1,720,992 research outputs found
Advanced MI-BCI procedures for neurorehabilitation of PD's and post-stroke patients
No full textLa malattia di Parkinson (PD) è la seconda malattia neurodegenerativa più comune dopo la malattia di Alzheimer. Allo stesso tempo, l'ictus cerebrale è una delle principali cause di disabilità e morte nel mondo. La mattina di Parkinson e l'ictus cerebrale ischemico sono patologie neurologiche che provocano alterazioni del segnale elettroencefalografico (EEG). Tuttavia, ci sono pochi studi che mettono in relazione le alterazioni EEG con il deficit neurologico per un migliore monitoraggio della progressione della malattia, per la neuroriabilitazione personalizzata e per la previsione dell’outcome clinico. I protocolli terapeutici avanzati per il miglioramento delle prestazioni motorie, compresi quelli basati sulla Brain Computer Interface (BCI), possono beneficiare dell’identificazione delle alterazioni EEG e del legame tra queste e il deficit specifico. Le tecniche BCI si sono dimostrate promettenti nella neuroriabilitazione motoria e cognitiva nei pazienti parkinsoniani e post-ictus specialmente attraverso l’utilizzo dell’Immaginazione Motoria (Motor Imagery, MI) supportata dalla BCI (MI-BCI) in grado di creare un ambiente di riabilitazione più controllato venendo fornito al paziente un feedback sulla corretta esecuzione del task riabilitativo. Per migliorare l’applicabilità, la prestazione e l’efficacia di questa strategia riabilitativa, le nuove tecniche di elaborazione del segnale EEG devono essere studiate e sviluppate. Inoltre, le alterazioni dei parametri EEG nei pazienti PD e ictus devono essere considerate nella progettazione di un sistema BCI personalizzato e robusto.
Durante il corso di dottorato, sono stati condotti una serie di studi per identificare le correlazioni tra alterazioni EEG e il deficit neurofisiologico relativo all’ ictus e alla malattia di Parkinson. Inoltre, sono stati condotti degli studi per identificare le tecniche di elaborazione del segnale, di apprendimento automatico e di classificazione più appropriate per la modellazione BCI in queste popolazioni di pazienti.
I risultati ottenuti ed in particolare la correlazione significativa tra i parametri spettrali dell’EEG e le scale cliniche di interesse hanno confermato l’ipotesi che i parametri EEG sono sensibili ai cambiamenti delle funzioni cerebrali nella prima fase dell'ischemia, che possono quindi essere utilizzati sia nella valutazione della gravità dell'ictus sia come strumento di monitoraggio e mappatura dei cambiamenti longitudinali nel paziente con ictus. Inoltre, le correlazioni identificate tra il parametri spettrali dell’EEG e il deficit motorio nei parkinsoniani indicano che la valutazione EEG può essere un biomarcatore utile per il monitoraggio obiettivo della progressione della patologia e dell'efficacia delle diverse strategie riabilitative.
Nella seconda parte della tesi, viene descritto uno studio che ha evidenziato la modulazione corticale indotta dalla MI sull’EEG durante il resting-state, supportando l’ipotesi dell’efficacia della MI-BCI come strategia neuroriabilitativa.
Nella terza parte sono riportati i risultati degli studi condotti su pazienti PD e su quelli con ictus che hanno dimostrato che entrambe le popolazioni, caratterizzate di deficit motorio, erano in grado di controllare la MI-BCI con elevata precisione. Inoltre, viene dimostrato che la migliore performance in termini di accuratezza di classificazione è stata ottenuta con la tecnica di preprocessing Filter Bank Common Spatial Patterns (FBCSP). Nel lavoro di tesi si propone anche una estensione dell'approccio FBCSP basato sul multi-session transfer learning.
I risultati di questa tesi possono contribuire al miglioramento in termini di accuratezza di classificazione e di usabilità degli attuali sistemi BCI, aumentando la diffusione e gli aspetti benefici della neuroriabilitazione MI-BCI nei pazienti con PD e ictus.Parkinson's disease (PD) is the second most common and chronic neurodegenerative disorder after Alzheimer's Disease. At the same time, stroke is one of the leading causes of disability and death. Both PD and stroke are neurological diseases and are almost always coupled with EEG alterations. However, the studies that correlate the EEG features with the clinical scales and treatment outcomes are rare. As a prerequisite for the efficient diagnosis, disease progression monitoring, neurorehabilitation, and outcome prediction, it is required to study the alterations of stroke and PD subjects' cerebral rhythms. Series of novel therapeutic protocols for motor performance improvement, including those based on BCI, can benefit from these findings. BCIs have shown a promising result for motor and cognitive neurorehabilitation for PD and stroke patients. In conjunction with Motor Imagery, BCI can guide a patient to a functional recovery by real-time acquisition, processing, and feeding back information on his task engagement. The MI-BCI application creates a more controlled rehabilitation environment since the MI-induced oscillatory activity can be monitored to assess whether the patient performs the task correctly. For MI-BCI to reach the point where it is consistently successful as a neurorehabilitation tool, the new EEG signal processing techniques have to be studied and developed. Moreover, the alteration of EEG rhythms in PDs and stroke has to be considered to design a personalised and robust BCI system.
During the PhD course, a series of studies have been conducted to study EEG alterations and neurophysiological deficits of stroke and PD's, signal processing, machine learning, and classification techniques needed for BCI modelling.
Our results on EEG spectral features and clinical data show that the EEG confirmed to be a sensitive measure for brain functions in the earliest phase of cerebral ischemia and that EEG can be used as complementary in the evaluation of stroke severity and as a potentially useful tool in monitoring and mapping longitudinal changes in acute stroke patients. Furthermore, the significant correlation between EEG spectral features and symptom-specific motor decline indicates that EEG assessment may be a useful biomarker for objective monitoring of disease progression and as evaluation measure of the effect of PD's rehabilitation approaches.
In the second part of the thesis, our study demonstrates the effect of cortical modulation induced by MI on the EEG resting-state and provides support for further development of Motor Imagery based BCI (MI-BCI).
The third part shows that PD's and stroke patients could control MI-BCI, with high accuracy and that FBCSP may be used as the MI-BCI approach for complementary neurorehabilitation. The thesis's additional novelty is the proposal of the transfer learning-based multi-session extension FBCSP approach. The new approach has been tested, and the study shows that significantly improves BCI model calibration accuracy in PD patients. Finally, our last study results showed that the signal nonstationarities and power covariance shifts significantly reduce BCI models' accuracy. However, only after introducing the Stationary Subspace Analysis (SSA) preprocessing the classifier's performance is significantly increased.
The abovementioned main findings of this thesis may improve the present BCI systems in terms of accuracy and usability and enhance the diffusion and beneficial aspects of MI-BCI neurorehabilitation to PD and stroke patients
Characterization of Parkinson's Disease using spectral features of kinetic tremor: correlation of on-line digitized handwriting and classical motor scales
No full textTremor is one of the motor impairments of Parkinson’s disease (PD). To date, kinetic tremor in PD is barely examined and there is lack of information on the relation between its severity and scales related to specific motor deficits [1]. In this study, we aimed at investigating the correlation between handwriting-related kinetic tremor and motor score measures by motor part of Unified Parkinson's Disease Rating Scale (UPDRS-III) [2], using a digitizing tablet.
In this preliminary study, eight PD patients (7 M/1 F; age 74.5±8 years) draw an accurate Archimedes’ Spiral (AS) and fast, overlapped Circles (C) for a duration of 15 seconds. All patients underwent motor deficit assessment using UPDRS-III. Power Spectral Density of both velocity and acceleration profiles in their horizontal, vertical, and curvilinear components was estimated by using Welch’s method, with a Hamming window on intervals of 5 s and a 50% overlap. To analyze the power distribution related to different movement-associated phenomena, the ratio between two frequency bands (BME/BT) and the BT bandwidth (BW) were calculated for each subject. BME is the band of voluntary Movement Execution required by the task, ranging from 0.2 to 4 Hz, and BT is the band associated with involuntary Tremor, ranging from 4.0 to 12 Hz [3]. Normalized Jerk, a classic kinematic feature representing handwriting fluidity, was also estimated. The correlation between the parameters and UPDRS-III scores were assessed using Spearman’s rank correlation coefficients. All the evaluations were conducted in the pharmacological on state of PD patients.
A positive correlation was found between Jerk and UPDRS-III scores (Table 1). On the contrary BME/BT correlates negatively with motor scale scores in horizontal and vertical velocities (Vx, Vy) for both tasks, vertical and curvilinear acceleration (Ay, Ac) for AS task, horizontal and vertical acceleration (Ax, Ay) for C task. Only Ay of C task shows correlation with motor scores.
The results highlight that the severity of motor deficits in PD patients, as assessed by a widely employed motor scale, correlates with the outcomes of spectral and kinematic analysis of handwriting that indicate a loss of fluency, an increased power at BT level and a thinning of the spectral peak of BT. This suggests that handwriting assessment of parkinsonian dysgraphia can be used to implement clinical evaluation and represents a non-invasive, low-cost method for the identification of objective and reproducible biomarkers of kinetic tremor
Kinematic Characterization of Movements During the Tinetti Test
No full textThe improvement in life expectancy has led to a corresponding increase in people suffering from chronic illnesses as well as in subjects at high risk of falling. Various scales exist in literature to evaluate fall risk in ambulatory settings among which the Tinetti Test is the most used. However, only trained healthcare professionals can conduct this test. In order to make this scale available to a growing number of older people outside hospital and to reduce the high inter operator bias in scoring the exercises, a less provider dependent system is necessary. In this preliminary study, which can be used as a benchmark for future evaluation of individuals at risk of falling, some parameters were extracted from a wireless 3D magnetic inertial sensor applied on the chest of 30 young healthy participants. Each subject performed four exercises from the Tinetti balance test: arising from a chair (1), standing balance with open (2), and closed eyes (3) and sitting down (4). For exercises (1) and (4), the duration of movement and the maximum angular amplitude were calculated, while for exercises (2) and (3) the fractal dimension and the spectral power were evaluated. The obtained values, directly correlated with the exercises, showed a low variability among subjects, resulting as potential candidates for the characterization of the movement during the Tinetti test, enabling non-expert operators to assess the falling risk
Editorial: Brain-connectivity-based computer interfaces
Full text linkEditorial on the Research Topic Brain-connectivity-based computer interface
Does Ectopic Beats Bring More Discriminatory Information to Diagnose Ischemic Heart Disease?
Full text linkEarly non-invasive diagnosis of Ischemic Heart Disease (IHD) can often be challenging. HRV features have a potentially important role in risk stratification for subjects with suspected heart disease. However , there is no consensus on the HRV preprocessing steps, particularly on how to properly treat ectopic beats.We aimed to investigate the performance of the models for classification of early IHD versus healthy subjects (HC) based on HRV features extracted from signals excluding ectopic beats and based on the same features extracted from the signals that contain both ectopic and normal heartbeats. This study encompassed 385 subjects (170 IHD and 215 HC). The models were produced by logistic regression method considering two sets of HRV features obtained by two preprocessing approaches. The results showed that the model with the input features from HRV signals including normal and ectopic beats presented a higher classification accuracy (72.7%) than the model based on features extracted only from normal heart beats (67.8%). In addition, the evaluation of the feature importance by analysis of produced nomograms and observed significant differences between features extracted with two preprocessing approaches, showed also that the exclusion of the ectopic beats modifies the features' discriminatory power between HC and IHD
Combined and Singular Effects of Action Observation and Motor Imagery Paradigms on Resting-State Sensorimotor Rhythms
Full text linkIn the present study, 30 right-handed participants randomly performed one of three motor neurorehabilitation paradigms: action observation (AO), motor imagery (MI) and combined action observation and motor imagery (AO+MI) of the right arm and hand movement. Resting state electroencephalography (EEG) was acquired for 5 min before and immediately after the motor paradigms session. EEG was recorded from 10 sites over sensorimotor areas, and the average power was calculated for left (FC3, C3, C1, C5, CP3) and right (FC4, C4, C2, C6, CP4) regions in the spectral bands: delta, theta, alpha, mu, low and high beta. Our main finding demonstrates that delta, theta and mu activity decreased significantly on the contralateral regions during MI, while low beta increased significantly. Except for the mu band, the same changes were observed on the ipsilateral side, where delta and theta decreased significantly, while low beta became significantly higher. No relevant effects were observed for AO or combined AO and MI. These findings demonstrate a rapid effect of MI on cortical modulation in sensorimotor areas which is revealed by changes in resting state oscillatory activity and suggest an interesting interplay between MI and AO. The presented findings may be relevant for choosing a proper protocol for clinical motor neurorehabilitation approaches
Detecting Heart Failure Relations: A Preliminary Study Integrating HRV, LVEF, and GLS in Patients with Ischemic Heart Disease and Dilated Cardiomyopathy
No full textCardiovascular diseases, such as Ischemic Heart Disease (IHD) and Dilated Cardiomyopathy (DCM), collectively represent the leading cause of mortality worldwide. In both pathological conditions, patients displaying heart failure symptoms emphasize the critical need for early detection, facilitating timely and appropriate care, enhancing patient outcomes, and optimizing healthcare resources. Heart rate variability (HRV), Left ventricular ejection fraction (LVEF) and Global longitudinal strain (GLS) are prominent parameters that could allow the identification of heart failure event. Therefore, the aim of our study was to develop an interpretable model that identify the relation between the occurrence of heart failure and HRV features, as well as LVEF, GLS, sex and age in patients with IHD and DCM. The study encompassed two groups: 126 patients with heart failure (HF group) and 126 patients without it (noHF group). GLS, LVEF, and linear and non-linear HRV features were extracted for each subject. Then, the interpretable model was produced by a logistic regression algorithm considering a set of features chosen with the univariate logistic regression method. The univariate logistic regression results indicate a significative correlation between the occurrence of heart failure events and the following parameters: LVEF, age, expBeta, HFn, and LF/HF. The obtained classification accuracy of produced model was 73% and the area under the ROC curve was 0.77. These preliminary findings showed that the identified parameters may be useful for stratification of IHD and DCM subjects with a risk of a heart failure event
Interpretable Model to Support Differential Diagnosis Between Ischemic Heart Disease, Dilated Cardiomyopathy and Healthy Subjects
No full textThe differential diagnosis between Ischemic Heart Disease (IHD) and Dilated Cardiomyopathy (DCM) can often be challenging, because only invasive, and not largely available exams can provide a definite diagnosis. The echocardiographic left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) as well as ECGheart rate variability (HRV) analysis are shown to be helpful tools for diagnosing several cardiac diseases. There is also a growing interest in application of interpretable machine learning techniques to guide the diagnosis.
We aimed to produce an interpretable model applied for differential diagnosis between DCM, IHD and healthy subjects (HC) based on LVEF, GLS and HRV features. The study encompassed three groups: 130 DCM, 164 IHD, and 152 HC subjects. The novel GLS, LVEF, and linear and non-linear HRV features were extracted for each subject. Then, the interpretable models were produced by a logistic regression algorithm considering a set of features chosen with the ReliefF method. The results showed that the most informative features for classification between IHD, DCM e HC were: GLS, LVEF, age, FD, SD1/SD2 and sex, listed in order of importance. The obtained classification accuracy was 70% and the area under the ROC curvewas 83.4%. The study demonstrates that a logistic regression model and its nomograms allow detailed clinical interpretation of the model and may be a powerful tools support differential diagnosis between IHD, DCM and HC
Relationship Between Personality and Kinematic Parameters of Handwriting
Full text linkMotor and cognitive systems are largely involved in producing handwriting that develops with age becoming more and more personalized until it reaches a style proper to the subject. This fact has led graphologists to assert that by examining the handwriting it is possible to somehow trace the personality of the writer. Many studies have been carried out to demonstrate this assumption but they are all based on the graphic examination of the tract left on the sheet of paper. On the other hand, recently it has been possible to examine writing through the use of digital tablets capable of providing information also on the kinematic of the movement, extracting parameters used to examine in particular dysgraphia and some neurological pathologies. Aim of this study was to determine possible relationships between kinematic parameters extracted using digital tablets and personality
traits. Sixty-one subjects took part in the study, executing three writing tasks (fast and accurate writing of an Italian phrase and fast sequence of cursive lowercase letters “lelele” without pen lifting for 30 s) and a personality test (IPIP-NEO-120). The linear regression between each of fourteen characteristic of handwriting and each of the five personality traits was computed. The results showed that four out of five main psychological tracts presented a linear relation with one or more kinematic characteristics. This study offers a first glance at a complex series of correlations, which will be investigated in future researches
Neurofeedback induced restoration on sensorimotor rhythm after 24h of hand immobilization.
Full text linkIn this study, we examined the effect of neurofeedback on EEG changes due to immobilization of the dominant hand. Desynchronization of the sensorimotor rhythms during motor imagery was used as a tool to investigate brain activity. The study is based on 8 healthy subjects who underwent immobilization of the dominant hand for 24 hours. The electrical activity of the sensorimotor region of the cerebral cortex was registered during mental imagery of hand movements before the immobilization, soon after its removal and after a single session of neurofeedback. The control of the feedback stimuli was based on changes in sensorimotor rhythms produced by imagination of movement. Preliminary results show that immobilization caused changes in alpha and beta rhythms that were rapidly reversed after a single session of neurofeedback. At the end of the full study, if the here presented observations will still hold, the neurofeedback protocol will be proposed for routine rehabilitation sessions in patients suffering partial or total limb disability
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