scholarly journals Early diagnosis of left ventricular diastolic dysfunction in diabetic patients: A possible role for natriuretic peptides

2011 ◽  
Vol 104 (4) ◽  
pp. 276-277
Author(s):  
F. Simona
Keyword(s):  
Early Diagnosis ◽  
Diastolic Dysfunction ◽  
Natriuretic Peptides ◽  
Left Ventricular Diastolic Dysfunction ◽  
Left Ventricular ◽  
Diabetic Patients ◽  
Ventricular Diastolic Dysfunction

scholarly journals Echocardiographic evaluation of left ventricular diastolic dysfunction in recently diagnosed type 2 diabetes mellitus

2018 ◽  
Vol 6 (5) ◽  
pp. 1691
Author(s):  
Swapnil Jain ◽  
C. L. Nawal ◽  
Amandeep Singh ◽  
Radhey Shyam Chejara ◽  
Sagar Barasara ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diastolic Dysfunction ◽  
Left Ventricular Diastolic Dysfunction ◽  
Left Ventricular ◽  
P Value ◽  
Diabetic Patients ◽  
Ventricular Diastolic Dysfunction

Background: Diastolic dysfunction in patients suffering from diabetes mellitus represents an earlier stage in the natural history of cardiomyopathy. This study was done to assess the left ventricular diastolic dysfunction in recently diagnosed (<5yr) Type 2 Diabetes Mellitus by Echocardiography and also to determine association of glycemic status (by HBA1c levels) with left ventricular diastolic dysfunction (LVDD).Methods: An observational descriptive study involving 100 diabetic patients, taken on first come first serve basis after applying inclusion and exclusion criteria. In all the subjects, other than routine investigations, HbA1c was estimated and echocardiography was done to evaluate LVDD.Results: Mean value of HbA1c in the study was 8.31+ 1.408 %. 63 out of 100 subjects had LVDD. There was significant positive correlation between HbA1c and LVDD (p value <0.001). As HbA1c increased, severity of LVDD increased. In this study, as BMI increased, HbA1c and LVDD increased & both findings were statistically significant (p value =0.001).Conclusion: Our study indicates that myocardial damage in patients with diabetes affects diastolic function before systolic function &higher HbA1C level is strongly associated with presence of LVDD. Patients should be advised strict control of diabetes in order to reduce the risk for developing LVDD which is a precursor for more advanced disease.Keywords: Diabetes mellitus, Diastolic dysfunction, BMI, HbA1c

scholarly journals Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2349
Author(s):  
Yang Yang ◽  
Xing-Ming Guo ◽  
Hui Wang ◽  
Yi-Neng Zheng
Keyword(s):  
Early Diagnosis ◽  
Diastolic Dysfunction ◽  
Data Augmentation ◽  
Ventricular Remodeling ◽  
Left Ventricular Diastolic Dysfunction ◽  
Left Ventricular ◽  
Generative Adversarial Networks ◽  
Denoising Method ◽  
Wall Stiffness ◽  
Ventricular Diastolic Dysfunction

The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

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