Bioimpedance Signal Research Articles

OPPORTUNITY AND PLANNING BIM-ANALYSIS FOR MONITORING BLOOD

Results of the study of the possibility of using the results of the bioimpedanceometry method for monitoring hidden and spontaneous bleeding are presented. The current problem of accounting for blood loss during resuscitation, surgery and rehabilitation activities is shown. The use, as informative parameters, of the spectral properties of the bioimpedance signal, namely, the coefficient of inter-spectral correlation of the original measurement signal and its linear transformation is proposed. In work, the existing models substantiating the prospects of using the bioimpedance method for such problems are considered in detail, and the calculated empirical expressions for determining the volume of liquid segments of the human body are given. The mathematical substantiation of the method of inter-spectral correlation based on the calculation of the correlation coefficients of the wavelet-decomposition coefficients of the original signal and its linear transformation is given. The results of experimental studies on the approbation of this method for fixed venous blood sampling are presented in the work. Using criterial T-statistics, a quantitative evaluation of the effectiveness of the options for choosing the spatial placement of measuring electrodes in bioimpedanceometry was carried out. The obtained results allow to draw a conclusion about the possibility and prospects of the proposed method for real-time monitoring of the onset of latent and spontaneous hemorrhages, and also make it possible to formulate recommendations for placement of measuring electrodes on the patient's body.

In-vivo Measurements of Tissue Impeditivity by Electrical Impedance Spectroscopy.

The electrical properties of biological tissues differ depending on their structural characteristics. In literature, a lot of study have been carried out with the intent of taking advantage of bioimpedance analysis. Unfortunately, many apparatuses used during these evaluations were not always designed for measurements on living tissues. As a consequence, data could be affected by electrode polarization. In 2016, we presented a new impedance meter, developed for measurements on living tissues. Initially, it was tested only on ex-vivo rabbit's tissues with promising results. As a continuation, this device has been tested on in-vivo samples by placing a needle-probe into 3 tissues (liver, spleen, ovary) of 2 female dogs. Furthermore, was evaluated also the bioimpedance signal of the ovary explanted, comparing it with the in-vivo data. Bioimpedance was analyzed in terms of modulus and phase along a broad spectrum of frequencies (10Hz - 10kHz).Data obtained confirm the possibility of discriminating among the 3 tested tissues, at high frequencies for modulus and at low for phase. Confirmation that values on in-vivo and exvivo tissues are comparable if detected within few minutes after the explant, is also reported. We conclude that this clinical evaluation confirmed, also in-vivo, the good performance of the device previously tested on ex-vivo tissues, and provide more information about the tissue properties and characteristics.

A 55.77 μW Bio-impedance Sensor with 276 μs Settling Time for Portable Blood Pressure Monitoring System

An ultra-low power bio-impedance sensor for wrist watch-type blood pressure monitoring system is implemented in 0.18 μm CMOS technology under 1.8 V supply. The duty-cycled current generator (DCG) enables low power and high amplitude current injection with 93.58% power reduction in a current generator block. A fast-settling readout circuit with DC balanced amplifier reduces settling time to 276 μs, allowing bio-impedance signal acquisition even when the motion artifact induced circuit transient occurs. The simulated 2.125 mm² single chip bio-impedance sensor consumes only 55.77 μW.

Wearable Multimodal Skin Sensing for the Diabetic Foot

Ulceration of the diabetic foot is currently difficult to detect reliably in a timely manner causing undue suffering and cost. Current best practice is for daily monitoring by those living with diabetes coupled to scheduled monitoring by the incumbent care provider. Although some metrics have proven useful in the detection or prediction of ulceration, no single metric can currently be relied upon for diagnosis. We have developed a prototype multivariate extensible sensor platform with which we demonstrate the ability to gather acceleration, rotation, galvanic skin response, environmental temperature, humidity, force, skin temperature and bioimpedance signals in real time, for later analysis, utilising low cost Raspberry Pi and Arduino devices. We demonstrate the utility of the Raspberry Pi computer in research which is of particular interest to this issue of electronics—Raspberry Pi edition. We conclude that the hardware presented shows potential as an adaptable research tool capable of gathering synchronous data over multiple sensor modalities. This research tool will be utilised to optimise sensor selection, placement and algorithm development prior to translation into a sock, insole or platform diagnostic device at a later date. The combination of a number of clinically relevant parameters is expected to provide greater understanding of tissue state in the foot but requires further volunteer testing and analysis beyond the scope of this paper which will be reported in due course.

Subclinical pulmonary congestion is prevalent in nephrotic syndrome.

In patients with nephrotic syndrome (NS), the lung is considered an organ protected from the risk of edema. However, data on objectively measured lung water in NS patients is lacking. Here we measured lung water by an ultrasound (US) technique as well as by transthoracic impedance in 42 asymptomatic patients with active NS, in 14 stage G5D CKD patients on chronic hemodialysis, and in 21 healthy individuals. In patients with active NS, the median number of US-B lines (a metric of lung water) after 5 min in a supine position was significantly higher (12; interquartile range: 7-25) compared with that in healthy individuals (4; 2-9) but similar to that in hemodialysis patients (23; 10-39). The difference between NS patients and healthy individuals was significantly amplified (16; 10-35 vs. 4; 2-9) after 60 min of supine resting and significantly attenuated after 5 min of standing (10; 7-25 vs. 3; 1-6). Posture-dependent changes in lung water in patients with active NS were significantly accentuated compared with both hemodialysis patients and healthy individuals. After NS remission, the number of US-B lines was significantly reduced to 5 (4-18) at 5 min and to 6 (5-22) at 60 min approaching the normal range. Lung congestion in patients with active NS was confirmed by transthoracic impedance. Thus, asymptomatic pulmonary congestion is pervasive in patients with NS. A clinical trial is needed to assess the utility of lung US for the management of patients with NS.

A parametric framework for the development of bioelectrical applications: application to a bio-impedance signal simulator

A parametric framework for the development of bioelectrical applications: application to a bio-impedance signal simulator

Wavelet-based multiscale analysis of bioimpedance data measured by electric cell-substrate impedance sensing for classification of cancerous and normal cells.

The paper presents a study to differentiate normal and cancerous cells using label-free bioimpedance signal measured by electric cell-substrate impedance sensing. The real-time-measured bioimpedance data of human breast cancer cells and human epithelial normal cells employs fluctuations of impedance value due to cellular micromotions resulting from dynamic structural rearrangement of membrane protrusions under nonagitated condition. Here, a wavelet-based multiscale quantitative analysis technique has been applied to analyze the fluctuations in bioimpedance. The study demonstrates a method to classify cancerous and normal cells from the signature of their impedance fluctuations. The fluctuations associated with cellular micromotion are quantified in terms of cellular energy, cellular power dissipation, and cellular moments. The cellular energy and power dissipation are found higher for cancerous cells associated with higher micromotions in cancer cells. The initial study suggests that proposed wavelet-based quantitative technique promises to be an effective method to analyze real-time bioimpedance signal for distinguishing cancer and normal cells.

Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones.

Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

Cooperative dry-electrode sensors for multi-lead biopotential and bioimpedance monitoring

Cooperative sensors is a novel measurement architecture that allows the acquiring of biopotential signals on patients in a comfortable and easy-to-integrate manner. The novel sensors are defined as cooperative in the sense that at least two of them work in concert to measure a target physiological signal, such as a multi-lead electrocardiogram or a thoracic bioimpedance.This paper starts by analysing the state-of-the-art methods to simultaneously measure biopotential and bioimpedance signals, and justifies why currently (1) passive electrodes require the use of shielded or double-shielded cables, and (2) active electrodes require the use of multi-wired cabled technologies, when aiming at high quality physiological measurements.In order to overcome the limitations of the state-of-the-art, a new method for biopotential and bioimpedance measurement using the cooperative sensor is then presented. The novel architecture allows the acquisition of the aforementioned biosignals without the need of shielded or multi-wire cables by splitting the electronics into separate electronic sensors comprising each of two electrodes, one for voltage measurement and one for current injection. The sensors are directly in contact with the skin and connected together by only one unshielded wire. This new configuration requires one power supply per sensor and all sensors need to be synchronized together to allow them to work in concert.After presenting the working principle of the cooperative sensor architecture, this paper reports first experimental results on the use of the technology when applied to measuring multi-lead ECG signals on patients. Measurements performed on a healthy patient demonstrate the feasibility of using this novel cooperative sensor architecture to measure biopotential signals and compliance with common mode rejection specification accordingly to international standard (IEC 60601-2-47) has also been assessed.By reducing the need of using complex wiring setups, and by eliminating the presence of central recording devices (cooperative sensors directly sense and store the measured biosignals on the site), the depicted novel technology is a candidate to a novel generation of highly-integrated, comfortable and reliable technologies that measure physiological signals in real-life scenarios.

Integrated All Electrical Pulse Wave Velocity and Respiration Sensors Using Bio-Impedance

In this paper, we propose wire-free integrated sensors that monitor pulse wave velocity (PWV) and respiration, both non-electrical vital signs, by using an all-electrical method. The key techniques that we employ to obtain all-electrical and wire-free measurement are bio-impedance (BI) and analog-modulated body-channel communication (BCC), respectively. For PWV, time difference between ECG signal from the heart and BI signal from the wrist is measured. To remove wires and avoid sampling rate mismatch between ECG and BI sensors, ECG signal is sent to the BI sensor via analog BCC without any sampling. For respiration measurement, BI sensor is located at the abdomen to detect volume change during inhalation and exhalation. A prototype chip fabricated in 0.11 μm CMOS process consists of ECG, BI sensor and BCC transceiver. Measurement results show that heart rate and PWV are both within their normal physiological range. The chip consumes 1.28 mW at 1.2 V supply while occupying 5 mm×2.5 mm of area.

A New Method for Cardiac Diseases Diagnosis

The objective of this work is to perform automatic diagnosis using a non invasive method which consists on the bioimpedance signal processing. Bioimpedance signal (BIS) represents the aorta impedance variation during the heart cycle activity. BIS is detected by mean of two electrodes located at the level of the ascendant aorta. Automatic diagnosis method consists on preparing, first, a data base with a set of cepstral parameters of different BIS according to normal case and different cardiac diseases. This data base is composed from n classes Yk corresponding to n diseases. The classification of anonymous individuals is based on the determination of Fisher distance between anonymous disease and class Yk using Fischer formula. Our method permits to calculate seven relevant cepstral parameters. The application of Fisher method has allowed us to perform the diagnosis of five anonymous cases. The major interest of this method is its especially useful for the exploration of cardiovascular system anomalies for emergency cases, children, elderly and pregnant women who can’t support surgical operations especially at the level of the heart.

Frequency Analysis of Bioimpedance Variations in Human Bladder during Changes of Urine Volume's States

To verify the hypothesis that the variations of bioimpedance can reflect the status of bladder volume during natural bladder filling. We chose 10 healthy, male volunteers by recording different bladder filling stages using bioimpedance and ECG measurement. The signal measurement acquisition device was design by Sun Yat-sen University. Bioimpedance measurement was employed four silver-silver chloride compound electrodes, two of which are stimulating electrodes, and the others are measuring electrodes. Each electrode is attached to the lower abdomen, the mapping position of urinary bladder. Using Fourier analysis, the bioimpedance signals in different stages were analyzed. During natural bladder filling, subjects were asked to report real sensations. Sensations intensity increased with urine volume increase. Bioimpence power spectrum density (PSD) was related with sensation intensity. During natural bladder filling, PSD revealed two parameters with 0.04-0.15 Hz (low frequency, LF), and 0.15-0.4 Hz (high frequency, HF), which were observed under different bladder sensations. In natural bladder filling, the HF spectrum power is increased, in parallel with an inconspicuous change in LF spectrum. With the ECG signal analysis, the reaults revealed spectrum powers were explained by the autonomic nervous regulation. The novel method can assess the states of bladder volume during natural bladder filling.

Complexity quantification of signals from the heart, the macrocirculation and the microcirculation through a multiscale entropy analysis

Quantifying and modelling the cardiovascular system (CVS) represent a challenge to improve our understanding of the CVS. To describe and quantify the CVS, several physiological signals have been analyzed through various signal processing methods. Recently, a quantitative descriptor – the multiscale entropy (MSE) – has been proposed to quantify time series complexity (i.e. the degree of regularity of signal fluctuations) over multiple time scales. Heart rate variability (HRV) signals (i.e. data from the heart) have largely been studied through MSE. By contrast, complexities of signals from the macrocirculation (i.e. elastic and muscular arteries) and the microcirculation (i.e. arterioles and capillaries), two other main components of the CVS, have rarely been investigated simultaneously with MSE. We therefore propose to quantify and compare complexity of signals from these three CVS subsystems: the heart, the macrocirculation and the microcirculation, using MSE.Electrocardiograms, electrical bio-impedance signals (macrocirculation), as well as laser Doppler flowmetry (LDF) signals from finger and forearm (microcirculation) have been recorded simultaneously in nine healthy subjects. The MSE values from these data have been computed and compared.We note a significant lower complexity on scales τ=1, 2 and 3 (i.e. around 1.08Hz, 0.54Hz and 0.36Hz respectively) for LDF signals from the finger compared to the ones of signals from the heart and the macrocirculation. On scale τ=5 (i.e. 0.21Hz), complexity value of signals from the macrocirculation is significantly lower than the ones of HRV and data from the microvascular blood flow in forearm.The three CVS subsystems present different complexity values depending on scales. It could now be of interest to investigate if these complexity differences are due to physiological activities. Moreover, our results could be compared with those obtained from data recorded on patients with vascular diseases.

Assessment of cerebral blood flow autoregulation (CBF AR) with rheoencephalography (REG): studies in animals

The ability of cerebral vasculature to regulate cerebral blood flow (CBF) in the face of changes in arterial blood pressure (SAP) or intracranial pressure (ICP) is an important guard against secondary ischemia in acute brain injuries, and official guidelines recommend that therapeutic decisions be guided by continuous monitoring of CBF autoregulation (AR). The common method for CBF AR monitoring, which rests on real-time derivation of the correlation coefficient (PRx) between slow oscillations in SAP and ICP is, however, rarely used in clinical practice because it requires invasive ICP measurements. This study investigated whether the correlation coefficient between SAP and the pulsatile component of the non-invasive transcranial bioimpedance signal (rheoencephalography, REG) could be used to assess the state and lower limit of CBF AR. The results from pigs and rhesus macaques affirm the utility of REG; however, additional animal and clinical studies are warranted to assess selectivity of automatic REG-based evaluation of CBF AR.

Bioimpedance rise in response to histone deacetylase inhibitor is a marker of mammary cancer cells within a mixed culture of normal breast cells

Detection of a few cancer cells within a complex cellular mixture is a key challenge presented by clinical human biopsy samples. We have designed and tested a microfabricated bioimpedance device that can detect a few human MDA-MB-231 breast cancer cells in a mixed cell culture model of a breast tissue sample. The normal tissue components were modelled using non-cancerous MCF10A human breast epithelial cells and normal human HS68 fibroblasts. The sensor is a silicon chip 0.5 cm in diameter that contains one counter electrode and four 40 μm-wide multi-branched sensing electrodes. The cells' bioimpedances were characterized in pure monocultures and in mixed cell cultures following a brief cultivation on the sensor. After cell seeding, a stable bioimpedance signal was achieved indicative of cell attachment. A cancer-selective bioimpedance signal was elicited by addition of suberoylanilide hydroxamic acid (SAHA), a histone deacetylase inhibitor with selective actions on the cytoskeleton in breast cancer cells. SAHA elicited a 50% rise in peak bioimpedance in MDA-MB-231 breast cancer cells by 15 h. In mixed cultures of MDA-MB-231, MCF10A, and HS68 cells, the contribution of cancer cells present in the mixture dominated impedance response to SAHA. A single adherent cancer cell on any one of four electrodes in a background of ∼100 normal cells resulted in ≥5% increase in bioimpedance. The estimated sensitivity of this device is therefore one cancer cell among a background of 400 normal cells or the equivalent of 25 cancer cells in a biopsy sample of 10 000 cells.

Swallow Detection Algorithm Based on Bioimpedance and EMG Measurements

Image-based swallowing assessment tools like videofluoroscopy and endoscopy allow experts manual investigation of a few individual swallows. However, these tools are expensive and can only be used by clinicians. Systems which utilize easily attachable, inexpensive and non-invasive sensors at the throat could be a real progress for diagnosis and therapy. This contribution investigates the use of a combined electromyography (EMG) and bioimpedance (BI) measurement at the throat to automatically detect swallowing events. The absolute value of the measured BI completely describes the swallowing process, i.e. the closure of the larynx. There is a typical reproducible drop in BI during a swallow. The muscle activity needed for the laryngeal movement during a swallow is measured using EMG. The presented algorithm involves a valley detection in order to perform a segmentation of the BI signal. Additionally, only BI valleys that coincide with EMG activity are selected for feature extraction. In the second part of the algorithm, extracted features of the BI and integrated EMG are fed into a support vector machine (SVM) which is able to separate BI valleys related to swallowing events from valleys which are not caused by swallowing. The detection algorithm has been tested on data from nine healthy subjects. The data set contained 1370 swallows of different bolus sizes and consistency and was effected by other movements and speech. The combined BI/EMG segmentation detected 99.3% of all swallowing events. The subsequently applied classifier showed a sensitivity of 96.1% and a specificity of 97.1% for the test data.

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.

A wireless bioimpedance device for abdominal fatness monitoring

A wireless bioelectrical impedance (bioimpedance) device for assessing abdominal fatness is presented. The proposed device is based on a bioimpedance measurement circuit designed in 0.35-μm CMOS technology and a commercially available ZigBee device, which provides reliable wireless communication. The traditional techniques for measuring bioimpedance signals are either time-consuming or associated with high cost, high power consumption, and large board space and, hence, are not suited to design an unobtrusive monitoring device. A magnitude-ratio method able to evaluate visceral fat accumulation with low influence of subcutaneous fat volume while providing a cost-effective solution is proposed. The presented device achieves less than 1% relative error over the 100-Ω range with a current of 10 μA at 50 kHz.

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.