Abstract
The characterization of heart rate variability (HRV) series has become important for clinical diagnosis. These series are non-stationary and exhibit long and short-range correlations. The non-parametric methodology detrended fluctuation analysis (DFA) has become widely used for the detection of these correlations. The standard procedure is to apply DFA to the RR series, estimating the desired scaling exponents. In this work we pursue an alternative approach which consists in applying DFA to the fractionally differenced RR series, Delta(RR)-R-d, where 0 < d < 1 is the long-range correlation parameter. Both methodologies are applied to 24 hour HRV series from the Noltisalis data base. We conclude that changes in HRV are better quantified by DFA scaling exponents calculated over fractionally differenced RR series than by the standard procedure. The results indicate that the scaling exponent corresponding to high frequencies obtained from Delta(RR)-R-d increases the discriminatory power among the groups: from 60% to 87% during the day period and 57% to 77% during the night period.

Abstract
This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different components of HRV memory (short and long) and are extracted from HRV recordings using parametric as well as non parametric methods. Thus, the parameters are: spectral components at low frequencies (LH) and high frequencies (HF) which are associated with the short memory of HRV and the long memory parameter (d) obtained from autoregressive fractionally integrated moving average (ARFIMA) models. In the non parametric context, short memory (a1) and long memory (a2) parameters are obtained from detrended fluctuation analysis (DFA). The sample used in this study contains 24-hour Holter HRV recordings of 30 subjects: 10 healthy individuals, 10 patients suffering from congestive heart failure and 10 heart transplanted patients from the Noltisalis database. It was found that short memory parameters present higher values for the healthy individuals whereas long memory parameters present higher values for the diseased individuals. Moreover, there is evidence that ARFIMA modeling allows the discrimination between the 3 groups under study, being advantageous over DFA.

Abstract
Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation. (C) 2013 AIP Publishing LLC

Abstract
Long recordings of heart rate variability (HRV) display non-stationary characteristics and exhibit long- and short-range correlations. The nonparametric methodology detrended fluctuation analysis (DFA) has become a widely used technique for the detection of long-range correlations in non-stationary HRV data. Recently, we have proposed an alternative approach based on fractional integrated autoregressive moving average (ARFIMA) modelling. These models are an extension of the AR models usual in HRV analysis and have special interest for applications because of their ability for modelling both short- and long-term behaviour of a time series. In this work, DFA is used to assess also short-range scales, further characterizing the data. The methods are applied to 24 h HRV recordings from the Noltisalis database, collected from healthy subjects, patients suffering from congestive heart failure and heart transplanted patients. The analysis of short-range scales leads to a better discrimination between the different groups. © 2013, Springer-Verlag Berlin Heidelberg.

Abstract
Emergence of the social web has brought new powerful web applications that connect people in a global scale and allow them to reap the benefits of social life from online virtual environments. Taking into account the strengths and weaknesses of online social networks and tourism information systems, we propose an evolved version of the Toursplan information system, backed up by an online social network. This way, Toursplan can exploit social data to generate collective intelligence in the tourism and travel domains, and improve the quality of its recommendation and planning services. © 2013 ACM.

Abstract
Agent technology has been applied in e-commerce to help coping with problems that arise due to its rapid growth. However, despite the amount of research in this area, the level of automation achieved is still limited. This is mainly due to the natural diversity existent in e-commerce environments, where agents may possess different conceptualizations about their needs and capabilities, giving rise to interoperability issues. In this paper we approach this problem and present the AEMOS system as a possible solution. AEMOS is an agent-based e-commerce platform that includes ontology matching services facilitating the interoperability between agents that have different conceptualizations about the same domain of knowledge. The system also explores emergent social networks in order to improve its efficiency by enhancing its ontology matching services and supporting agents in their decisions. © Springer International Publishing Switzerland 2013.