Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA.

Acute intestinal ischemia is a life-threatening condition. The current gold standard, with evaluation based on visual and tactile sensation, has low specificity. In this study, we explore the feasibility of using machine learning models on images of the intestine, to assess small intestinal viability. A digital microscope was used to acquire images of the jejunum in 10 pigs. Ischemic segments were created by local clamping (approximately 30 cm in width) of small arteries and veins in the mesentery and reperfusion was initiated by releasing the clamps. A series of images were acquired once an hour on the surface of each of the segments. The convolutional neural network (CNN) has previously been used to classify medical images, while knowledge is lacking whether CNNs have potential to classify ischemia-reperfusion injury on the small intestine. We compared how different deep learning models perform for this task. Moreover, the Shapley additive explanations (SHAP) method within explainable artificial intelligence (AI) was used to identify features that the model utilizes as important in classification of different ischemic injury degrees. To be able to assess to what extent we can trust our deep learning model decisions is critical in a clinical setting. A probabilistic model Bayesian CNN was implemented to estimate the model uncertainty which provides a confidence measure of our model decisions.

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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.