Search Results (55)

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

Cell membrane capacitance (C_m) is a potential biomarker that reflects the structural and functional integrity of cell membranes. It is essential for physiological processes such as signal transduction, ion transport, and cellular homeostasis. In clinical practice, C_m can be determined using bioimpedance spectroscopy (BIS), a non-invasive technique for analysing the intrinsic electrical properties of biological tissues across a range of frequencies. C_m may be relevant in various clinical fields, where high capacitance is associated with healthy and intact membranes, while low capacitance indicates cellular damage or disease. Despite its promise as a prognostic indicator, several knowledge gaps limit the broader clinical application of C_m. These include variability in measurement techniques (e.g., electrode placement, frequency selection), the lack of standardised measurement protocols, uncertainty on how C_m is related to pathology, and the relatively low amount of C_m research. By addressing these gaps, C_m may become a valuable tool for examining cellular health, early disease detection, and evaluating treatment efficacy in clinical practice. This review explores the fundamental principles of C_m measured with the BIS technique, its mathematical basis and relationship to the biophysical Cole model, and its potential clinical applications. It identifies current gaps in our knowledge and outlines future research directions to enhance the understanding and use of C_m. For example, C_m has shown promise in identifying membrane degradation in sepsis, predicting malnutrition in anorexia nervosa, and as a prognostic factor in cancer. Full article

The monitoring of peripheral electrical bioimpedance (EBI) variations is a promising method that has the potential to replace invasive or burdensome techniques for cardiovascular measurements. Segmental or continuous recording of peripheral pulse waves can serve as a basis for calculating prognostic markers like pulse wave velocity (PWV) or include parameters such as pulse transit time (PTT) or pulse arrival time (PAT) for noninvasive blood pressure (BP) estimation, as well as potentially novel cardiovascular risk indicators. However, several technical, analytical, and interpretative aspects need to be resolved before the EBI method can be adopted in clinical practice. Our goal was to investigate and improve the application of EBI, executing its comparison with other cardiovascular assessment methods in patients hospitalized for coronary catheterization procedures. Methods: We analyzed data from 44 non-acute patients aged 45–74 years who were hospitalized for coronary catheterization at East Tallinn Central Hospital between 2020 and 2021. The radial EBI and electrocardiogram (ECG) were measured simultaneously with central and contralateral pressure curves. The Savitzky–Golay filter was used for signal smoothing. The Hankel matrix decomposer was applied for the extraction of cardiac waveforms from multi-component signals. After extracting the cardiac component, a period detection algorithm was applied to EBI and blood pressure curves. Results: Seven points of interest were detected on the pressure and EBI curves, and four with good representativeness were selected for further analysis. The Spearman correlation coefficient was low for all but the central and distal pressure curve systolic upstroke time points. A high positive correlation was found between PWV measured both invasively and with EBI. The median value of complimentary pulse wave velocity (CPWV), a parameter proposed in the paper, was significantly lower in patients with normal coronaries compared to patients with any stage of coronary disease. Conclusions: With regard to wearable devices, the EBI-derived PAT can serve as a substrate for PWV calculations and cardiovascular risk assessment, although these data require further confirmation. Full article

The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance sensing has stood out as a powerful, non-invasive, and energy-efficient way to track physiological changes and gather important health information. This review looks at the basic principles behind bio-impedance sensing, how it is being built into wearable devices, and its use in healthcare and everyday wellness tracking. We examine recent progress in sensor design, signal processing, and machine learning, and show how these developments are making real-time health monitoring more effective. While bio-impedance systems offer many advantages, they also face challenges, particularly when it comes to making devices smaller, reducing power use, and improving the accuracy of collected data. One key issue is that analyzing bio-impedance signals often relies on complex digital signal processing, which can be both computationally heavy and energy-hungry. To address this, researchers are exploring the use of neuromorphic processors—hardware inspired by the way the human brain works. These processors use spiking neural networks (SNNs) and event-driven designs to process signals more efficiently, allowing bio-impedance sensors to pick up subtle physiological changes while using far less power. This not only extends battery life but also brings us closer to practical, long-lasting health-monitoring solutions. In this paper, we aim to connect recent engineering advances with real-world applications, highlighting how bio-impedance sensing could shape the next generation of intelligent wearable devices. Full article

Objectives: Early detection of cardiovascular diseases and their pre-existing conditions, arteriosclerosis and atherosclerosis, is crucial to increasing a patient’s chance of survival. While imaging technologies and invasive procedures provide a reliable diagnosis, they carry high costs and risks for patients. This study aims to explore impedance plethysmography (IPG) as a non-invasive and affordable alternative for diagnosis. Methods: To address the current lack of large-scale, high-quality impedance data, we introduce arterioscope.sim, a simulation platform that models arterial blood flow and computes the electrical conductivity of blood. The platform simulates bioimpedance measurements on specific body segments using patient-specific parameters. The study investigates how introducing arterial diseases into the simulation affects the bioimpedance signals. Results: The simulation results demonstrate that introducing atherosclerosis and arteriosclerosis leads to significant changes in the computed signals compared to simulations of healthy arteries. Furthermore, simulation of a patient-specific healthy artery strongly correlates with measured signals from a healthy volunteer. Conclusions and significance: arterioscope.sim effectively simulates bioimpedance signals in healthy and diseased arteries and highlights the potential of using these signals for early diagnosis of arterial diseases, offering a non-invasive and cost-effective alternative to traditional diagnostic methods. Full article

This paper presents a highly efficient, low-power, compact mixed-signal sinusoidal current generator (CG) integrated circuit (IC) designed for bioelectrical impedance spectroscopy (BIS) with low total harmonic distortion (THD). The proposed system employs a 9-bit sine wave lookup table (LUT) which is simplified to a 4-bit data stream through a third-order digital delta–sigma modulator (ΔΣM). Unlike conventional analog low-pass filters (LPF), which statically limit bandwidth, the finite impulse response (FIR) filter attenuates high-frequency noise according to the operating frequency, allowing the frequency range of the sinusoidal signal to vary. Additionally, the output of the FIR filter is applied to a 6-bit capacitive digital-to-analog converter (CDAC) with data-weighted averaging (DWA), enabling dynamic capacitor matching and seamless interfacing. The sinusoidal CG IC, fabricated using a 65 nm CMOS process, produces a 5 μA amplitude and operates over a wide frequency range of 0.6 to 20 kHz. This highly synthesizable CG achieves a THD of 0.04%, consumes 19.2 μW of power, and occupies an area of 0.0798 mm². These attributes make the CG IC highly suitable for compact, low-power bio-impedance applications. Full article

Background: Heart rate (HR) is a critical biomarker that provides insights into overall health, stress levels, and the autonomic nervous system. Pulse wave signals contain valuable information about the cardiovascular system and heart status. However, signal acquisition in wearables poses challenges, particularly when using electrical sensors, due to factors like the distance from the heart, body movement, and suboptimal electrode placement. Methods: Electrical bioimpedance (EBI) measurements using bipolar and tetrapolar electrode systems were employed for pulse wave signal acquisition from the wrist in both perpendicular and distal configurations. Signal preprocessing techniques, including baseline removal via Hankel matrix methods, normalization, cross-correlation, and peak detection, were applied to improve signal quality. This study describes the combination of sensor-level signal acquisition and processing for accurate wearable HR estimation. Results: The bipolar system was shown to produce larger $\Delta Z\left(t\right)$, while the tetrapolar system demonstrated higher sensitivity. Distal placement of the electrodes yielded greater $\Delta Z\left(t\right)$ (up to 0.231 $\Omega$) when targeting both wrist arteries. Bandpass filtering resulted in a better signal-to-noise ratio (SNR), achieving 3.6 dB for the best bipolar setup and 4.8 dB for the tetrapolar setup, compared to 2.6 and 3.3 dB SNR, respectively, with the Savitzky–Golay filter. The custom HR estimation algorithm presented in this paper demonstrated improved accuracy over a reference method, achieving an average error of 1.8 beats per minute for the best bipolar setup, with a mean absolute percentage error (MAPE) of 8%. Conclusions: The analysis supports the feasibility of using bipolar electrode setups on the wrist and highlights the importance of electrode positioning relative to the arteries. The proposed signal processing method, featuring a preprocessing pipeline and HR estimation algorithm, provides a proof-of-concept demonstration for HR estimation from EBI signals acquired at the wrist. Full article

Blood clot formation inside the membrane oxygenator (MO) remains a risk in extracorporeal membrane oxygenation (ECMO). It is associated with thromboembolic complications and normally detectable only at an advanced stage. Established clinical monitoring techniques lack predictive capabilities, emphasizing the need for refinement in MO monitoring towards an early warning system. In this study, an MO was modified by integrating four sensor fibers in the middle of the hollow fiber mat bundle, allowing for bioimpedance measurement within the MO. The modified MO was perfused with human blood in an in vitro test circuit until fulminant clot formation. The optical analysis of clot residues on the extracted hollow fibers showed a clot deposition area of 51.88% ± 14.25%. This was detectable via an increased bioimpedance signal with a significant increase 5 min in advance to fulminant clot formation inside the MO, which was monitored by the clinical gold standard (pressure difference across the MO (dp-MO)). This study demonstrates the feasibility of detecting clot growth early and effectively by measuring bioimpedance within an MO using integrated sensor fibers. Thus, bioimpedance may even outperform the clinical gold standard of dp-MO as a monitoring method by providing earlier clot detection. Full article

Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in functional 2D images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) electrodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the conventional star arrangement. Prior research demonstrated cooperative sensors for measuring bioimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to remove batteries from the sensors and have the central unit supply power over the two-wire bus. Building from our previous research, this paper presents the application of this method to the measurement of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application-specific integrated circuit (ASIC). The initial experimental results reveal that both approaches are feasible, but the ASIC approach offers advantages for medical safety, as well as lower power consumption and a smaller size. Full article

The objective of the present study is to analyze the urinary metabolome profile of patients with obesity and overweight and relate it to different obesity profiles. This is a prospective, cross-sectional study in which patients with a body mass index (BMI) ≥25 kg/m were selected. Anthropometric data were assessed by physical examination and body composition was obtained by bioimpedance (basal metabolic rate, body fat percentile, skeletal muscle mass, gross fat mass and visceral fat). Urine was collected for metabolomic analysis. Patients were classified according to abdominal circumference measurements between 81 and 93, 94 and 104, and >104 cm; visceral fat up to 16 kilos and less than; and fat percentiles of <36%, 36–46% and >46%. Spectral alignment of urinary metabolite signals and bioinformatic analysis were carried out to select the metabolites that stood out. NMR spectrometry was used to detect and quantify the main urinary metabolites and to compare the groups. Seventy-five patients were included, with a mean age of 38.3 years, and 72% females. The urinary metabolomic profile showed no differences in BMI, abdominal circumference and percentage of body fat. Higher concentrations of trigonelline (p = 0.0488), sarcosine (p = 0.0350) and phenylalanine (p = 0.0488) were associated with patients with visceral fat over 16 kg. The cutoff points obtained by the ROC curves were able to accurately differentiate between patients according to the amount of visceral fat: sarcosine 0.043 mg/mL; trigonelline 0.068 mg/mL and phenylalanine 0.204 mg/mL. In conclusion, higher visceral fat was associated with urinary levels of metabolites such as sarcosine, related to insulin resistance; trigonelline, related to muscle mass and strength; and phenylalanine, related to glucose metabolism and abdominal fat. Trigonelline, sarcosine and phenylalanine play significant roles in regulating energy balance and metabolic pathways essential for controlling obesity. Our findings could represent an interesting option for the non-invasive estimation of visceral fat through biomarkers related to alterations in metabolic pathways involved in the pathophysiology of obesity. Full article

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects. Full article

Bioimpedance spectrum (BIS) measurements are highly appreciated in in vivo studies. This non-destructive method, supported by simple and efficient instrumentation, is widely used in clinical applications. The multi-frequency approach allows for the efficient extraction of the most information from the measured data. However, low-frequency implementations are still unexploited in the development of the technique. A self-developed BIS measurement technology is considered the pioneering approach for low (<5 kHz) and ultra-low (<100 Hz) frequency range studies. In this paper, the robustness of ultra-low frequency measurements in the prototypes is examined using specially constructed physical models and a dedicated neural network-based software. The physical models were designed to model the dispersion mainly in the ultra-low frequency range. The first set of models was used in the training of the software environment, while the second set allowed a complete verification of the technology. Further, the Hilbert transformation was employed to adjust the imaginary components of complex signals and for phase determination. The findings showed that the prototypes are capable of efficient and robust data acquisition, regardless of the applied frequency range, minimizing the impact of measurement errors. Consequently, in in vivo applications, these prototypes minimize the variance of the measurement results, allowing the resulting BIS data to provide a maximum representation of biological phenomena. Full article

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO₂ gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0–11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO₂ gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO₂ (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves. Full article

Conventional dry electrodes often yield unstable results due to the presence of parasitic capacitance between the flat electrode surface and the non-uniform skin interface. To address this issue, a gel is typically placed between the electrodes to minimize parasitic capacitance. However, this approach has the drawbacks of being unsuitable for repeated use, limited lifetime due to gel evaporation, and the possibility of developing skin irritation. This is particularly problematic in underserved areas since, due to the cost of disposable wet electrodes, they often sterilize and reuse dry electrodes. In this study, we propose a method to neutralize the effects of parasitic capacitance by attaching high-value capacitors to the electrodes in parallel, specifically when applied to pulse wave monitoring through bioimpedance. Skin capacitance can also be mitigated due to the serial connection, enabling stable reception of arterial pulse signals through bioimpedance circuits. A high-frequency structure simulator (HFSS) was first used to simulate the capacitance when injection currents flow into the arteries through the bioimpedance circuits. We also used the simulation to investigate the effects of add-on capacitors. Lastly, we conducted preliminary comparative analyses between wet electrodes and dry electrodes in vivo with added capacitance values ranging from 100 pF to 1 μF, altering capacitance magnitudes by factors of 100. As a result, we obtained a signal-to-noise ratio (SNR) that was 8.2 dB higher than that of dry electrodes. Performance was also shown to be comparable to wet electrodes, with a reduction of only 0.4 dB using 1 μF. The comparative results demonstrate that the addition of capacitors to the electrodes has the potential to allow for performance similar to that of wet electrodes for bioimpedance pulse rate monitoring and could potentially be used for other applications of dry electrodes. Full article

Bioimpedance sensing is widely used across a spectrum of biomedical applications. Among the different system architectures for measuring tissue impedance, synchronous detection or demodulation (SD) stands out for its lock-in amplifier utilising in-phase (I) and quadrature (Q) demodulation signals to derive real and imaginary impedance components. Typically, the current injected into the tissue is controlled by a voltage-controlled current source (VCCS). However, the VCCS can introduce phase shifts leading to discrepancies in real/imaginary outputs, especially at the highest end of the operating frequency bandwidth. Such discrepancies can significantly impact diagnostic accuracy in applications reliant on precise tissue phase profiling, such as cancer and neuromuscular evaluations. In the present work, we propose an automatic phase error compensation stage for bioimpedance measurement systems to minimise this systematic error. Our experimental findings demonstrated a considerable reduction in phase error, with the Phase Error Compensated Synchronous Detection (PECSD) system exhibiting a maximum phase error of 2° (≤5% error) compared with the uncompensated SD system where error exceeded 20%. The improvements made by our proposed SD system hold great potential for enhancing the accuracy of impedance measurements, particularly in clinical diagnosis and disease detection. Full article