Capacitive on-line hematocrit sensor design based on impedance spectroscopy for use in hemodialysis machines

Trebbels, Dennis; Hradetzky, David; Zengerle, Roland

doi:10.1109/iembs.2009.5332420

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…The value of Xc is frequency-dependent and is described as capacitive resistance, which is inversely related to frequency and CAP as shown in the following equation: Reactance (Ohms) = 1/(2 × π × Frequency (Hz) × CAP (Farads)). 8 It was previously demonstrated by Trebbels and colleagues 34 that at a given temperature, variable red blood cell content (hematocrit), in a constant volume of saline solution, has a linear relation with CAP. Ultimately, based on this finding, we can assume that Xc/Hs is indirectly related to the cell content in a water solution, which supports its translational value as a biomarker of cell integrity or quality.…”

Section: Discussionmentioning

confidence: 98%

Prediction of body water compartments by raw bioelectrical impedance parameters in athletes: Comparison between series and parallel measurements

Francisco

Jesus

Nunes

et al. 2023

Scandinavian Med Sci Sports

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BackgroundThe aim of this study was to determine the predictive role of series and parallel bioelectrical impedance‐derived parameters in predicting total body (TBW), intracellular (ICW), and extracellular water (ECW) in athletes.MethodsThis cross‐sectional study analyzed 134 male (21.33 ± 5.11 years) and 64 female (20.45 ± 5.46 years) athletes. Using dilution techniques, TBW and ECW were determined while ICW was the difference between both. Raw and standardized for height (/H) bioelectrical resistance (R), reactance (Xc), and impedance (Z) values were obtained using a phase‐sensitive device at a single frequency in a series array (s). These were mathematically transformed in a parallel array (p) and capacitance (CAP). Fat‐free mass (FFM) was assessed by dual‐energy X‐ray absorptiometry.ResultsMultiple regressions adjusted for age and FFM show that R/Hs, Z/Hs, R/Hp, and Z/Hp were significant predictors of TBW (p < 0.001 in females and males). While Xc/Hs did not predict ICW, Xc/Hp was a predictor (p < 0.001 in females and Males). In females, R/H and Z/H predicted similarly TBW, ICW, and ECW. In males, R/Hs was considered a better predictor than R/Hp for TBW and ICW, and the Xc/Hp was considered the best predictor for ICW. Another significant predictor of ICW was CAP (p < 0.001 in females and males).ConclusionThis study highlights the potential value of parallel bioelectrical impedance values to identify fluid compartments in athletes as an alternative to the regularly used series measurements. Moreover, this study supports Xc in parallel, and ultimately CAP, as valid indicators of cell volume.

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Section: Discussionmentioning

confidence: 98%

Prediction of body water compartments by raw bioelectrical impedance parameters in athletes: Comparison between series and parallel measurements

Francisco

Jesus

Nunes

et al. 2023

Scandinavian Med Sci Sports

View full text Add to dashboard Cite

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“…Since RBCs suspended in the blood occupy around 45% of the blood volume, the RBC membrane has an important role in the total tissue impedance [158] and has an effect on the capacitance value in the equivalent circuit of the tissue. In a microscopic view, the electrical behavior of a single red blood cell is modeled as illustrated in Figure 10b [159]. The red blood cell membrane contributes to the capacitance, and the intra and extracellular fluid contribute to the resistance of the electrical model.…”

Section: Bioimpedance Spectroscopymentioning

confidence: 99%

Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Shokrekhodaei

Quinones

2020

Sensors

156

109

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Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

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“…Research has been carried out by Tereul et al [ 18 ] utilizing an ultrasonic sensor to measure blood flow during dialysis sessions. Trebbels et al [ 19 ] measured hematocrit levels by designing impedance-spectroscopy-based sensors for dialysis apparatus. Yi-Chun Du et al [ 20 ] proposed a wearable device to monitor blood leakage during HD using an array sensing patch.…”

Section: Introductionmentioning

confidence: 99%

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data

Thakur

Syed-Abdul

Chiu

et al. 2018

Sensors

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Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.

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Capacitive on-line hematocrit sensor design based on impedance spectroscopy for use in hemodialysis machines

Cited by 11 publications

References 5 publications

Prediction of body water compartments by raw bioelectrical impedance parameters in athletes: Comparison between series and parallel measurements

Prediction of body water compartments by raw bioelectrical impedance parameters in athletes: Comparison between series and parallel measurements

Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data

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