The results indicate that the proposed algorithm for the cuffless estimation of the BP can potentially enable mobile health-care gadgets to monitor the BP continuously.

Recently a few methods have been proposed in the literature for non-invasive cuff-less estimation of systolic and diastolic blood pressures. One of the most prominent methods is to use the Pulse Transit Time (PTT). Although it is proven that PTT has a strong correlation with the systolic and diastolic blood pressures, this relation is highly dependent to each individuals physiological properties. Therefore, it requires per person calibration for accurate and reliable blood pressure estimation from PTT, which is a big drawback. To alleviate this issue, in this paper, a novel method is proposed for accurate and reliable estimation of blood pressure that is calibration-free. This goal is accomplished by extraction of several physiological parameters from Photoplethysmography (PPG) signal as well as utilizing signal processing and machine learning algorithms. The results show that the accuracy of the proposed method achieves grade B for the estimation of the diastolic blood pressure and grade C for the estimation of the mean arterial pressure under the standard British Hypertension Society (BHS) protocol.978-1-4799-8391-9/15/$31.00 ©2015 IEEE

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10The brain is constantly anticipating the future of sensory inputs based on past 11 experiences. When new sensory data is different from predictions shaped by recent 12 trends, neural signals are generated to report this surprise. Existing models for 13 quantifying surprise are based on an ideal observer assumption operating under one 14 of the three definitions of surprise set forth as the Shannon, Bayesian, and 15 Confidence-corrected surprise. In this paper, we analyze both visual and auditory 16 EEG and auditory MEG signals recorded during oddball tasks to examine which 17 temporal components in these signals are sufficient to decode the brain's surprise 18 based on each of these three definitions. We found that for both recording systems 19 the Shannon surprise is always significantly better decoded than the Bayesian 20 surprise regardless of the sensory modality and the selected temporal features used 21 for decoding. 22 squared of regression model, ERP temporal components, generative distribution, 24 decoding power, middle temporal components, late temporal components, mismatch 25 negativity (MMN), P300, oddball task 26 Author summary 27 A regression model is proposed for decoding the level of the brain's surprise in 28 response to sensory sequences using selected temporal components of recorded EEG 3 29 and MEG data. Three surprise quantification definitions (Shannon, Bayesian,and 30 Confidence-corrected surprise) are compared in offering decoding power. Four 31 different regimes for selecting temporal samples of EEG and MEG data are used to 32 evaluate which part of the recorded data may contain signatures that represent the 33 brain's surprise in terms of offering a high decoding power. We found that both the 34 middle and late components of the EEG response offer strong decoding power for 35 surprise while the early components are significantly weaker in decoding surprise. In 36 the MEG response, we found that the middle components have the highest decoding 37 power while the late components offer moderate decoding powers. When using a 38 single temporal sample for decoding surprise, samples of the middle segment possess 39 the highest decoding power. Shannon surprise is always better decoded than the 40 other definitions of surprise for all the four temporal feature selection regimes. 41 Similar superiority for Shannon surprise is observed for the EEG and MEG data 42 across the entire range of temporal sample regimes used in our analysis. 43 45 next sensory input. Past inputs are used by the brain to form prior knowledge in the 46 Bayesian brain model [2,3]. In fact, the results of brain functions such as perception, 47 eliminating ambiguity, attention, and decision making are dependent on the way the 48 current sensory input and the knowledge gained from previous experiences are 49 combined in the hierarchical inference model of the brain [1] (for review see [4]). 50An input different from what the brain has predicted will be surprising in that it 51 generates a form of response measurable by brain ...

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Surprise and social influence are linked through several neuropsychological mechanisms. By garnering attention, causing arousal, and motivating engagement, surprise provides a context for effective or durable social influence. Attention to a surprising event motivates the formation of an explanation or updating of models, while high arousal experiences due to surprise promote memory formation. They both encourage engagement with the surprising event through efforts aimed at understanding the situation. By affecting the behavior of the individual or a social group via setting an attractive engagement context, surprise plays an important role in shaping personal and social change. Surprise is an outcome of the brain’s function in constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from the brain’s predictions shaped by recent trends, distinct neural signals are generated to report this surprise. As a quantitative approach to modeling the generation of brain surprise, input stimuli containing surprising elements are employed in experiments such as oddball tasks during which brain activity is recorded. Although surprise has been well characterized in many studies, an information-theoretical model to describe and predict the surprise level of an external stimulus in the recorded MEG data has not been reported to date, and setting forth such a model is the main objective of this paper. Through mining trial-by-trial MEG data in an oddball task according to theoretical definitions of surprise, the proposed surprise decoding model employs the entire epoch of the brain response to a stimulus to measure surprise and assesses which collection of temporal/spatial components in the recorded data can provide optimal power for describing the brain’s surprise. We considered three different theoretical formulations for surprise assuming the brain acts as an ideal observer that calculates transition probabilities to estimate the generative distribution of the input. We found that middle temporal components and the right and left fronto-central regions offer the strongest power for decoding surprise. Our findings provide a practical and rigorous method for measuring the brain’s surprise, which can be employed in conjunction with behavioral data to evaluate the interactive and social effects of surprising events.

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