New monitoring method of body fluid status by digital weight scale incorporating bioelectrical impedance analyzer in definite heart failure patients

2008 ◽  
Vol 7 ◽  
pp. 12-12
Author(s):  
H KATAOKA
Keyword(s):  
Heart Failure ◽  
Body Fluid ◽  
Bioelectrical Impedance ◽  
Monitoring Method ◽  
Impedance Analyzer ◽  
Heart Failure Patients ◽  
Fluid Status ◽  
Bioelectrical Impedance Analyzer

scholarly journals Wrist-wearable bioelectrical impedance analyzer with miniature electrodes for daily obesity management

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Myoung Hoon Jung ◽  
Kak Namkoong ◽  
Yeolho Lee ◽  
Young Jun Koh ◽  
Kunsun Eom ◽  
...  
Keyword(s):  
Contact Resistance ◽  
Body Fat ◽  
Bioelectrical Impedance ◽  
The Body ◽  
Upper Body ◽  
Sensor Technology ◽  
Clinical Test ◽  
Obesity Management ◽  
Impedance Analyzer ◽  
Bioelectrical Impedance Analyzer

AbstractBioelectrical impedance analysis (BIA) is used to analyze human body composition by applying a small alternating current through the body and measuring the impedance. The smaller the electrode of a BIA device, the larger the impedance measurement error due to the contact resistance between the electrode and human skin. Therefore, most commercial BIA devices utilize electrodes that are large enough (i.e., 4 × 1400 mm2) to counteract the contact resistance effect. We propose a novel method of compensating for contact resistance by performing 4-point and 2-point measurements alternately such that body impedance can be accurately estimated even with considerably smaller electrodes (outer electrodes: 68 mm2; inner electrodes: 128 mm2). Additionally, we report the use of a wrist-wearable BIA device with single-finger contact measurement and clinical test results from 203 participants at Seoul St. Mary’s Hospital. The correlation coefficient and standard error of estimate of percentage body fat were 0.899 and 3.76%, respectively, in comparison with dual-energy X-ray absorptiometry. This result exceeds the performance level of the commercial upper-body portable body fat analyzer (Omron HBF-306). With a measurement time of 7 s, this sensor technology is expected to provide a new possibility of a wearable bioelectrical impedance analyzer, toward obesity management.

Abstract 17060: Machine Learning and Video Recognition for Automated Detection of Fluid Status in Heart Failure Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pratik Doshi ◽  
John Tanaka ◽  
Jedrek Wosik ◽  
Natalia M Gil ◽  
Martin Bertran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Point Of Care ◽  
Machine Learning Algorithms ◽  
Right Atrial ◽  
Heart Catheterization ◽  
Heart Failure Patients ◽  
Non Invasive ◽  
Fluid Status ◽  
Video Recognition

Introduction: There is a need for innovative solutions to better screen and diagnose the 7 million patients with chronic heart failure. A key component of assessing these patients is monitoring fluid status by evaluating for the presence and height of jugular venous distension (JVD). We hypothesize that video analysis of a patient’s neck using machine learning algorithms and image recognition can identify the amount of JVD. We propose the use of high fidelity video recordings taken using a mobile device camera to determine the presence or absence of JVD, which we will use to develop a point of care testing tool for early detection of acute exacerbation of heart failure. Methods: In this feasibility study, patients in the Duke cardiac catheterization lab undergoing right heart catheterization were enrolled. RGB and infrared videos were captured of the patient’s neck to detect JVD and correlated with right atrial pressure on the heart catheterization. We designed an adaptive filter based on biological priors that enhances spatially consistent frequency anomalies and detects jugular vein distention, with implementation done on Python. Results: We captured and analyzed footage for six patients using our model. Four of these six patients shared a similar strong signal outliner within the frequency band of 95bpm – 200bpm when using a conservative threshold, indicating the presence of JVD. We did not use statistical analysis given the small nature of our cohort, but in those we detected a positive JVD signal the RA mean was 20.25 mmHg and PCWP mean was 24.3 mmHg. Conclusions: We have demonstrated the ability to evaluate for JVD via infrared video and found a relationship with RHC values. Our project is innovative because it uses video recognition and allows for novel patient interactions using a non-invasive screening technique for heart failure. This tool can become a non-invasive standard to both screen for and help manage heart failure patients.

Relationship between Bioelectrical Impedance and Anthropometric Techniques to Determine Body Fat in a Black Pediatric Population

1990 ◽  
Vol 2 (2) ◽  
pp. 140-148 ◽  
Author(s):  
N. Kay Covington ◽  
Darlene A. Kluka ◽  
Phyllis A. Love
Keyword(s):  
Body Fat ◽  
School Environment ◽  
Pediatric Population ◽  
Bioelectrical Impedance ◽  
Black Children ◽  
Impedance Analyzer ◽  
Moment Coefficient ◽  
Percentage Of Body Fat ◽  
Impedance Technique ◽  
Bioelectrical Impedance Analyzer

This investigation compared the percentage of body fat obtained using the bioelectrical impedance technique and the anthropometric technique on a black pediatric population consisting of 196 subjects, 93 girls and 103 boys, ages 5-11 years. Subjects were measured utilizing the Bioelectrical Impedance Analyzer-103 (RJL Systems, Inc.). In order to simulate a realistic school environment, protocol was deliberately not followed. Anthropometric measurements were obtained at two sites: triceps and medial calf. The anthropometric and BIA percentages of body fat were compared using the Pearson product-moment coefficient or correlation and an ANOVA. The overall relationship between the groups was .809. Use of the BIA appears to lead to an overestimation of fatness in black children.

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