In this paper, we present the development and the validation of a novel index of nociception/anti-nociception (N/AN) based on skin impedance measurement in time and frequency domain with our prototype AnspecPro device. The primary objective of the study was to compare the Anspec-PRO device with two other commercial devices (Medasense, Medstorm). This comparison was designed to be conducted under the same conditions for the three devices. This was carried out during total intravenous anesthesia (TIVA) by investigating its outcomes related to noxious stimulus. In a carefully designed clinical protocol during general anesthesia from induction until emergence, we extract data for estimating individualized causal dynamic models between drug infusion and their monitored effect variables. Specifically, these are Propofol hypnotic drug to Bispectral index of hypnosis level and Remifentanil opioid drug to each of the three aforementioned devices. When compared, statistical analysis of the regions before and during the standardized stimulus shows consistent difference between regions for all devices and for all indices. These results suggest that the proposed methodology for data extraction and processing for AnspecPro delivers the same information as the two commercial devices.

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient $\eta$ using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast $\eta$ values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter $\eta$, achieving an $R^2$ of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast $\eta$ with an $R^2$ of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

Near-field wireless power transfer enables energy transfer via electromagnetic coupling between a power source and a receiver over short distances. This technology improves user convenience and autonomy in both low-power and high-power applications, such as wearable electronics, electric vehicles, and biomedical systems. This paper provides a comprehensive review of the underlying principles, practical applications, regulatory frameworks, and technological challenges associated with near-field wireless energy transfer. The review focuses on capacitive and inductive wireless power transfer. This is supported by a summary of research articles, evaluated using metrics such as power, transfer distance, and frequency. Applications are discussed within the market sectors, medical devices, electric mobility, and unmanned aerial systems. Recent developments in international standards WPC’s Qi2 and SAE’s J2954, as well as health-related standards IEEE C95 and ICNIRP-guidelines are discussed. Finally, the review identifies the critical limitations and trade-offs of capacitive and inductive wireless power transfer technologies.

Surgical procedures exceeding six hours and intensive care for traumatic injuries often require prolonged general anesthesia, commonly maintained via induced coma. Similar protocols are used for COVID-19 patients under mechanical ventilation. These conditions pose challenges such as tissue drug accumulation and overdose risk. Extended supine or prone positioning alters tissue volumes based on their physical properties. Accurate anesthesia management depends on patient-specific models that characterize pharmacokinetics (PK, drug distribution) and pharmacodynamics (PD, drug effects) using compartmental representations. However, comorbidities such as obesity are typically overlooked in existing PK models. This study augments PK models to investigate the impact of obesity on drug distribution and clearance by incorporating the risk of drug trapping in adipose tissue as a nonlinear function of body mass index (BMI). A theoretical framework links BMI with tissue porosity and permeability, introducing a "trap" compartment to model delayed clearance in obese patients. The model is validated using in vitro impedance measurements and numerical simulations. Fat tissue properties were characterized via Cole-Cole fractional-order models, with parameters identified using a genetic algorithm. The nonlinear BMI-trapping relationship was estimated using the trust region method. Simulations covered both open-loop (single-bolus) and closed-loop (continuous infusion) scenarios using model predictive control(MPC). Impedance measurements and identified Cole-Cole fractional-order models confirmed volume-dependent properties of fat tissue. Simulations using clinical data demonstrated delayed drug clearance in high-BMI patients, which support the proposed theoretical background. Open-loop simulations showed prolonged drug retention, while closed-loop control using MPC maintained anesthetic depth with reduced total drug input. Differences in BIS nadir and total drug usage were observed across BMI and age groups, supporting the model's clinical applicability. The augmented model with MPC minimizes drug trapping and reduces total drug use, lowering overdose risk and supporting safer anesthesia management.

Early assessment of respiratory mechanics is crucial for early-stage diagnosing and managing lung diseases, leading to greater patient outcomes. Traditional methods like spirometry are limited in continuous monitoring and patient compliance as they require forced maneuvers with significant patient cooperation, which may not be available in fragile individuals. The Forced Oscillation Technique (FOT) is a non-invasive measurement method, only based on the tidal breathing at rest from the patient for a limited time period. The proposed solution integrates low-frequency FOT with continuous monitoring using Equivital (EQV) sensors to enhance respiratory mechanics information with heart rate variability. Data were collected over a two-hour period from six healthy volunteers, measuring respiratory impedance every 7 min and continuously recording physiological parameters. The best-fitting fractional-order models for impedance data were identified using genetic algorithms. This study also explores the correlation between impedance model parameters and EQV data, discussing the potential of AI tools for forecasting respiratory properties. Our findings indicate that combined monitoring techniques and AI analysis provides additional complementary information, subsequently aiding the improved evaluation of respiratory function and tissue mechanics. The proposed protocol allows for ambulatory assessment and can be easily performed in normal breathing conditions.