Programpunkter

Electrodermal activity (EDA) reflects sympathetic nervous system activitythrough sweating-related changes in skin conductance. Decomposition analysisis used to deconvolve the EDA into slow and fast varying tonic and phasicactivity, respectively. In this study, we used machine learning models to comparethe performance of two EDA decomposition algorithms to detect emotions such asamusing, boring, relaxing, and scary. The EDA data considered in this study wereobtained from the publicly available Continuously Annotated Signals of Emotion(CASE) dataset. Initially, we pre-processed and deconvolved the EDA data intotonic and phasic components using decomposition methods such as cvxEDA andBayesianEDA. Further, twelve-time domain features were extracted from the phasiccomponent of EDA data. Finally, we applied machine learning techniques suchas logistic regression (LR) and support vector machine (SVM), to evaluate the performanceof the decomposition method. Our results imply that the BayesianEDAdecomposition method outperforms the cvxEDA. The mean of first derivative featurediscriminated all the considered emotional pairs with high statistical significance(p<0.05). SVM was able to detect emotions better than the LR classifier.We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision,and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively,using BayesianEDA and SVM classifier. The proposed framework can be utilizedto detect emotional states for the early diagnosis of psychological conditions.

Ai in its current form and function is unrealistic unsupportable and for some unattainable economically speaking. A vision of democratizing and commoditizing Ai while running at scale never seen. In addition how can this new user not human never takes a break be managed without overwhelming the clinical staff.Presented will be a challenge to all vendors in the industry and the future. It’s time for a change.Presented will be an idea for a platform that leverages existing application as we manage human/clinical driven workflows vs data/machine learning workflows going forward.

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The aim of this work is to develop and evaluate a multi-stage procedure model for the identification of use problems and optimization of usability using biosignal data. The concept is divided into 5 steps: 1. static analysis of data to identify use problems; 2. conducting interviews within the context of use and requirements analysis to investigate problems in more detail; 3. developing new interface concepts to implement the requirements and a prototype of an interface including dynamic visualization of data; 4. formative evaluation using an unmoderated remote usability test; 5. usability test with realistic scenarios and influencing factors in the simulation room. The concept was evaluated in the ventilation setting as an example. The procedure allowed the identification of use problems in the ventilation of patients as well as the development of suitable concepts and their evaluation to counteract use problems. To relieve users, ongoing analyses of biosignals with respect to the use problem are to be carried out. To overcome technical barriers, further development is needed in this area.