Electrical Impedance Data Research Articles

A monitoring platform based on electrical impedance and AI techniques to enhance the resilience of the built environment

Structural Health Monitoring (SHM) and early warning systems (EWSs) play a pivotal role in enhancing seismic resilience for both buildings and occupants. This paper introduces a monitoring platform that collects electrical impedance data from scaled concrete beams undergoing load and accelerated degradation tests. Artificial Intelligence (AI) algorithms are employed for predictive analysis, scrutinizing historical impedance data, and forecasting future trends. These algorithms adapt to environmental parameters, becoming valuable tools in data-driven decision-making processes. In particular, the study investigates concrete specimens in different test conditions, utilizing a distributed sensor network based on electrical impedance as well as temperature and relative humidity sensors. Real-time data are transmitted to a cloud infrastructure during accelerated degradation tests (both in water and in chloride-rich solution) and in room conditions. An AI-based forecasting approach using Prophet is proposed, ingesting electrical impedance and temperature data, and tested to predict electrical impedance corresponding to approximately 10 % of the time series balancing responsiveness with predictive accuracy, crucial for effective EWS operations and management requirements. The performance of the tested models is evaluated employing metrics such as Mean Average Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The proposed approach surpasses statistical methods and deep learning techniques, reporting a MAPE always lower than 3.20 % and a correlation higher than 81.65 % (in wet-dry cycles in water these values are 0.65 Ω and 91.85 %, respectively). This proves to be a promising step towards transparent SHM, which integrates AI models facilitating self-monitoring and early maintenance prediction, thus enhancing the resilience of the built environment.

Speakers-Used as sensors for detecting acoustic loads with artificial intelligence.

Previous research demonstrated the potential of using speakers as sensors to detect ear canal conditions. This study continues that effort by using a single speaker to measure electrical impedance across various acoustic loads. Electrical impedance data were collected and preprocessed for machine learning model training. Different image forms were tested, including magnitude only and combined magnitude phase. Using 2100 data samples with convolutional neural network-based models (AlexNet, ResNet, and DenseNet), binary and multiclass classifications achieved average accuracies of 0.9716 and 0.907, respectively. This innovative approach is set to revolutionize acoustic sensing through artificial intelligence.

Evaluating Dielectric Properties for Assessing Water Content at Acupuncture Points: New Methodology.

Understanding acupuncture point microenvironments is vital for optimizing treatment efficacy. Evaluating changes in water content at these points can provide further insights into the effects of acupuncture on tissues. This study aimed to measure tissue dielectric constant (TDC) and assess changes in water content, specifically at stomach 36 (ST36, Zusanli) and spleen 6 (SP6, Sanyinjiao) acupuncture points. In a controlled, blinded, randomized trial, 113 healthy volunteers were divided into six groups based on TDC sensor diameters (XS, M, and L): three control groups and three acupuncture groups. They were assessed at three time points: T1, baseline; T2, 20 min post-needle withdrawal; and T3, 40 min post-needle withdrawal. Electrical impedance (EI) was also analyzed. Significance level was set at p < 0.001. TDC at ST36 and SP6 significantly decreased with the XS probe at T2 and T3 compared with that at T1 (F8, 452: 54.61). TDC did not significantly vary between T2 and T3 with M and L probes. EI data indicated that the current passage increased in the SP (F2, 226: 39.32) and ST (F2, 226: 37.32) groups during T2 and T3 compared with that during T1 within their respective groups and controls. and Relevance: This study demonstrated the efficacy of TDC measurements in detecting water content fluctuations at acupuncture points and their responses to needles. TDC measurements, which were validated against EI, provide valuable insights into acupuncture point microenvironments and thus help optimize treatments.

Speakers—As a sensor for detecting acoustic loads with Artificial Intelligent (AI)

The potential of using speakers as a sensor to detect ear canal conditions was demonstrated previously. This research contains our ongoing and continuous effort to utilize a single speaker as a sensor by measuring electrical impedance varying acoustic loads. Electrical impedance data (magnitude and phase) from six different acoustic load conditions were collected as features for machine learning (ML) model training. To enhance the learning performance, the data were pre-processed and augmented with normalization and level-shifting techniques, respectively. The raw data were converted to images to optimize the learning performance to classify acoustic loads from the impedance measurement. Several forms of images were experimented such as magnitude only, overlapped magnitude and phase, and rectangular form. A total of 2100 data (350 each) were used with CNN-based State of the Art (SOTA) models such as AlexNet, ResNet, and DenseNet. Both binary and multiclass classifications were performed, showing 0.9716 average accuracy and 0.907 accuracy, respectively. This innovative single-speaker approach using impedance as ML features is poised to revolutionize traditional acoustic sensing research by harnessing the limitless power of AI.

Recent Prospects of Medical Imaging and Sensing Technologies Based on Electrical Impedance Data Acquisition System

This paper presents a cost-effective data acquisition & medical imaging system using electrical impedance tomography (EIT). The presented method investigates & analyses the output image retrieved from the electrical conductivity distribution of a test object or phantom. The image reconstruction was performed using a modified imaging algorithm. The experimental setup of the proposed system mainly consists of convertible 16/8/4 electrodes (which are the phantom circumference attached), the accurate static Current Source (Milli Ampere Current with KHz Frequency) for providing current which passes into the boundary of an object through a different pair of electrodes. The output of electrodes is acquired as a voltage that is measured from the boundary of the conductive object of other electrodes pair and fed into a computer for the appropriate computing of the acquired data. EIT systems with GUI are used in the data collection procedure to provide a user-friendly interface for effective picture reconstruction, which is used both like offline and online functioning are possible. Performance testing was carried out utilizing a variety of phantom models that represented human test subjects and different physical objects. The data acquisition (DAQ) techniques used in EIT produced findings that were innovative, comparable and showed enhanced sensitivity, functionality for engineering society.

Real-Time Feature Extraction from Electrocochleography with Impedance Measurements during Cochlear Implantation using Linear State-Space Models.

Electrocochleography (ECochG) is increasingly used to monitor the inner ear function of cochlear implant (CI) patients during surgery. Current ECochG-based trauma detection shows low sensitivity and specificity and depends on visual analysis by experts. Trauma detection could be improved by including electric impedance data recorded simultaneously with the ECochG. However, combined recordings are rarely used because the impedance measurements produce artifacts in the ECochG. In this study, we propose a framework for automated real-time analysis of intraoperative ECochG signals using Autonomous Linear State-Space Models (ALSSMs). We developed ALSSM based algorithms for noise reduction, artifact removal, and feature extraction in ECochG. Feature extraction includes local amplitude and phase estimations and a confidence metric over the presence of a physiological response in a recording. We tested the algorithms in a controlled sensitivity analysis using simulations and validated them with real patient data recorded during surgeries. The results from simulation data show that the ALSSM method provides improved accuracy in the amplitude estimation together with a more robust confidence metric of ECochG signals compared to the state-of-the-art methods based on the fast Fourier transform (FFT). Tests with patient data showed promising clinical applicability and consistency with the findings from the simulations. We showed that ALSSMs are a valid tool for real-time analysis of ECochG recordings. Removal of artifacts using ALSSMs enables simultaneous recording of ECochG and impedance data. The proposed feature extraction method provides the means to automate the assessment of ECochG. Further validation of the algorithms in clinical data is needed.

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.

Needle impedance-electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2-mdx muscular dystrophy and wild-type (WT) mouse skeletal muscle. iEMG data were obtained from quadriceps of D2-mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampled across the frequency range using three different approaches. Random forest-based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross-validation procedure. Data from 20 D2-mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy. We have demonstrated an ML-based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM-EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.

Breast cancer classification application based on QGA-SVM

Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58] was used as the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM.

Using pulsed magnetic fields to improve the quality of frozen blueberry: A bio-impedance approach

Freezing assisted by pulsed magnetic field (PMF) is an emerging technology that could be used in food preservation. This paper evaluates how PMF-assisted freezing affects the preservation of blueberry and its bioactive compounds. Blueberries were subjected to 8 PMF-assisted freezing treatments: T1 (36.8 mT/30 Hz), T2 (36.8 mT/60 Hz), T3 (36.8 mT/90 Hz), T4 (36.8 mT/20 Hz), T5 (44.7 mT/30 Hz), T6 (44.7 mT/60 Hz), T7 (44.7 mT/90 Hz), and T8 (44.7 mT/120 Hz). In treatment T9 (control), the blueberries were subjected to conventional freezing to −35 °C; T10 represents fresh blueberries. Compared to conventional freezing (T9), PMF-assisted freezing (T1 to T8) gave different parameters of temperature, nucleation time, degree of supercooling, and phase change time. The parameters achieved with T7 evidenced better behavior: smaller crystals were formed, allowing the cellular structure to be preserved, as confirmed by the electrical parameters (Re, Ri, and Cm) obtained from electrical impedance data. Moreover, T7 preserved anthocyanins and polyphenols, promoting the highest antioxidant capacity among the blueberries subjected to PMF-assisted freezing. Meanwhile, conventional freezing and PMF-assisted freezing reduced the polyphenol oxidase and peroxidase activities. In conclusion, at the laboratory level, PMF-assisted freezing preserves the blueberry cellular structures and bioactive compounds.

Keratinocyte cellular damage induced by pesticide doses below the cytotoxic level evidenced by electrical impedance and broadband dielectric spectroscopy

The cellular response of a normal human keratinocyte cell line exposed to non-cytotoxic doses of a deltamethrin-based pesticide was investigated by means of two different electrical impedance data spectroscopy approaches: Nyquist plot and broadband dielectric spectroscopy. The measurements have shown that the membrane capacity increases with pesticide concentration and this facilitates the electric current through cell membranes. Furthermore, the impedance of the extracellular matrix also increases with pesticide concentration, thus reducing the electric current outside the cell. Dielectric permittivity changes in the cellular samples at frequencies larger than 100 Hz. Fluorescence (FL) measurements emphasized an increase in neutral membrane lipids as a consequence of the pesticide exposure. Comparison of FL response of pesticide exposed cells with the control ones showed a time increase in the emission intensity, suggesting the existence of a membrane lipid response aimed at repairing the cell damage due to pesticide exposure. Therefore, both spectroscopic techniques have been demonstrated as potential means to investigate the response to cell stress and damage. This opens up new possibilities in the early diagnosis of cellular modifications related to the pesticide exposure of cells.

Sensor Enabled Catheter Ablation Study (SECAS).

BackgroundEnSite Precision technology (Abbott, Chicago, Illinois) is a novel mapping and navigation system facilitating the visualization and manipulation of intracardiac catheters during arrhythmia ablation procedures. When using Sensor Enabled (SE) catheters (Abbott, Chicago, Illinois), the mapping system uses both electrical impedance and magnetic data to facilitate more accurate mapping and navigation. Whether this translates into better clinical outcomes is unknown.MethodsThis retrospective study will examine whether SE catheters improve the success rate or decrease the risks compared to Biosense Thermocool catheters (Biosense Webster Inc., Irvine, California) not employing sensor-enabled technology utilizing NavX EnSite Precision algorithms. Charts of 146 patients who underwent radiofrequency ablations for supraventricular and ventricular arrhythmias between 2016 and 2019 in the Beirut Cardiac Institute were reviewed and analyzed. It was concluded that SE catheters have the same success rate as electrical impedance catheters.ResultsA total of 70% of the ablations carried using the impedance-based catheter were successful compared to 74% using the SE catheter. However, the difference was statistically non-significant (p-value: 0.7). As for complications, the ventricular fibrillation rate was increased in the SE catheter group. Three procedures were complicated by pericardial effusion, three patients had reversible heart block, and one death was recorded, all reported while using the standard catheter (p-value: 0.01).ConclusionSE catheters have the same success rates compared to standard catheters using the EnSite Precision mapping system.

Electrical Modelling of In-Vivo Impedance Spectroscopy of Nicotiana tabacum Plants

Electrical impedance spectroscopy has been suggested as a sensing method for plants. Here, a theoretical approach for electrical conduction via the plant stem is presented and validated, linking its living electrical characteristics to its internal structure. An electrical model for the alternating current conduction and the associated impedance in a live plant stem is presented. The model accounts for biological and geometrical attributes. It uses the electrically prevalent coupled transmission line model approach for a simplified description of the complicated vessel structure. It considers the electrode coupling to the plant stem (either Galvanic or Faradic), and accounts for the different interactions of the setup. Then the model is simplified using the lumped element approach. The model is then validated using a four-point probe impedance spectroscopy method, where the probes are galvanically coupled to the stem of Nicotiana tabacum plants. The electrical impedance data was collected continuously and the results exhibit an excellent fitting to the theoretical model, with a fitting error of less than 1.5% for data collected on various days and plants. A parametric evaluation of the fitting corresponds to the proposed physically based model, therefore providing a baseline for future plant sensor design.

Design of a bioelectronic tongue for glucose monitoring using zinc oxide nanofibers and graphene derivatives

Monitoring glucose levels is critical for diabetes management and might be a key step in the development of individualized treatment strategies. In this scenario, tracking salivary glucose has been recognized as a promising strategy due to its merits of ease sampling and non-invasive nature. In this paper, we report on the development of an electrical impedance-based biosensor array to distinguish glucose at different concentrations in saliva. The enzymatic biosensors were made of gold interdigitated electrodes coated with pristine electrospun zinc oxide nanofibers (NFZ) and NFZ combined with graphene-based nanomaterials (i.e., reduced graphene oxide - rGO and graphene quantum dots - GQDs), on which a layer of glucose oxidase (GOx) enzyme was adsorbed. Electrical impedance measurements indicate that the NFZ-GQDs@GOx and NFZ-rGO@GOx platforms presented good linear relationship with glucose concentration in the range of 0.1 to 6 mM. The highest sensitivity was reached for NFZ-rGO@GOx with a detection limit (LOD) of 14 μM, while the LOD was 32 μM for NFZ-GQDs@GOx. Both biosensors were also capable of detecting glucose in artificial saliva using aliquots of 10 μL, with recovery between 87.3 and 106.8%. Furthermore, the three sensing units (NFZ@GOx, NFZ-rGO@GOx and NFZ-GQDs@GOx) were employed to build a bioelectronic tongue. Using Principal Component Analysis (PCA) technique to project the electrical impedance data of all sensing units allowed the discrimination of the different glucose concentrations and interferents. This study reveals the applicability of the developed bioelectronic tongue as non-invasive glucose sensors, which approach could also be pottentially adapted to detect other disease biomarkers present in saliva.

Relative Contribution of Different Mitochondrial Oxidative Phosphorylation Components to the Retinal Pigment Epithelium Barrier Function: Implications for RPE-Related Retinal Diseases.

Disruption of retinal pigment epithelial (RPE) barrier integrity is involved in the pathology of several blinding retinal diseases including age-related macular degeneration (AMD) and diabetic retinopathy (DR), but the underlying causes and pathophysiology are not completely well-defined. Mitochondria dysfunction has often been considered as a potential candidate implicated in such a process. In this study, we aimed to dissect the role of different mitochondrial components; specifically, those of oxidative phosphorylation (OxPhos), in maintaining the barrier functionality of RPE. Electric cell-substrate impedance sensing (ECIS) technology was used to collect multi-frequency electrical impedance data to assess in real-time the barrier formation of the RPE cells. For this purpose, the human retinal pigment epithelial cell line—ARPE-19—was used and treated with varying concentrations of specific mitochondrial inhibitors that target different steps in OxPhos: Rotenone for complex I (the largest protein complex in the electron transport chain (ETC)); oligomycin for ATP synthase; and carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP) for uncoupling ATP synthesis from the accompanying ETC. Furthermore, data were modeled using the ECIS-Zθ software to investigate in depth the effects of these inhibitors on three separate barrier parameters: cell–cell interactions (Rb), cell–matrix interactions (α), and the cell membrane capacitance (Cm). The viability of ARPE-19 cells was determined by lactate dehydrogenase (LDH) Cytotoxicity Assay. The ECIS program’s modeling demonstrated that FCCP and thus OxPhos uncoupling disrupt the barrier function in the ARPE-19 cells across all three components of the total resistance (Rb, α, and Cm) in a dose-dependent manner. On the other hand, oligomycin and thus ATP synthase inhibition mostly affects the ARPE-19 cells’ attachment to their substrate evident by a significant decrease in α resistance in a dose-dependent manner, both at the end and throughout the duration of the experiment. On the contrary, rotenone and complex I inhibition mostly affect the ARPE-19 paracellular resistance Rb in a dose-dependent manner compared to basolateral resistance α or Cm. Our results clearly demonstrate differential roles for different mitochondrial components in maintaining RPE cell functionality in which uncoupling of OxPhos is a major contributing factor to the disruption barrier function. Such differences can be used in investigating gene expression as well as for screening of selective agents that improve the OxPhos coupling efficiency to be used in the therapeutic approach for treating RPE-related retinal diseases.

Electrical impedance measurement system to assess in-situ experiments on epithelia under ELF magnetic fields exposure

Purpose: The development of an electric impedance meter based on the impedance spectroscopy technique, for in vitro and in situ experimentation, with cellular epithelia submitted to extremely low frequency magnetic fields in a controlled environment. Unlike other reported systems, a strength of the one presented here is that it avoids the influence of external factors on the experiment. Materials and methods: The designed system employs the electrical impedance values obtained by the impedance spectroscopy technique to determine the parameters of the simple equivalent electrical model of a cellular monolayer. The Madin-Darby Canine Kidney (MDCK) cell cultures were used as subjects of study in the experimental protocol. Results: The validation was carried out by comparing the transepithelial electrical impedance data of the cell cultures obtained with the developed system and those of the Cellzscope® commercial system used as the standard. Non-significant differences were obtained. Conclusion: It was confirmed that the developed system provides reliable values of transepithelial electrical impedance to experiment with cell cultures and take advantage of the controlled environment to reduce the effects of experimental management.

Electric impedance spectroscopy feature extraction for tissue classification with electrode embedded surgical needles through a modified forward stepwise method

There has been a growing interest in developing electric impedance sensing surgical tools for tissue identification during surgery. A key facet of this development is identifying distinct features that can be used to identify tissues from one another. This paper explores several feature extraction techniques and classification methods applied to electric impedance data. Furthermore, a modified forward stepwise method is proposed. The method introduces a scoring metric to help select features to add to the model, that is based off of the coefficient of variation and overlapping index from the feature's probability density functions for each of the classes. The proposed and existing methods were applied to spectral data measured at 23 frequencies, from 132 samples across 6 different tissues including ex-vivo bovine kidney, liver and muscle, poultry liver, as well as freshly excised canine testicle and ovary samples. These methods were able to successfully find impedance spectra features for the investigated biological tissues. The best predictive accuracy was with Boruta feature extraction and a Random Forest classifier but without significantly reducing the number of features in the classifier model. The proposed method was able to reduce the number of features in the model to an average of 5.8 features for all tested classifiers. These methods may have use in finding features to discriminate other tissue types, possibly to aid in targeting lesions in minimally invasive cancer treatment surgeries.

Paradoxical Effect of Chest Wall Compression on Respiratory System Compliance: A Multicenter Case Series of Patients With ARDS, With Multimodal Assessment

Paradoxical Effect of Chest Wall Compression on Respiratory System Compliance: A Multicenter Case Series of Patients With ARDS, With Multimodal Assessment

Structural, dielectric and electrical characteristics of manganese modified (Bi0.5Ba0.25Sr0.25) (Ti0.5Fe0.5)O3 relaxor

In this communication, detailed studies of the structural, microstructural, dielectric, and electrical properties of polycrystalline materials, (Bi0.5Ba0.25Sr0.25) (Ti0.5Fe0.5)O3 and (Bi0.5Ba0.25Sr0.25) (Ti0.25Mn0.25Fe0.5)O3, synthesized by using a high–temperature solid-state-reaction method, have been reported. X-ray structural and scanning electron micrograph studies exhibit phase pure tetragonal system and surface morphology (size and distribution of grains and grain boundaries) of the samples respectively. Analysis of the temperature and frequency dependence of dielectric and electrical (impedance, modulus, and conductivity) data reveals the ferroelectric relaxor behavior, relaxation mechanism, and semiconductor (negative temperature coefficient of resistance) properties of the bulk BFBST and Mn modified BFBST electro-ceramics. The relaxation time and activation energy (Ea) were calculated from the above data. The characteristics of Mn modified BFBST have been compared to that of it’s parent (BFBST) compound. The different inherent conduction mechanisms, such as Ohmic, hopping, space charge limited (SCLC) have been analyzed. The bulk- and interface-limited conduction processes were evidently found in the materials by the Poole–Frenkel (PF) and Schottky (SEmen modified BFBST have been compared to that of its parent (BFBST) compound) emission fitting of the J ∼ E characteristic data. The leakage data of BFBST-Mn (Mn modified Mn) quantified the average energy gap (Eg) in the range of 0.83–0. 87 eV for different applied voltages and in a wide range of temperature (25 °C–300 °C). With the increase in voltage, Eg decreases. This work suggests that Mn-substitution (Mn4+) at B (Ti4+) site keeping the stoichiometry undisturbed enhances structural, dielectric response (higher dielectric constant) and reduce the leakage behavior especially at low temperature and high-frequency range.

PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10–100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified while using the Cole–Cole model to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulated the real and complex electrical potential signals for selected frequencies for SIP. These frequency-dependent bulk conductivities contain information that is relevant to geochemical changes in the system. This study demonstrated that the PFLOTRAN-SIP framework is able to detect the presence of a tracer in the subsurface. SIP offers a significant benefit over ERT in the form of greater information content. It provided multiple datasets at different frequencies that better constrained the tracer distribution in the subsurface. Consequently, this framework allows for practitioners of environmental hydrogeophysics and biogeophysics to monitor the subsurface with improved resolution.

Dielectric Response of ZnTe–Ti/Al Schottky Junctions with CdTe Quantum Dots Studied by Impedance Spectroscopy

The electrical properties of ZnTe–Ti/Al Schottky junctions were investigated by the impedance spectroscopy (IS) method. Current-voltage (I-V) and capacitance-voltage (C-V) measurements were also performed. The studied samples were the CdTe quantum dot structures embedded in ZnTe matrix and a reference ZnTe sample without quantum dots. C-V characteristics confirmed the presence of quantum dots (QDs) in the structures. Electric modulus and impedance data were analyzed. IS studies proved that long-range conductivity governs the relaxation processes in the junctions. For both samples, the data were fitted with a simple RC circuit composed of a depletion layer capacitance in parallel with bulk resistance and a series resistance of contacts. The activation energy of the relaxation process observed for the reference sample obtained from the Arrhenius plot of the resistance, imaginary impedance, and electric modulus equals 0.4 eV at zero bias. For the quantum dot sample, the value of activation energy determined with the help of the same methods equals 0.2 eV. In conclusion, it was assumed that the relaxation processes for the reference sample are attributed to the trap present in ZnTe host material, whereas those observed for the QD structure are assigned to the deep level associated with defects located close to the QDs created during their growth.