Elisa Fornasa

Impact of Aging on Heart Rate Variability Properties of Healthy Subjects

Heart Rate Variability (HRV) has been studied in a variety of clinical situations in order to quantify the modulations in the heart rate associated to different pathological conditions. Nevertheless, significant changes in spectral and some nonlinear parameters of the HRV were reported also in normal subjects, depending on age and gender. The aim of this work was to quantify the age-related differences in other nonlinear parameters, particularly in the fractal dimension, of the HRV of healthy subjects and to compare the results with the changes showed by spectral measures. The RR time series extracted by the Holter monitoring of 60 healthy subjects, divided into three groups similar for both age and gender, were accurately analyzed. The results only partially revealed age-related changes both in the spectral and fractal HRV measures, underlining the need to carefully examine the RR data selection and the pre-processing phases.

Relationships between linear and nonlinear indexes of heart rate variability in obstructive sleep apnea syndrome

Heart rate variability (HRV) decrease has been described in patients with obstructive sleep apnea syndrome (OSAS). Nevertheless, time and frequency domain methods commonly used for HRV analysis are considered not completely reliable in case of respiratory and arrhythmic disorders. On the contrary, the nonlinear approach may supply powerful and promising tools to give a better insight of the autonomic control in OSAS. In this study, power-law β exponent, fractal dimension and approximate entropy parameters are calculated on the whole night recordings of 77 OSAS patients. The relationships between each pair of the parameters are quantified and discussed, also in comparison to the theoretical models.

EEG Analysis in Resting State and during a Memorization Task in Mild Cognitive Impairment and Subjective Cognitive Impairment

Mild Cognitive Impairment (MCI) and Subjective Cognitive Impairment (SCI) are brain disorders with a high risk to progress to Alzheimer’s disease. This work investigates the possible EEG differences in MCI and SMC subjects, with respect to control subjects (CS), both during rest and cognitive stimulation. EEG data were analyzed by decomposing the power spectral density into frequency sub-bands and by calculating the power-law beta exponent parameter. Moreover, from a nonlinear point of view, also Higuchi’s fractal dimension and Poincare plot indexes were estimated. Feature extraction was performed by using the Principal Component Analysis method, in an effort to distinguish CS, MCI and SCI patients. Results show that some parameters present statistically significant differences between SCI and control subjects, whereas MCI patients present intermediate values between the other two groups. The use of the principal component analysis allows a preliminary visual separation of CS and SCI, even if the difficulty in distinguishing MCI subjects persists.

Estimation of Respiratory Mechanics Parameters during HFPV

The respiratory mechanics parameters (resistance and elastance) are important for adequate mechanical ventilation settings and for a correct clinical diagnosis. High-frequency percussive ventilation (HFPV) is a non-conventional ventilatory strategy characterized by inspiratory high frequency pulsatile flow, while the expiratory phase is passive. It associates the beneficial aspects of conventional mechanical ventilation with those of high-frequency ventilation.

Impact of Device Settings and Spontaneous Breathing during IPV in CF Patients

Intrapulmonary Percussive Ventilation (IPV) is a ventilation technique that has been introduced for promoting airway clearance and for recruiting areas of lung in patients with restrictive and obstructive pulmonary diseases.