Development of a Smart Wireless Multisensor Platform for an Optogenetic Brain Implant.

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.

The Five Basic Human Senses Evoke Electrodermal Activity.

Electrodermal activity (EDA) usually relates to variations in the electrical properties of palmar or plantar skin sites. EDA responses, namely skin conductance responses (SCRs), skin potential responses (SPRs) and skin susceptance responses (SSRs) are shown to be sensitive indexes of sympathetic nervous system activation and are studied in many research projects. However, the association between EDA responses and the five basic human senses has not been investigated yet. Our study aimed to explore the relationship between the three EDA responses (SCRs, SSRs and SPRs) and the five basic human senses. These three EDA responses were measured simultaneously at the same skin site on each of the 38 volunteers. The tested five senses were sight, hearing, touch, taste and smell. The results showed that the different tested senses led to different degrees of EDA responses due to activation of the sympathetic nervous system and corresponding secretion of sweat. Although a controlled study on the degree of EDA as a function of the strength of each stimulus was not performed, we noted that the largest EDA responses were typically associated with the smell sense test. We conclude that EDA responses could be utilized as measures for examining the sensitivity of the human senses. Hence, EDA devices may have important roles in sensory systems for future clinical applications.

Detection of physiological concentrations of GABA using dielectric spectroscopy - A pilot study.

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.

Development of a prototype toe sensor for detection of diabetic peripheral small fiber neuropathy.

Diabetic peripheral neuropathy (DPN) affects a large proportion of people with diabetes, and early detection is essential to prevent further progression. Widespread clinical testing relies on simplicity and cost-effectiveness of examination. Early signs of DPN may be detected by assessing the sudomotor nerves, and sudomotor activity can be measured by bioimpedance. We present a prototype toe probe for DPN detection including sensors for measuring skin AC conductance, skin temperature and humidity. The prototype was tested on five participants with DPN and five healthy age-matched controls in a pilot study. Sudomotor sensor responses to a simple deep breathing test were very weak or absent in the DPN group, with all controls having larger responses than the DPN group. Evaporation was lower for the DPN group, and skin temperature was higher on average. For the same foot, the results for sudomotor responses were in agreement with sensory neurography amplitudes from the sural nerve whereas the monofilament test gave normal results for two of the DPN participants. If sufficient detection accuracy is confirmed in larger studies, the method may provide a simple and cost-effective tool to support clinical examination. Clinical Relevance- We present the early realization and testing of a simple device to support early detection of diabetic peripheral neuropathy.

Influence of ambient temperature on tonic and phasic electrodermal activity components.

Objective.Electrodermal activity (EDA) is a reliable indicator for variations in the skin electrical properties attributed to sympathetic nerve system activity. EDA recordings can be influenced by various internal and external factors including environmental ones. Ambient temperature can be considered as one of the possible factors which might influence EDA recordings. Hence, this study aimed to precisely investigate influence of ambient temperature on tonic and phasic EDA components by employing a new EDA measurement technique, which depends on simultaneously recording of several EDA parameters.Approach.Tonic and phasic EDA components during three different ambient temperature levels were recorded from 36 healthy participants. In addition, for evoking electrodermal responses, participants were exposed to cognitive, visual and breathing external stimuli.Main results.Significant effects of temperature on tonic skin conductance (SC), skin susceptance (SS) and skin potential (SP) were obtained, whereas such significant effects were not observed with phasic SC, SS and SP. Tonic EDA parameters were increased as a function of temperature, but changes in phasic component were fluctuating.Significance.This should mean that, keeping recording of tonic EDA component in normal room temperature is highly crucial, but for recording or analysis of phasic component it is not important as they are more robust in this context. This is important in applications of EDA instruments, particularly in wearable devices where environmental temperature typically cannot be controlled.

Current trends and opportunities in the methodology of electrodermal activity measurement.

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

Diabetic Foot Assessment using Skin Impedance in a Custom Made Sensor-sock.

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.

Bioimpedance-based Authentication of Defrosted Versus Fresh Pork at the End of Refrigerated Shelf Life.

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 [102-106 Hz]. Weight, pH and electrode positioning were assessed to test for potential confounding effects. Statistics for treatment dependent differences were based on the established Py parameter and phase angle, which were extracted from the BI spectra. We found that using bioimpedance testing with tetrapolar electrodes, Py 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 Py, only one fresh sample (5% of Ntotal=20) fell in the lowest Py 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 Py 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.

Skin Electrical Resistance as a Diagnostic and Therapeutic Biomarker of Breast Cancer Measuring Lymphatic Regions.

Skin changes associated with alterations in the interstitial matrix and lymph system might provide significant and measurable effects due to the presence of breast cancer. This study aimed to determine if skin electrical resistance changes could serve as a diagnostic and therapeutic biomarker associated with physiological changes in patients with malignant versus benign breast cancer lesions. Forty-eight women (24 with malignant cancer, 23 with benign lesions) were enrolled in this study. Repeated skin resistance measurements were performed within the same session and 1 week after the first measurement in the breast lymphatic region and non-breast lymphathic regions. Intraclass correlation coefficients were calculated to determine the technique's intrasession and intersession reproducibility. Data were then normalized as a mean of comparing cross-sectional differences between malignant and benign lesions of the breast. Six months longitudinal data from six patients that received therapy were analyzed to detect the effect of therapy. Standard descriptive statistics were used to compare ratiometric differences between groups. Skin resistance data were used to train a machine learning random forest classification algorithm to diagnose breast cancer lesions. Significant differences between malignant and benign breast lesions were obtained (p<0.01), also pre- and post-treatment (p<0.05). The diagnostic algorithm demonstrated the capability to classify breast cancer with an area under the curve of 0.68, sensitivity of 66.3%, specificity of 78.5%, positive predictive value 70.7% and negative predictive value 75.1%. Measurement of skin resistance in patients with breast cancer may serve as a convenient screening tool for breast cancer and evaluation of therapy. Further work is warranted to improve our approach and further investigate the biophysical mechanisms leading to the observed changes.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data.

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[Formula: see text], F[Formula: see text] and F[Formula: see text]-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.

A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators.

This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.

Electrical impedance myography: A critical review and outlook.

Electrical impedance myography (EIM) technology is finding application in neuromuscular disease research as a tool to assess muscle health. Correlations between EIM outcomes, functional, imaging and histological data have been established in a variety of neuromuscular disorders; however, an analytical discussion of EIM is lacking. This review presents an explanation for clinicians and others who are applying EIM and interpreting impedance outcomes. The background of EIM is presented, including the relation between EIM, volume conduction properties, tissue structure, electrode configuration and conductor volume. Also discussed are technical considerations to guide the reader to critically evaluate EIM and understand its limitations and strengths.

Classification of Emotions Based on Electrodermal Activity and Transfer Learning - a Pilot Study.

This paper describes the development, execution and results of an experiment assessing emotions with electrodermal response measurements and machine learning. With ten participants, the study was carried out by eliciting emotions through film clips. The data was gathered with the Sudologger 3 and processed with continuous wavelet transformation. A machine learning algorithm was used to classify the data with the use of transfer learning and random forest classification. The results showed that the experiment lays a foundation for further exploration in the field. The addition of augmented data strengthened the classification and proved that more data would benefit the machine learning algorithm. The pilot study brought to light several areas to help with the expansion of the study for larger scale assessment of emotions with electrodermal response measurements and machine learning for the benefit of fields like psychology.

Estimation of Heart Rate Variability from Finger Photoplethysmography During Rest, Mild Exercise and Mild Mental Stress.

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.

The correlations among the skin conductance features responding to physiological stress stimuli.

BACKGROUND/AIM: The skin conductance responses (SCRs) are a well-accepted indicator of physiological arousal for both research purposes and clinical approaches. The shape of SCRs is analyzed by various features. However, the estimation of how much (in %) one feature can explain another is still an open issue. The aim of this study was to assess whether variation in one SCR feature predicts changes in other features. METHODS: Skin conductance (SC) was measured during relaxation and mental stress in 40 subjects. SCRs were induced by three external stimuli, which were deep breath, a mental arithmetic, task and a visual task. RESULTS: The findings of this study showed that about 55% (R2  = 0.55) of the variation in the half recovery time (SCRs_rec 50%) can be explained by the rise time (SCRs_ris), whereas variation in amplitude of the skin conductance responses (SCRs_amp) and the skin conductance level (SCL) is independent and cannot be explained by the other features, as R2 values obtained from all analyses among these SCR features in average were lower 0.19. CONCLUSIONS: The study results suggest that the two timing phases (SCRs_rec and SCRs_ris) are not completely independent from each other, although they might be governed by different sweating mechanisms (secretion and reabsorption). However, SCRs_amp and SCL were independent. These findings can help in choosing the optimal set of features of an automated system for processing EDA, which reflect the alterations in the activation level generated during an emotional episode.

Simultaneous measurement of electrodermal activity components correlated with age-related differences.

Electrodermal activity (EDA) measurements are influenced by various factors. Age-related psychological and physiological changes may be considered as one of the possible factors which may influence EDA measurements. In order to properly investigate the effects of such factors on EDA, techniques of precisely and simultaneously recording more than one EDA parameter are recommended. This study aims to explore the impact of age-related differences on EDA components through employing a new measuring technique, which is composed of a small front-end electronic box, DAQ card, and a laptop running LabVIEW software. It is dependent on the simultaneous recording of three EDA parameters: skin conductance (SC), skin potential (SP), and skin susceptance (SS) at the same skin site. EDA components as results of breathing, mathematical tasks, and image stimuli were recorded from 60 healthy participants simultaneously at the same skin site. They were categorized by age into young adults (ages 18-25), middle-aged adults (ages 30-40), and old adults (ages 50-70) years. It was found that skin potential responses (SPRs), and skin conductance level (SCL) (p < 0.001), were significantly decreased due to aging, but changes in other EDA parameters were nonsignificant (p > 0.05). Moreover, both tonic and phasic SS were the least affected and found to be more robust than SC and SP with respect to aging. The study suggests that it is important to take age into account in research studies where the mean aim of the study is to compare EDA parameters; however, in the meantime, the results from our small number and specific study population cannot be generalized to clinical applications.

Towards exhaustive electromembrane extraction under stagnant conditions.

Electromembrane extraction (EME) in small, stagnant and chip-like devices has the potential for future in-field operation. Literature briefly discuss such systems, but performance suffered from evaporative losses of sample and acceptor. To address this, the current paper reports electromembrane extraction (EME) of five basic drugs (model analytes) from aqueous buffer solutions and whole blood samples under stagnant conditions in a completely closed system. A laboratory-made polyoxymethylene (POM) well plate served as compartment for the sample solution, while a commercially available well filter plate was used to immobilize 2-nitrophenyl octyl ether (NPOE) as supported liquid membrane (SLM) and as closed compartment for the acceptor solution. Major design parameters (sample compartment and electrode geometry) and operational parameters (sample volume, voltage and extraction time) were investigated and optimized. Electrode geometry was not very critical, but extraction efficiency increased with decreasing sample volume. Extraction from 50 µL aqueous buffer solution for 60 min and with a voltage of 75 V was considered exhaustive (sample was depleted), with recoveries ranging between 75% and 87% for loperamide, haloperidol, methadone, nortriptyline, and pethidine (RSD: 2-12%). Extraction from whole blood samples under optimized conditions yielded slightly lower recoveries, ranging between 57 and 96% (RSD: 3-12%). Stagnant EME was evaluated in combination with liquid chromatography-mass spectrometry (LC-MS) as a highly specific instrumental method, and provided evaluation data on methadone from blood samples in accordance with regulatory requirements (LOD: 0.4 ng/mL, LOQ: 1.4 ng/mL, RSD: 6-20%). This work has improved upon the design of stagnant EME, moving it further towards a viable in-field operation device.

Information can be stored in the human skin memristor which has non-volatile memory.

Much is already understood about the anatomical and physiological mechanisms behind the linear, electrical properties of biological tissues. Studying the non-linear electrical properties, however, opens up for the influence from other processes that are driven by the electric field or movement of charges. An electrical measurement that is affected by the applied electrical stimulus is non-linear and reveals the non-linear electrical properties of the underlying (biological) tissue; if it is done with an alternating current (AC) stimulus, the corresponding voltage current plot may exhibit a pinched hysteresis loop which is the fingerprint of a memristor. It has been shown that human skin and other biological tissues are memristors. Here we performed non-linear electrical measurements on human skin with applied direct current (DC) voltage pulses. By doing so, we found that human skin exhibits non-volatile memory and that analogue information can actually be stored inside the skin at least for three minutes. As demonstrated before, human skin actually contains two different memristor types, one that originates from the sweat ducts and one that is based on thermal changes of the surrounding tissue, the stratum corneum; and information storage is possible in both. Finally, assuming that different physiological conditions of the skin can explain the variations in current responses that we observed among the subjects, it follows that non-linear recordings with DC pulses may find use in sensor applications.

A multiparameter model for non-invasive detection of hypoglycemia.

OBJECTIVE: Severe hypoglycemia is the most serious acute complication for people with type 1 diabetes (T1D). Approximately 25% of people with T1D have impaired ability to recognize impending hypoglycemia, and nocturnal episodes are feared. APPROACH: We have investigated the use of non-invasive sensors for detection of hypoglycemia based on a mathematical model which combines several sensor measurements to identify physiological responses to hypoglycemia. Data from randomized single-blinded euglycemic and hypoglycemic glucose clamps in 20 participants with T1D and impaired awareness of hypoglycemia was used in the analyses. MAIN RESULTS: Using a sensor combination of sudomotor activity at three skin sites, ECG-derived heart rate and heart rate corrected QT interval, near-infrared and bioimpedance spectroscopy; physiological responses associated with hypoglycemia could be identified with an F1 score accuracy up to 88%. SIGNIFICANCE: We present a novel model for identification of non-invasively measurable physiological responses related to hypoglycemia, showing potential for detection of moderate hypoglycemia using a wearable sensor system.

Author Correction: Evaluation of Hypoglycaemia with Non-Invasive Sensors in People with Type 1 Diabetes and Impaired Awareness of Hypoglycaemia.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.