Application of linear and nonlinear methods for processing HRV and EEG signals

2013/2014L'elaborazione dei segnali biomedici è fondamentale per l'interpretazione oggettiva dei sistemi fisiologici, infatti, permette di estrarre e quantificare le informazioni contenute nei segnali che sono generati dai sistemi oggetto di studio. Per analizzare i segnali biomedici, sono stati introdotti un gran numero di algoritmi inizialmente nati in ambiti di ricerca differenti. Negli ultimi decenni, il classico approccio lineare, basato principalmente sull'analisi spettrale, è stato affiancato con successo da metodi e tecniche derivanti dalla teoria della dinamica nonlineare e, in particolare, da quella del caos deterministico. L'obiettivo di questa tesi è quello di valutare i risultati dell'applicazione di diversi metodi di elaborazione, lineari e non lineari, a specifici studi clinici basati sul segnale di variabilità cardiaca (Heart Rate Variability, HRV) e sul segnale elettroencefalografico (EEG). Questi segnali, infatti, mostrano comportamenti attribuibili a sistemi la cui natura può essere alternativamente di tipo lineare o non, a seconda delle condizioni nelle quali i sistemi vengono analizzati. Nella prima parte della tesi, sono presentati i due segnali oggetto di studio (HRV ed EEG) e le tecniche di analisi utilizzate. Nel capitolo 1 vengono descritti il significato fisiologico, i requisiti necessari per l'acquisizione dei dati e i metodi di pre-elaborazione dei segnali. Nel capitolo 2 sono presentati i metodi e gli algoritmi utilizzati in questa tesi per la caratterizzazione delle diverse condizioni sperimentali in cui HRV e EEG sono stati studiati, prestando particolare attenzione alle tecniche di analisi non lineare. Nei capitoli seguenti (capitoli 3-7), sono presentate le cinque applicazioni dell'analisi dei segnali HRV ed EEG esaminate durante il dottorato. Più precisamente, le prime tre riguardano la variabilità cardiaca, le altre due il segnale EEG. Per quanto riguarda il segnale HRV, il primo studio analizza le variazioni delle proprietà spettrali e frattali in soggetti sani di diversa età; il secondo è focalizzatosull'importanza dell'approccio nonlineare nell'analisi del segnale HRV ricavato da registrazioni polisonnografiche di pazienti affetti da gravi apnee notturne; il terzo presenta le differenze nelle caratteristiche spettrali e nonlineari della variabilità cardiaca in pazienti con scompenso cardiaco determinato da diverse eziologie. Invece, per il segnale EEG, il primo studio analizza le alterazioni negli indici spettrali e nonlineari in pazienti con deficit cognitivi soggettivi e lievi, mentre il secondo valuta l'efficacia di un nuovo protocollo per la riabilitazione della malattia di Parkinson, attraverso la quantificazione dei parametri spettrali dell'EEG.XXVII Ciclo198

BCI-Based Neuro-Rehabilitation Treatment for Parkinson’s Disease: cases Report

Parkinson's Disease (PD) is characterized by motor and cognitive decay, coupled to an alteration of brain oscillatory patterns. In this study a novel neuro-rehabilitation tool, based on the application of motor imagery into a Brain Computer Interface system, is presented with some preliminary data. Three patients were evaluated (with motor, neuropsychological and EEG testing) before and after a neuro-rehabilitation protocol made by 15 experimental sessions. Patients showed a decrease of freezing of gait severity, an improvement in alpha and beta EEG bands power, and a better performance on some attention and executive tasks

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\u2019s 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\u2019s fractal dimension and Poincar\ue9 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 pressure drop in pediatric endotracheal tubes during HFPV

4High-frequency percussive ventilation (HFPV) is a ventilation modality which has been proved useful as an alternative to conventional mechanical ventilation. In clinical practice the ventilator measures the pressure that represents the sum of the pressure drop due to the endotracheal tube (ΔPEET) and the pressure dissipated to inflate lung. From the clinical point of view, it is of paramount importance to estimate the real amount of ΔPEET. This study aimed at identifying in vitro the most adequate model and parameters for estimating ΔPEET of pediatric endotracheal tube during HFPV, under different working pressures, percussive frequencies and resistive and elastic lung loads. The results show that it is possible to estimate ΔPEET in pediatric endotracheal tubes by using a simple Blasius’ model, considering the presence of inertance. The Blasius’ model presents the same estimation error as Rohrer’s model and its coefficients result largely independent from ventilator settings and lung loads.nonemixedM. Ajcevic; A. Accardo; E. Fornasa; U. LucangeloAjcevic, Miloš; Accardo, Agostino; Fornasa, Elisa; Lucangelo, Umbert

HRV Spectral and Fractal Analysis in Heart Failure Patients with Different Aetiologies

Heart Rate Variability (HRV) has been widely studied both in healthy subjects and congestive heart failure (CHF) patients. Significant variations in the HRV patterns have been reported in cardiac patients and quantified both in time and spectral domain by various linear and nonlinear parameters which may be useful not only for the characterization of the autonomous nervous system but also for patients risk stratification. Nevertheless, the relationship between HRV measures and CHF aetiologies has not been completelyinvestigated yet. The purpose of this work was to evaluate the spectral and fractal properties of HRV in patients with CHF caused by either dilated cardiomyopathy or ischemic heart disease, and to compare the results with those coming from normal subjects. Results revealed that changes in some of the examined parameters may lead to a possible separation of the CHF aetiologies

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

Comparing accuracy of tomosynthesis plus digital mammography or synthetic 2D mammography in breast cancer screening: baseline results of the MAITA RCT consortium

Aim: The analyses here reported aim to compare the screening performance of digital tomosynthesis (DBT) versus mammography (DM). Methods: MAITA is a consortium of four Italian trials, REtomo, Proteus, Impeto, and MAITA trial. The trials adopted a two-arm randomised design comparing DBT plus DM (REtomo and Proteus) or synthetic-2D (Impeto and MAITA trial) versus DM; multiple vendors were included. Women aged 45 to 69 years were individually randomised to one round of DBT or DM. Findings: From March 2014 to February 2022, 50,856 and 63,295 women were randomised to the DBT and DM arm, respectively. In the DBT arm, 6656 women were screened with DBT plus synthetic-2D. Recall was higher in the DBT arm (5·84% versus 4·96%), with differences between centres. With DBT, 0·8/1000 (95% CI 0·3 to 1·3) more women received surgical treatment for a benign lesion. The detection rate was 51% higher with DBT, ie. 2·6/1000 (95% CI 1·7 to 3·6) more cancers detected, with a similar relative increase for invasive cancers and ductal carcinoma in situ. The results were similar below and over the age of 50, at first and subsequent rounds, and with DBT plus DM and DBT plus synthetic-2D. No learning curve was appreciable. Detection of cancers >= 20 mm, with 2 or more positive lymph nodes, grade III, HER2-positive, or triple-negative was similar in the two arms. Interpretation: Results from MAITA confirm that DBT is superior to DM for the detection of cancers, with a possible increase in recall rate. DBT performance in screening should be assessed locally while waiting for long-term follow-up results on the impact of advanced cancer incidence