established cardiovascular disease before, during, and after the COVID-19 lockdown: a Danish Nationwide Cohort Study

2021 ◽  
Author(s):  
Jawad H Butt ◽  
Emil L Fosbøl ◽  
Thomas A Gerds ◽  
Charlotte Andersson ◽  
Kristian Kragholm ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Location Of Death ◽  
All Cause Mortality ◽  
History Of ◽  
Adjusted Incidence Rate ◽  
Artery Disease ◽  
Adjusted Incidence ◽  
Overall Mortality

Abstract Background On 13 March 2020, the Danish authorities imposed extensive nationwide lockdown measures to prevent the spread of the coronavirus disease 2019 (COVID-19) and reallocated limited healthcare resources. We investigated mortality rates, overall and according to location, in patients with established cardiovascular disease before, during, and after these lockdown measures. Methods and results Using Danish nationwide registries, we identified a dynamic cohort comprising all Danish citizens with cardiovascular disease (i.e. a history of ischaemic heart disease, ischaemic stroke, heart failure, atrial fibrillation, or peripheral artery disease) alive on 2 January 2019 and 2020. The cohort was followed from 2 January 2019/2020 until death or 16/15 October 2019/2020. The cohort comprised 340 392 and 347 136 patients with cardiovascular disease in 2019 and 2020, respectively. The overall, in-hospital, and out-of-hospital mortality rate in 2020 before lockdown was significantly lower compared with the same period in 2019 [adjusted incidence rate ratio (IRR) 0.91, 95% confidence interval (CI) CI 0.87–0.95; IRR 0.95, 95% CI 0.89–1.02; and IRR 0.87, 95% CI 0.83–0.93, respectively]. The overall mortality rate during and after lockdown was not significantly different compared with the same period in 2019 (IRR 0.99, 95% CI 0.97–1.02). However, the in-hospital mortality rate was lower and out-of-hospital mortality rate higher during and after lockdown compared with the same period in 2019 (in-hospital, IRR 0.92, 95% CI 0.88–0.96; out-of-hospital, IRR 1.04, 95% CI1.01–1.08). These trends were consistent irrespective of sex and age. Conclusions Among patients with established cardiovascular disease, the in-hospital mortality rate was lower and out-of-hospital mortality rate higher during lockdown compared with the same period in the preceding year, irrespective of age and sex.

scholarly journals Part-Aware Mask-Guided Attention for Thorax Disease Classification

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 653
Author(s):  
Ruihua Zhang ◽  
Fan Yang ◽  
Yan Luo ◽  
Jianyi Liu ◽  
Jinbin Li ◽  
...  
Keyword(s):  
Visual Cues ◽  
Learning Strategy ◽  
Feature Learning ◽  
Classification Performance ◽  
Disease Classification ◽  
Feature Representations ◽  
Chest X Ray ◽  
Global And Local ◽  
Aided Diagnosis ◽  
Single Organ

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.

scholarly journals The Relationship between Adult Height and Blood Pressure

Cardiology ◽  
2021 ◽  
pp. 1-6
Author(s):  
John Michael Cochran ◽  
Vincent R. Siebert ◽  
Jeffrey Bates ◽  
Djenita Butulija ◽  
Anna Kolpakchi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Adult Height ◽  
Augmentation Index ◽  
Epidemiological Data ◽  
Systemic Review ◽  
Inverse Association ◽  
Literature Evidence ◽  
Artery Disease ◽  
The Relationship

Background: Identification and modification of cardiovascular risk factors is paramount to reducing cardiovascular disease morbidity and mortality. Hypertension is a major risk factor for cardiovascular disease, but its association with height remains largely underrecognized. Objectives: The objective of this manuscript is to review the evidence examining the association between blood pressure and human stature and to summarize the plausible pathophysiological mechanisms behind such an association. Methods: A systematic review of adult human height and its association with hypertension and coronary artery disease was undertaken. The literature evidence is summarized and tabulated, and an overview of the pathophysiological basis for this association is presented. Results: Shorter arterial lengths found in shorter individuals may predispose to hypertension in a complex hemodynamic interplay, which is explained predominantly by summated arterial wave reflections and an elevated augmentation index. Our systemic review suggests that an inverse relationship between adult height and blood pressure exists. However, differences in the studied populations and heterogeneity in the methods applied across the various studies limit the generalizability of these findings and their clinical application. Conclusion: Physiological studies and epidemiological data suggest a potential inverse association between adult height and blood pressure. Further research is required to define the relationship more clearly between adult height and blood pressure and to assess whether antihypertensive therapeutic approaches and goals should be modified according to patients’ heights.

scholarly journals A muscle synergies-based movements detection approach for recognition of the wrist movements

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Aida Masoumdoost ◽  
Reza Saadatyar ◽  
Hamid Reza Kobravi
Keyword(s):  
Decision Fusion ◽  
Classification Performance ◽  
Muscle Synergy ◽  
Fusion Algorithm ◽  
Main Research ◽  
Main Challenge ◽  
Detection Approach ◽  
Average Accuracy ◽  
Bayesian Fuzzy Clustering ◽  
Research Challenge

Abstract Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively.

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