Heart Rate Variability (HRV) reflects the balance between sympathetic and parasympathetic activity. Slower HRV rhythms (LF) indicate increased sympathetic and/or lower vagal activity, wakefulness characteristics, while faster HRV rhythms (HF) indicate lower sympathetic and/or increased parasympathetic and vagal activity, sleepy characteristics. In this work we demonstrate that power spectral analysis of drivers' heart rate can report driving errors caused by sleepiness. Furthermore, variation of Fractal Dimension (FD) can aid significant information for the assessment of the driving situation. ECG and EEG data were collected from sleep-deprived subjects exposed to real field driving conditions. A lower ratio of low frequency to high frequency components (LF/HF), and lower LF values were reported on the occurrence of driving errors.

In this work we investigate the synchronization of the dynamic behaviour of heart rate (ECG) and brain (EEG) signals using sample entropy as a measure of complexity. EEG and ECG recordings were collected during experiment with sleep-deprived subjects exposed to real field driving conditions. The degree to which brain and heart complexity loose complexity in a synchronous manner, indicating a possible interaction between the two systems is investigated. Preliminary results obtained from the examination of four subjects show the existence of a weak-to-intermediate cross-correlation between these pairs of biological oscillators. Furthermore, the frequency content in both heart rate and brain signals was calculated via power spectrum analysis and the association of synchronisation patterns with prevalent frequencies in the two systems was investigated. IntroductionOne of the main reasons for many fatal road accidents is the fatigue of sleep-derived drivers. Drowsiness leads to lengthen reaction time, decreases vigilance and attention and slows information processing [1]. Heart Rate Variability (HRV) and electroencephalogram (EEG) are two physiological factors that co-vary with drowsiness levels [2-6] addressing an underlying common central mechanism. EEG is a brain activity measure, able to track variations in alertness [7] while drowsiness estimation can be achieved with the use only of central and posterior channels [8]. Spectral analysis of HRV indicates drivers fatigue by increased High Frequencies (HF) and decreased Low Frequencies (LF) and LF/HF [9].The association of EEG and ECG recordings using frequency analysis [10] demonstrated an inverse correlation between delta band in EEG and LF, LF/HF from HRV analysis, suggesting that sympathetic nervous activities became decreased with sleep deepening and increased with sleep lightening. This study aims to determine possible correlations between the dynamic behaviour of heart rate (ECG) and brain (EEG) signals in order to explore the interaction between these two systems for sleep-derived drivers in real field conditions. The complexity measure employed is the sample entropy which is a probabilistic estimate of the pointwise match, within a tolerance, between the two signals. Synchronisation patterns are also investigated between HF and theta waves (4-8Hz), which is the predominant frequency during the transition state between wakefulness and sleep [11]. MethodsTwenty one subjects (20 male and 1 female) with a mean age of 26.5 years were participated in the real field driving experiment, which was performed in CERTH, Thessaloniki, Greece, from 6 June to 27July 2007, using the CERTH experiment car. The participants remained awake at least 24h before the experiment under supervision.Electrodes attached to the subject, ensured the acquisition of EEG and ECG signals through an ambulatory monitoring system, supported by a battery. An experienced driving instructor was seated at the codriver's seat. A technician monitoring the functioning of the recording equipment was in...

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Exercise constitutes an important intervention aiming to improve health and quality of life for several categories of patients. Personalized exercise prescription is a rather complicated issue, requiring several aspects to be taken into account, e.g. patient's medical history and response to exercise, medication treatment, personal preferences, etc. The present work proposes an ontology-based framework designed to facilitate healthcare professionals in personalized exercise prescription. The framework encapsulates the necessary domain knowledge and the appropriate inference logic, so as to generate exercise plan suggestions based on patient's profile. It also supports readjustments of a prescribed plan according to the patient's response with respect to goal achievement and changes in physical-medical status. An instantiation of the proposed framework for cardiac rehabilitation illustrates the virtue and the applicability of this work.

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