Time Series Analysis using Embedding Dimension on Heart Rate Variability

Heart Rate Variability (HRV) is the measurement sequence with one or more visible variables of an underlying dynamic system, whose state changes with time. In practice, it is difficult to know what variables determine the actual dynamic system. In this research, Embedding Dimension (ED) is used to find out the nature of the underlying dynamical system. False Nearest Neighbour (FNN) method of estimating ED has been adapted for analysing and predicting variables responsible for HRV time series. It shows that the ED can provide the evidence of dynamic variables which contribute to the HRV time series. Also, the embedding of the HRV time series into a four-dimensional space produced the smallest number of FNN. This result strongly suggests that the Autonomic Nervous System that drives the heart is a two features dynamic system: sympathetic and parasympathetic nervous system.Peer reviewedFinal Published versio

A Data Fusion System to Study Synchronization in Social Activities

As the world population gets older, the healthcare system must be adapted, among others by providing continuous health monitoring at home and in the city. The social activities have a significant role in everyone health status. Hence, this paper proposes a system to perform a data fusion of signals sampled on several subjects during social activities. This study implies the time synchronization of data coming from several sensors whether these are embedded on people or integrated in the environment. The data fusion is applied to several experiments including physical, cognitive and rest activities, with social aspects. The simultaneous and continuous analysis of four subjects cardiac activity and GPS coordinates provides a new way to distinguish different collaborative activities comparing the measurements between the subjects and along time.Comment: Healthcom 201

Deriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition

Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.Comment: 12 pages, 6 figures. Will be published on "Chaos, Solitons and Fractals

Body Fat is Associated with Decreased Endocrine and Cognitive Resilience to Acute Emotional Stress

*Objective:* Cortisol is elevated both in individuals with increased emotional stress as well as with higher percentages of body fat. Cortisol is also known to affect cognitive performance, particularly spatial processing, selective attention, and working memory. We hypothesized that increased body fat might therefore be associated with decreased performance on a spatial processing task, in response to an acute real-world stressor. 

*Design:* We tested two separate samples of subjects undergoing their first (tandem) skydive. In the first sample (N=78), subjects were tested for salivary cortisol and state-anxiety (Spielberger State Anxiety Scale) during the plane's fifteen-minute ascent to altitude in immediate anticipation of the jump. In a second sample (N=20), subjects were tested for salivary cortisol, as well as cardiac variables (heart rate, autonomic regulation via heart rate variability) and performance on a cognitive task of spatial processing, selective attention, and working memory. 

*Results:* In response to the skydive, individuals with greater body fat percentages showed significantly increased reactivity for both cortisol (on both samples) and cognition, including decreased accuracy of our task of spatial processing, selective attention, and working memory. These cognitive effects were restricted to the stress response and were not found under baseline conditions. There were no body fat interactions with cardiac changes in response to the stressor, suggesting that the cognitive effects were specifically hormone-mediated rather than secondary to general activation of the autonomic nervous system. 

*Conclusions:* Our results indicate that, under real-world stress, increased body fat may be associated with endocrine stress-vulnerability, with consequences for deleterious cognitive performance

Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs

We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to design procedures for detecting and, more importantly, predicting PAF episodes. We propose a method for predicting PAF events whose first step consists of a feature extraction procedure that represents each ECG as a multi-variate time series. Successively, we design a classification framework based on kernel similarities for multi-variate time series, capable of handling missing data. We consider different approaches to perform classification in the original space of the multi-variate time series and in an embedding space, defined by the kernel similarity measure. We achieve a classification accuracy comparable with state of the art methods, with the additional advantage of detecting the PAF onset up to 15 minutes in advance