Background: Electroporation based therapies and treatments (e.g. electrochemotherapy, gene electrotransfer for gene therapy and DNA vaccination, tissue ablation with irreversible electroporation and transdermal drug delivery) require a precise prediction of the therapy or treatment outcome by a personalized treatment planning procedure. Numerical modeling of local electric field distribution within electroporated tissues has become an important tool in treatment planning procedure in both clinical and experimental settings. Recent studies have reported that the uncertainties in electrical properties (i.e. electric conductivity of the treated tissues and the rate of increase in electric conductivity due to electroporation) predefined in numerical models have large effect on electroporation based therapy and treatment effectiveness. The aim of our study was to investigate whether the increase in electric conductivity of tissues needs to be taken into account when modeling tissue response to the electroporation pulses and how it affects the local electric distribution within electroporated tissues. Methods: We built 3D numerical models for single tissue (one type of tissue, e.g. liver) and composite tissue (several types of tissues, e.g. subcutaneous tumor). Our computer simulations were performed by using three different modeling approaches that are based on finite element method: inverse analysis, nonlinear parametric and sequential analysis. We compared linear (i.e. tissue conductivity is constant) model and non-linear (i.e. tissue conductivity is electric field dependent) model. By calculating goodness of fit measure we compared the results of our numerical simulations to the results of in vivo measurements. Results: The results of our study show that the nonlinear models (i.e. tissue conductivity is electric field dependent: σ(E)) fit experimental data better than linear models (i.e. tissue conductivity is constant). This was found for both single tissue and composite tissue. Our results of electric field distribution modeling in linear model of composite tissue (i.e. in the subcutaneous tumor model that do not take into account the relationship σ(E)) showed that a very high electric field (above irreversible threshold value) was concentrated only in the stratum corneum while the target tumor tissue was not successfully treated. Furthermore, the calculated volume of the target tumor tissue exposed to the electric field above reversible threshold in the subcutaneous model was zero assuming constant conductivities of each tissue. Our results also show that the inverse analysis allows for identification of both baseline tissue conductivity (i.e. conductivity of non-electroporated tissue) and tissue conductivity vs. electric field (σ(E)) of electroporated tissue. Conclusion: Our results of modeling of electric field distribution in tissues during electroporation show that the changes in electrical conductivity due to electroporation need to be taken into account when an electroporation based treatment is planned or investigated. We concluded that the model of electric field distribution that takes into account the increase in electric conductivity due to electroporation yields more precise prediction of successfully electroporated target tissue volume. The findings of our study can significantly contribute to the current development of individualized patient-specific electroporation based treatment planning.

Electrochemotherapy and electrogene therapy are new methods in molecular medicine based on electroporation-mediated introduction of foreign molecules (chemotherapeutic drugs, DNA) into target cells in vivo. Electrochemotherapy involves the injection of chemotherapeutic agent followed by a local delivery of a train of short high-voltage pulses to the tumor nodule (i.e. 8 square-wave pulses of 100 mus duration delivered at the repetition frequency of 1 Hz or several kHz, with a voltage-to-distance ratio of up to 1500 V/cm). For the transfer of DNA across a cell membrane a train of long low-voltage pulses (i.e. 8 rectangular pulses of 50 ms duration delivered at the repetition rate of 1 Hz, with a voltage-to-distance ratio up to 250 V/cm) is much more effective due to the electrophoretic effect on DNA molecule. In this paper we present a comprehensive analysis of tissue heating as a potential side effect of electric pulses used for electroporation-based treatments. The analysis is based on a coupled electrothermal model using 3-D finite-element approach. We studied two electrode geometries: parallel plates and a pair of needles. By setting the appropriate boundary conditions, we simulated driving of electrodes with short, high-voltage, electropermeabilizing pulses and with longer, lower voltage, electrophoretic pulses. We obtained time dependent solutions for electric field and temperature distribution by FEM solver. Based on the numerical simulations we analyzed the influence of tissue electrical conductivity and parameters of electric pulses (amplitude, duration, number of pulses, pulse repetition frequency) on the temperature distribution within the tissue and the electrodes. Results of our simulations show that at specific pulse parameters at least locally tissue heating might be significant (i.e. tissue temperatures to grow in excess of 43degC). For electrochemotherapy, this is not critical, but DNA electrotransfer may be unsuccessful due to heating-related DNA damage or denaturation.

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.