Electrical Bio-impedance Signal Research Articles

Sensor-Location-Specific Joint Acquisition of Peripheral Artery Bioimpedance and Photoplethysmogram for Wearable Applications.

Cardiovascular diseases (CVDs), being the culprit for one-third of deaths globally, constitute a challenge for biomedical instrumentation development, especially for early disease detection. Pulsating arterial blood flow, providing access to cardiac-related parameters, involves the whole body. Unobtrusive and continuous acquisition of electrical bioimpedance (EBI) and photoplethysmography (PPG) constitute important techniques for monitoring the peripheral arteries, requiring novel approaches and clever means. In this work, five peripheral arteries were selected for EBI and PPG signal acquisition. The acquisition sites were evaluated based on the signal morphological parameters. A small-data-based deep learning model, which increases the data by dividing them into cardiac periods, was proposed to evaluate the continuity of the signals. The highest sensitivity of EBI was gained for the carotid artery (0.86%), three times higher than that for the next best, the posterior tibial artery (0.27%). The excitation signal parameters affect the measured EBI, confirming the suitability of classical 100 kHz frequency (average probability of 52.35%). The continuity evaluation of the EBI signals confirmed the advantage of the carotid artery (59.4%), while the posterior tibial artery (49.26%) surpasses the radial artery (48.17%). The PPG signal, conversely, commends the location of the posterior tibial artery (97.87%). The peripheral arteries are highly suitable for non-invasive EBI and PPG signal acquisition. The posterior tibial artery constitutes a candidate for the joint acquisition of EBI and PPG signals in sensor-fusion-based wearable devices-an important finding of this research.

Bayesian source separation of electrical bioimpedance signals

For physicians, it is often crucial to monitor hemodynamic parameters to provide appropriate treatment for patients. Such hemodynamic parameters can be estimated via electrical bioimpedance (EBI) signal measurements. Time dependent changes of the measured EBI signal occur due to several different phenomena in the human body. Most of the time one is just interested in a single component of the EBI signal, such as the part caused by cardiac activities, wherefore it is necessary to decompose the EBI signal into its different source terms. The changes of the signal are mostly caused by respiration and cardiac activity (pulse). Since these fluctuations are periodic in sufficiently small time windows, the signal can be approximated by a harmonic series with two different fundamental frequencies and an unknown number of higher harmonics. In this work, we present Bayesian Probability Theory as the adequate and rigorous method for this decomposition. The proposed method allows, in contrast to other methods, to consistently identify the model-function, compute parameter estimates and predictions, and to quantify uncertainties. Further, the method can handle a very low signal-to-noise ratio. The results suggest that EBI-based estimation of hemodynamic parameters and their monitoring can be improved and its reliability assessed.

A method to detect heart rate based on electrical bio-impedance

As a basic health indicator, heart rate has been widely used in clinical measurement and daily health care. Electrical bio-impedance (EBI) measurement provides non-invasive method for heart rate detection. Therefore, this paper proposed a method to detect heart rate based on EBI. With the BIOPAC EBI module, the signal can be de-noised in real-time. Finally, the de-noised EBI signal is used to compute heart rate. Four electrodes are located at radial artery of left upper limb in this method. The result proves that this method has high accuracy on heart rate measurement.

Electrical bioimpedance and other techniques for gastric emptying and motility evaluation

The aim of this article is to identify non-invasive, inexpensive, highly sensitive and accurate techniques for evaluating and diagnosing gastric diseases. In the case of the stomach, there are highly sensitive and specific methods for assessing gastric motility and emptying (GME). However, these methods are invasive, expensive and/or not technically feasible for all clinicians and patients. We present a summary of the most relevant international information on non-invasive methods and techniques for clinically evaluating GME. We particularly emphasize the potential of gastric electrical bioimpedance (EBI). EBI was initially used mainly in gastric emptying studies and was essentially abandoned in favor of techniques such as electrogastrography and the gold standard, scintigraphy. The current research evaluating the utility of gastric EBI either combines this technique with other frequently used techniques or uses new methods for gastric EBI signal analysis. In this context, we discuss our results and those of other researchers who have worked with gastric EBI. In this review article, we present the following topics: (1) a description of the oldest methods and procedures for evaluating GME; (2) an explanation of the methods currently used to evaluate gastric activity; and (3) a perspective on the newest trends and techniques in clinical and research GME methods. We conclude that gastric EBI is a highly effective non-invasive, easy to use and inexpensive technique for assessing GME.

Bio-impedance signal decomposer: enhanced accuracy and reduced latency solution

The paper presents an overview of the electrical bio-impedance (EBI) signal decomposi- tion into its cardiac and respiratory components. This problem mainly originates from the non- stationarity of the signal components and overlapping of their harmonic spectra. In the introductory part of the paper, an overview of the bio-impedance signal decomposer (BISD), as a solution of the problem, is accompanied with an introduction to a cardiac BI signal model, which is constructed from the components of the application-specific orthonormal basis. In the main part of the paper a semi-synchronous cardiac signal amplitude estimator, which is based on the cardiac signal model and on the proposed extrema searching algorithm, is proposed. After that, the cardiac signal lock-in detection algorithm is proposed. Finally, a conditioning of the estimated cardiac signal frequency is discussed. The proposed amplitude estimator, lock-in detector and frequency conditioning increase twice the reaction speed of the BISD to the input EBI signal. The proposed version of the BISD estimates the cardiac signal amplitude during only a few cardiac periods, even if very large difference between amplitudes exists in different conditions. As a result, the entire BISD becomes locked during 8 s (including 4 s of soft start). The proposed improvements allowed reducing the latency of the BISD from 2 to 1 s.

Effects of metoclopramide on gastric motility measured by short-term bio-impedance

To analyze the accuracy of short-term bio-impedance as a means of measuring gastric motility. We evaluated differences in the short-term electrical bio-impedance signal from the gastric region in the following conditions: (1) fasting state, (2) after the administration of metoclopramide (a drug that induces an increase in gastric motility) and (3) after food ingestion in 23 healthy volunteers. We recorded the real component of the electrical impedance signal from the gastric region for 1000 s. We performed a Fast Fourier Transform (FFT) on this data and then compared the signal among the fasting, medicated, and postprandial conditions using the median of the area under the curve, the relative area under the curve and the main peak activity. The median of the area under the curve of the frequency range in the region between 2-8 cycles per minute (cpm) decreased from 4.7 cpm in the fasting condition to 4.0 cpm in the medicated state (t = 3.32, P = 0.004). This concurred with the decrease seen in the relative area under the FFT curve in the region from 4 to 8 cpm from 38.3% to 26.6% (t = 2.81, P = 0.012) and the increase in area in the region from 2 to 4 cpm from 22.4% to 27.7%, respectively (t = -2.5, P = 0.022). Finally the main peak position also decreased in the region from 2 to 8 cpm. Main peak activity in the fasting state was 4.72 cpm and declined to 3.45 cpm in the medicated state (t = 2.47, P = 0.025). There was a decrease from the fasting state to the postprandial state at 3.02 cpm (t = 4.0, P = 0.0013). Short-term electrical bio-impedance can assess gastric motility changes in individuals experiencing gastric stress by analyzing the area medians and relative areas under the FFT curve.

Decomposition method of an electrical bio-impedance signal into cardiac and respiratory components

The paper presents a method for adaptive decomposition of an electrical bio-impedance (BI) signal into two components: cardiac and respiratory. The decomposition of a BI signal is not a trivial process because of the non-stationarity of the signal components and overlapping of their harmonic spectra. An application specific orthonormal basis (ASOB) was designed to solve the decomposition task using the Jacobi weighting function in the standard Gram–Schmidt process. The key element of the bio-impedance signal decomposer (BISD) is a model of the cardiac BI signal, which is constructed from the components of the ASOB and is intended for use in the BISD for on-line tracking of the cardiac BI signal. It makes it possible to separate the cardiac and respiratory components of the total BI signal in non-stationary conditions. In combination with the signal-shape locked loop (SSLL), the BISD allows us to decompose the BI signals with partially overlapping spectra. The proposed BISD based method is accomplished as a PC software digital system, but it is oriented towards applications in portable and stationary cardiac devices and in clinical settings.

Trending of impedance-monitored cardiac variables

The NCCOM3-R6 monitor continuously monitors cardiac output and five other cardiovascular variables from the thoracic electrical bioimpedance signal. We averaged data over 5-min intervals for 130 min in 100 control studies in 40 pediatric ICU patients, age 0.04 to 20.39 yr (median 1.39) and weighing 2.0 to 59.5 kg (median 8.8). For individual studies, 99% of the 5-min averages of cardiac output fell within +/- 44% of the baseline cardiac output for that study. Normal ranges were somewhat narrower for the other five variables. When we averaged data for 100 studies, 5-min interval observations for each variable did not deviate from baseline over a 2-h period (p greater than .70). With a sample size of 100 studies, we could detect a change in cardiac output of +/- 5% at the p less than .005 level with a power of 0.95. We conclude that with a sufficiently large sample size, studies employing the NCCOM3 can detect clinically significant cardiovascular changes due to pharmacologic or procedural stressors.