Underwater surface electromyography (sEMG) signals are especially of interest for rehabilitation and sports medicine applications. Silver/silver chloride (Ag/AgCl) hydrogel electrodes, although the gold standard for sEMG data collection, require waterproofing for underwater applications. Having to apply waterproof tape over electrodes impedes the deployment of sEMG in immersed conditions. As a better alternative for underwater applications, we have developed carbon black/polydimethylsiloxane (CB/PDMS) electrodes for collecting sEMG signals under water. We recruited twenty subjects to collect simultaneous recordings of sEMG signals using Ag/AgCl and CB/PDMS electrodes on biceps brachii, triceps brachii, and tibial anterior muscles. The Ag/AgCL electrodes were covered in waterproof tape, and the CB/PDMS electrodes were not. We found no differences in sEMG signal amplitudes between both sensors, for the three muscles. Moderate mean correlation between Ag/AgCl and CB/PDMS electrodes was found on the linear envelopes (≥ 0.7); correlation was higher for power spectral densities (≥ 0.84). Ag/AgCl electrodes performed better in response to noise, whilst the CB/PDMS electrodes were more sensitive to myoelectric activity in triceps and tibialis, and exhibited better response to motion artifacts in the measurements on the triceps and tibialis. Results suggest that sEMG signal collection is possible under water using CB/PDMS electrodes without requiring any waterproof or adhesive tape.
The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs to place them daily in the same spots. Textile electrodes can reduce skin irritation; however, they inconveniently require wetting before each use and provide poor adherence to the skin. Previously, we developed waterproof reusable dry carbon black polydimethylsiloxane (CB/PDMS) electrodes that exhibited a good response to motion artifacts. We examined whether these CB/PDMS electrodes were suitable sensing components to be embedded into a monitoring vest for measuring TBI and the electrocardiogram (ECG). We recruited N = 20 subjects to collect TBI and ECG data. The TBI parameters were different between the various types of electrodes. Inter-subject variability for copper-mesh CB/PDMS electrodes and Ag/AgCl electrodes was lower compared to textile electrodes, and the intra-subject variability was similar between the copper-mesh CB/PDMS and Ag/AgCl. We concluded that the copper mesh CB/PDMS (CM/CB/PDMS) electrodes are a suitable alternative for textile electrodes for TBI measurements, but with the benefit of better skin adherence and without the requirement of wetting the electrodes, which can often be forgotten by the stressed HF subjects.
Background Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. Objective We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black–polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). Methods We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. Results Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 – R∞, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 – R∞ had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 – R∞: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. Conclusions This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.
BACKGROUND Clinically, the most important signs and symptoms of acute decompensated heart failure (ADHF) relate to accumulation of excess body fluid, but autonomic dysregulation is another characteristic feature of ADHF physiology. Transthoracic bioimpedance (TBI) is a non-invasive, simple method for measuring fluid retention in lungs. Heart rate variability (HRV) is another widely used noninvasive tool to assess autonomic function. We hypothesize that TBI and HRV can be used for detection of fluid accumulation in ADHF participants. OBJECTIVE In this paper, we aimed to evaluate the performance of TBI and HRV parameters obtained using a fluid accumulation vest (FAV) with dry carbon black polydimethylsiloxane (CB-PDMS) electrodes in a prospective clinical study ‘System for Heart-failure Identification using an External Lung-fluid Device’ (S.H.I.E.L.D.). METHODS We computed fifteen parameters, eight calculated from the model to fit Cole-Cole plot from TBI measurements (R0, RI, R∞, R0 - R∞, FE, fc, α, and Cm), and seven based on linear (mean HR, HRVLF, HRVHF, HRVLFn, HRVHFn) and nonlinear (PDMSymp, and PDMPSymp) analysis of HRV. We compared the values of these parameters between three groups of participants: Control (non-HF hospitalized participants), Baseline (ADHF participants’ recordings taken at the time of admittance to the hospital), and Discharge (ADHF participants’ recordings acquired at the time of discharge from hospital). RESULTS Among the fifteen parameters, two TBI (R0 and R0-R∞) and three HRV (HRVHF, HRVLFn, and HRVHFn) parameters were found to be the most discriminatory between non-HF and ADHF groups. The two TBI parameters had statistically significantly lower values for ADHF participants than for non-HF participants, which is an indicator that accumulated fluids in the lungs are of higher volume for HF participants. We used several machine learning approaches to classify participants with fluid accumulation (Baseline ADHF) and without fluid accumulation (Control and ADHF participants at discharge), termed Wet vs. Dry groups, respectively. A cubic support vector machine model using TBI and HRV parameters achieved an accuracy of 92% classifying Wet and Dry groups. Looking at the parameters included in the model, the TBI parameters are related to intra and extra-cellular fluid, whereas the HRV parameters are mostly related to sympathetic activation. CONCLUSIONS This is useful, for instance, to provide in-home diagnostic wearable vest that can detect or predict fluid accumulation in HF participants. Results suggest that fluid accumulation, detection, and subsequently ADHF detection, could be performed using TBI and HRV measurements acquired with a wearable vest.
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