scholarly journals Microwave System for the Early Stage Detection of Congestive Heart Failure

IEEE Access ◽  
2014 ◽  
Vol 2 ◽  
pp. 921-929 ◽  
Author(s):  
Sasan Ahdi Rezaeieh ◽  
Konstanty S. Bialkowski ◽  
Amin M. Abbosh
Author(s):  
Navneet Singh ◽  
Sazal Patyar ◽  
Naveen Chandra Talniya

 Ascites impairs both the physical and mental dimensions of quality of life in patients. The patients due to unawareness do not report to medical practitioners in the early stage of disease, and also in few cases, medical practitioners due to lack of adequate expertise face difficulty to ensure the early stage detection for causes of ascites, i.e., due to cirrhosis, cancer, congestive heart failure, mycobacterium tuberculosis, or others. Ascites is a symptom of progression of single disease or multiple diseases. Gross collection of fluid in peritoneal cavity may initiate a series of problems such as spontaneous bacterial peritonitis and an increase in abdominal distension and discomfort and hinder the mobility of the patient and dullness and loss of appetite. In the present review, a detail study over the ecology of ascites has been done with emphasizing on diagnosis by history and physical examination, clinical examination, and imagining techniques followed by management of treatment through general guidelines, and various available therapies are covered.


Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 534
Author(s):  
Meng Lei ◽  
Jia Li ◽  
Ming Li ◽  
Liang Zou ◽  
Han Yu

Congestive heart failure (CHF), a progressive and complex syndrome caused by ventricular dysfunction, is difficult to detect at an early stage. Heart rate variability (HRV) was proposed as a prognostic indicator for CHF. Inspired by the success of 2-D UNet++ in medical image segmentation, in this paper, we introduce an end-to-end encoder-decoder model to detect CHF using HRV signals. The developed model enhances the UNet++ model with Squeeze-and-Excitation (SE) residual blocks to extract deep features hierarchically and distinguish CHF patients from normal subjects. Two open-source databases are utilized for evaluating the proposed method, and three segment lengths of intervals between successive R-peaks are employed in comparison with state-of-the-art methods. The proposed method achieves an accuracy of 85.64%, 86.65% and 88.79% when 500, 1000 and 2000 RR intervals are utilized, respectively. It demonstrates that HRV evaluation based on deep learning can be an important tool for early detection of CHF, and may assist clinicians in achieving timely and accurate diagnoses.


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