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Implantable cell replacement therapies promise to completely restore the function of neural structures, possibly changing how we currently perceive the onset of neurodegenerative diseases. One of the major clinical hurdles for the routine implementation of stem cell therapies is poor cell retention and survival, demanding the need to better understand these mechanisms while providing precise and scalable approaches to monitor these cell-based therapies in both pre-clinical and clinical scenarios. This poses significant multidisciplinary challenges regarding planning, defining the methodology and requirements, prototyping and different stages of testing. Aiming toward an optogenetic neural stem cell implant controlled by a smart wireless electronic frontend, we show how an iterative development methodology coupled with a modular design philosophy can mitigate some of these challenges. In this study, we present a miniaturized, wireless-controlled, modular multisensor platform with fully interfaced electronics featuring three different modules: an impedance analyzer, a potentiostat and an optical stimulator. We show the application of the platform for electrical impedance spectroscopy-based cell monitoring, optical stimulation to induce dopamine release from optogenetically modified neurons and a potentiostat for cyclic voltammetry and amperometric detection of dopamine release. The multisensor platform is designed to be used as an opto-electric headstage for future in vivo animal experiments.

Neuromorphic computing has the potential to facilitate detection of GABA concentration levels in the brain, and offers energy-efficient, real-time machine learning processing possibilities. To study whether neuromorphic computing can be used for GABA concentration detection, dielectric relaxation spectroscopy was used to acquire permittivity data of different concentrations of GABA solution. Thereafter, two different machine learning models were compared (Feedforward neural network (FFNN) and convolutional neural network (CNN)) for accuracy in prediction of GABA concentration from dielectric properties. The CNN model was then converted to spiking Neural Networks (SNNs), which showed promising results for energy efficiency and real-time processing capabilities. The system incorporates Tkinter, a Python interface to the Tcl/Tk GUI toolkit for seamless data transfer between the neuromorphic chip and the measurement system, ensuring flexibility and scalability in a user-friendly system.

Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter that is present at a relatively low level throughout the normal adult human brain. Abnormal GABA levels are found in people with neurodegenerative disorders such as Parkinson’s disease, epilepsy, schizophrenia, depression, and others. Being able to measure the GABA concentration would be beneficial for patient groups with fluctuating GABA levels for better diagnosis and treatment. In this study, we explore the feasibility of using dielectric relaxation spectroscopy for the detection of GABA concentrations within a physiological range, with the perspective of miniaturization and use during implantation. Utilizing machine learning techniques, we were able to differentiate GABA concentrations down to 5 μm. This work investigates a novel use of dielectric relaxation spectroscopy, to assess if physiological GABA concentrations can be detected through permittivity measurements.

In spite of the development in technology and the recent innovations in dentistry, dental anxiety remains a common issue, and accurately assessing it is challenging due to reliance on patients’ self-reports, which are often biased. Hence, this study was undertaken to determine whether dental anxiety can be quantified objectively using the EDA parameters. EDA (skin conductance (SC), skin susceptance (SS), and skin potential (SP)) parameters and heart rate (HR) were recorded from 40 participants during two different sessions (baseline and anxiety). In addition, the Modified Dental Anxiety Scale (MDAS) scale was also used to record the level of anxiety. The physiological data from EDA and HR were compared with the subjective self-reports of anxiety provided on the MDAS to show whether higher EDA and HR readings correspond to higher scores on the MDAS. To elicit dental anxiety, participants were exposed to several film clips associated with dental treatment. EDA signals were compared between the two sessions for all clips and all EDA scores. SC and HR significantly (p < 0.05) increased during the anxiety session compared to the baseline session. The number of fluctuations per minute in the SC, SS, and SP notably increased during the anxiety session. The MDAS results revealed that the participants had dental anxiety when they were exposed to video clips of dental procedures. The study results imply that EDA parameters could be used as a useful tool to monitor dental anxiety, in particular in young children and non-verbal patients or those with intellectual disabilities, which may aid the dentist in the successful management of dental anxiety during treatment. The moment-to-moment EDA data on a patient’s anxiety levels throughout particular dental operations provides a clearer picture of anxiety variations than pre- or post-appointment surveys alone, in addition to offering unbiased tracking of dental anxiety levels over self-reports. This study seeks to encourage further research into the most effective EDA parameters for improving the management of dental anxiety.

People with type 1 diabetes and impaired awareness of hypoglycaemia (IAH) are prone to severe hypoglycaemia. Previous attempts to develop non-invasive hypoglycaemia alarm systems have shown promising results, but it is not known if such alarms can detect severe hypoglycaemia in people with IAH. We aimed to explore whether a combination of non-invasive sensors could reliably evaluate hypoglycaemia (plasma glucose (PG) minimum 2.5 mmol/L) in people with IAH. Twenty participants with type 1 diabetes and IAH underwent randomly ordered, single blinded hyperinsulinemic euglycaemic and hyperinsulinemic hypoglycaemic clamps. Sweating, skin temperature, ECG, counterregulatory hormones and symptoms of hypoglycaemia were assessed. Overall, we were not able to detect clamp-induced hypoglycaemia with sufficient sensitivity and specificity for further clinical use. As a post-hoc analysis, we stratified participants according to their ability to identify hypoglycaemic symptoms during hypoglycaemic clamps. Five out of 20 participants could identify such symptoms. These participants had a significantly higher adrenaline response to hypoglycaemia (p < 0.001) and were reliably identified by sensors. Based on our observations, a non-invasive alarm system based on measurement of sweating responses and ECG changes during hypoglycaemia might provide an alert at a plasma glucose concentration around 2.5 mmol/L if an adequate sympatho-adrenal reaction is elicited.

Correct food labeling is a legal requirement and helps consumers to make informed purchasing choices. Mislabeling defrosted meat as fresh is illegal in the EU. However, there are no standardized technologies to authenticate fresh versus defrosted meat. We address this by testing if bioimpedance-based measurements can separate defrosted meat from refrigerated-only meat at the end of shelf life, i.e., when also fresh meat shows deterioration. Pork sirloin samples from 20 pigs were first tested at 12 days postmortem (‘fresh group’). This time point was chosen to represent a typical use-by date for refrigerated storage of fresh pork. Then, all samples were transferred to a -24°C freezer for 3 days and thawed for 2 days before final testing (‘frozen-thawed group’). Bioimpedance analyses (BIA) were done in a frequency range of [10 -10 Hz]. Weight, pH and electrode positioning were assessed to test for potential confounding effects. Statistics for treatment dependent differences were based on the established P parameter and phase angle, which were extracted from the BI spectra. We found that using bioimpedance testing with tetrapolar electrodes, P and phase angle allowed almost complete separation of fresh and previously frozen samples. However, within the whole sample population, there was some overlap between the spectra of fresh and frozen samples. Yet, based on P , only one fresh sample (5% of N =20) fell in the lowest P class with all the frozen samples. We used a multifactorial design that allowed to test the effects of potential confounding factors, such as electrode positioning and meat quality parameters. We found a relatively low explained variance for the P parameter, indicating that confounding effects from other factors or quality defects in fresh pork may affect the detection capacity of bioimpedance-based authentication of fresh pork. Our data, therefore, suggest that reliable fresh-label authentication with bioimpedance testing should be based on testing a small number of samples to represent a specific lot of pork that is to be inspected.

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F 1 , F 0.5 and F 2 -score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Diabetic peripheral neuropathy (DPN) may lead to several changes in the skin, and some of these may influence the skin impedance spectrum. In the present study we have developed a prototype solution for skin impedance spectroscopy at selected skin sites (big toe pulp, heel and toe ball) that was tested in a pilot study on five patients with DPN and five healthy controls. At the big toe, most of the controls had markedly lower impedance than the DPN group, especially in the range of 1-100 kHz. The separation between the groups seems to be weaker at the heel and weakest at the toeball. The results may indicate that monitoring of the skin impedance spectrum may be a method for detection of skin changes associated with DPN, encouraging further studies with the big toe sensor in particular.

An electrical measurement is non-linear when the applied stimulus itself affects the electrical properties of the underlying tissue. Corresponding voltage-current plots may exhibit pinched hysteresis loops which is the fingerprint of a memristor (memory resistor). Even though non-linear electrical properties have been demonstrated for different biological tissues like apples, plants and human skin, non-linear measurements as such have not been defined, yet. We are studying the non-linear properties of human skin systematically and initiate non-linear measurements on biological tissues as a field of research in general by introducing applicable recording techniques and parameterization. We found under which voltage stimulus conditions a measurement on human skin is non-linear and show that very low voltage amplitudes are already sufficient. The non-linear properties of human skin originate from the sweat ducts, as well as, from the surrounding tissue, the stratum corneum and we were able to classify the overall skin memristor as a generic memristor. Pinched hysteresis loops vary largely among subjects; an indication for the potential use in biomedical sensor applications.

Due to the possibilities in miniaturization and wearability, photoplethysmography (PPG) has recently gained a large interest not only for heart rate measurement, but also for estimating heart rate variability, which is derived from ECG by convention. The agreement between PPG and ECG-based HRV has been assessed in several studies, but the feasibility of PPG-based HRV estimation is still largely unknown for many conditions. In this study, we assess the feasibility of HRV estimation based on finger PPG during rest, mild physical exercise and mild mental stress. In addition, we compare different variants of signal processing methods including selection of fiducial point and outlier correction. Based on five minutes synchronous recordings of PPG and ECG from 15 healthy participants during each of these three conditions, the PPG-based HRV estimation was assessed for the SDNN and RMSSD parameters, calculated based on two different fiducial points (foot point and maximum slope), with and without outlier correction. The results show that HRV estimation based on finger PPG is feasible during rest and mild mental stress, but can give large errors during mild physical exercise. A good estimation is very dependent on outlier correction and fiducial point selection, and SDNN seems to be a more robust parameter compared to RMSSD for PPG-based HRV estimation.