Dysfunctions of the autonomic nervous system (ANS) have been associated with a variety of diseases including hypertension, heart failure, obesity, diabetes, sleep apnea, and mental disorders. Noninvasive assessment of ANS has become a challenging and potential tool in the prognosis, diagnosis, and followup of the former diseases. It is also of great interest for the understanding of the regulation of cardiovascular system and its response to perturbations. Since ANS activity prints characteristic patterns in biological signals, information about ANS can be noninvasively derived through the processing of such signals. Analysis of heart rate variability (HRV) at rest is one of such approaches widely used and accepted. However, it is not the only one. Analysis of blood pressure variability (BPV), pulse rate variability (PRV, as a surrogate of HRV), pulse transit time (PTT), or heart rate turbulences (HRT) provides valuable information about ANS activity, which whether it is complementary to HRV or not is still a matter of debate. Moreover, the latest trends in noninvasive assessment of ANS are devoted to its characterization in nonstationary situations, where the system needs to adapt to changing conditions and perturbations.

This special issue will focus on advanced and innovative methods to evaluate ANS through signal processing, with particular interest in HRV analysis during nonstationary conditions, such as exercise testing, and in response to perturbations, such as stress, fatigue, emotional state, or disease. Papers dealing with alternative or complementary methods to assess ANS activity, such as baroreflex sensitivity (BRS), PRV, PTT, and HRT, are welcome. Moreover, manuscripts proposing new models for a comprehensive description and understanding of ANS print on biological signals would be of great interest. Potential topics include, but are not limited to:

• Analysis and interpretation of cardiovascular signals (HRV, BPV, PRV, and PTT) during nonstationary conditions
• Analysis of HRT
• Noninvasive assessment of ANS through multimodal signal processing
• Noninvasive assessment of ANS in stress, fatigue, and emotional states
• Noninvasive assessment of ANS during anesthesia
• Proposal of new parameters obtained from the processing of biological signals with clinical relevance in the prognosis and diagnosis of diseases (hypertension, heart failure, ischemia, obesity, diabetes, sleep apnea, and mental disorders)
• Modeling of ANS response to perturbations
• Real-time and low-cost techniques for noninvasive assessment of ANS for telemedicine, mobile applications, and wearable systems
• Analysis of skin temperature and skin galvanic resistance as alternative or complementary measures of ANS activity

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