By Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo

This e-book explores Autonomic worried procedure (ANS) dynamics as investigated via Electrodermal job (EDA) processing. It offers groundbreaking examine within the technical box of biomedical engineering, in particular biomedical sign processing, in addition to medical fields of psychometrics, affective computing, and mental review. This quantity describes one of the most whole, potent, and customized methodologies for extracting information from a non-stationary, nonlinear EDA sign with a purpose to represent the affective and emotional kingdom of a human topic. those methodologies are underscored by means of dialogue of real-world purposes in temper evaluation. The textual content additionally examines the physiological bases of emotion attractiveness via noninvasive tracking of the autonomic apprehensive approach. this can be an incredible e-book for biomedical engineers, physiologists, neuroscientists, engineers, utilized mathmeticians, psychiatric and mental clinicians, and graduate scholars in those fields.

This booklet additionally:

Expertly introduces a unique process for EDA research according to convex optimization and sparsity, an issue of speedily expanding curiosity
Authoritatively provides groundbreaking study accomplished utilizing EDA as an exemplary biomarker of ANS dynamics
Deftly explores EDA's capability as a resource of trustworthy and powerful markers for the evaluation of emotional responses in fit matters, in addition to for the popularity of pathological temper states in bipolar patients

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Extra info for Advances in Electrodermal Activity Processing with Applications for Mental Health: From Heuristic Methods to Convex Optimization

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Each impulse from either signal could only match a single impulse from the other one. Times in T e not matching any element of T s were considered false positives (FP) while times in T s not matching any element of T e were considered false negatives (FN). Finally, the performance of the algorithm was measured in terms of sensitivity, computed as the fraction of matched elements of T s , and positive predictive value (PPV), computed as the fraction of matched elements of T e . In the respiratory stimulation dataset, the presence of an estimated burst of SMNA activity was verified in each 5-s time window following a stimulus onset, in order to prove the model’s ability to correctly detect real SCRs.

The last aspect is probably related to the fact that emotions (at least those considered primary ones as anger, surprise, happiness, sadness, fear and disgust) are not only present in humans but are universally expressed in the same way. Studies on facial expressions by the American psychologist Paul Ekman confirm this idea: we can feel fear for different things, but always in the same way [101]. Of note, the time profile of the emotions changes rapidly both in the onset, and during the phase of extinction.

The effective assessment of the sympathetic dynamics, in fact, can have important diagnostic impact and is one of the major fields of interest in cardiovascular research [86]. The most common way to assess the ANS dynamics is to compute the PSD of the HRV [87]. 04 Hz. Therefore the ratio between LF and HF, which was typically used to assess the ANS balance, is not fully accepted as an accurate measure of the balance between sympathetic and parasympathetic systems, since the LF band also contains parasympathetic dynamics.

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