1,721,033 research outputs found
Analisi e modelli di segnali biomedici
No full textQuesto libro è il risultato dell’esperienza didattica maturata nei corsi di ‘Analisi e modelli di segnali biomedici’ e di ‘Metodi per l’analisi di segnali multidimensionali’ tenuti rispettivamente al 1° e al 2° anno della Laurea Magistrale in Ingegneria Biomedica a Pisa. Il libro ha come principale obiettivo quello di fornire agli studenti gli strumenti teorici e metodologici necessari per affrontare un’ ampia gamma di problematiche di analisi di dati, di segnali e di immagini biomediche. Il numero elevato di esercitazioni permette allo studente di trasferire nel contesto reale la quasi totalità degli argomenti teorici trattati, fornendo un’ampia panoramica di soluzioni a problemi tipici dell’ingegneria biomedica. Le esercitazioni comprendono sia metodi di simulazione numerica, sia algoritmi di analisi applicati alle misure biomediche. Un pregio del libro è aver raccolto in un unico volume tematiche che classicamente appartengono a ambiti culturali diversi e spesso distribuite su più volumi.
Gli argomenti trattati nel libro sono stati scelti per la loro attualità e per le importanti ricadute che rivestono nel campo biomedico. Essi sono anche il risultato dell’esperienza che gli autori hanno acquisito grazie alla convenzione tra l’Università di Pisa e la Fondazione Toscana Gabriele Monasterio, azienda di ricerca sanitaria, e mediante contatti e collaborazioni con le maggiori industrie nazionali e internazionali di imaging biomedico.
Il libro è sviluppato in otto capitoli. Partendo dalla definizione e sintesi dei processi stocastici, vengono trattati i principali metodi di stima di parametri derivati dall’analisi statistica del I e del II ordine, con esempi su serie temporali e immagini biomediche. Uno spazio significativo è dedicato all’analisi multiscala, con particolare riferimento all’analisi wavelet e ai metodi di denoising lineari e non. Segue un capitolo che tratta la deconvoluzione con e senza regolarizzazione, applicata alla soluzione di problemi inversi mal posti, con esempi nel settore dell’elaborazione di segnali e immagini. Un breve spazio è dedicato al filtraggio adattivo per la cancellazione di artefatti da segnali biomedici. Una parte importante è rivolta all’analisi statistica multivariata, comprendente l’analisi delle componenti principali e delle componenti indipendenti e la regressione lineare singola e multipla. Conclude il libro una raccolta di algoritmi supervisionati, parametrici e non, utilizzati per scopi di classificazione
Principal component analysis of psychophysiological data in a clinical population: a pilot study
No full textAnalisi delle componenti principali applicata a dati psicofisiologici di una popolazione clinica: uno studio pilota
No full textGotta trace ‘em all: A mini-review on tools and procedures for segmenting single neurons toward deciphering the structural connectome
Full text linkDecoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome
A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis
Full text linkBackground and Objective: The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified. Methods: We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels’ generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers. Results: According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works. Conclusions: The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis
A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets
Full text linkAccurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm’s outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm’s full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools
Reliability of Pulse Rate Variability in Elderly Men and Women: an Application of Cross-Mapping Approach
Full text linkPhotoplethysmography (PPG) is a completely noninvasive, optical method of assessing blood flow dynamics in peripheral vasculature. Wearable devices for PPG recording are becoming increasingly popular, due to their cost-effectiveness and ease of use. For these reasons, many recent scientific studies have proposed the use of pulse rate variability (PRV) extracted from PPG as a surrogate for heart rate variability (HRV), in monitoring autonomic activity and cardiovascular health.In this work, we used a cross-mapping approach, a methodology based on chaos theory, to compare PRV and HRV dynamics, and investigate their agreement according to age and gender of healthy subjects. We used ECG and PPG data acquired from 57 subjects (41 young and 16 elderly) during resting state in the supine position. Signals were gathered from the publicly available VORTAL dataset. Our results showed a statistically significant decrease of PRV reliability as an HRV surrogate in old participants, which was confirmed as significant when only men subjects were analyzed (p-value<0.01).Our findings, although preliminary, suggest greater caution in the use of PPG devices for monitoring cardiovascular health, especially in elderly men
Analysis of speech features and personality traits
Full text linkVoice signal has been widely investigated to characterize mood and emotional states. A further interesting dimension could regard the personality traits. The relationship between personality traits and specific speech features is known, however this topic requires further investigation. Specifically, most studies are focused on perceived personality traits, without adopting dedicated personality tests. Moreover, the relationship among speaker personality traits and specific speech features have still to be clarified. In this study, a correlational analysis between some speech-related features and the personality traits, as described by the Zuckerman-Kuhlman model and the Toronto Alexithymia Scale, is performed. An experimental protocol, consisting of two structured speech tasks, was administered to eighteen healthy subjects. Speech features were estimated to describe fundamental frequency (F 0 ) and voice quality related features from whole speech recording and tilt-related features, describing F 0 dynamics at voiced segment level. Significant correlations among personality traits and speech features were observed using both feature sets. Interestingly, the adopted speech task was found to influence the obtained results. Specifically, no feature reports the same significant correlation in both adopted tasks. The impact of personality traits and speech production studies on the characterization of mental disorders and the estimation of emotional/mood state of the speaker are discussed
Speech signal analysis as an aid to clinical diagnosis and assessment of mental health disorders
Full text linkObjective: In this study, we estimate speech features from different Verbal Fluency Tests (VFT) conditions to distinguish comorbid Bipolar Disorder (BD) in adults suffering from Attention Deficit Hyperactivity Disorder (ADHD) and to identify ADHD subtypes such as the inattentive (ADHD-I) from the combined one (ADHD-C). Methods: Prosodic and spectral features in five conditions of VFTs were extracted and selected for the classification performed with machine learning methods. Specifically, a Support Vector Machine exploiting Recursive Features Elimination (SVM-RFE) has been trained with clinical scores and exploiting the within subject variability of speech features across VFT conditions. The final classification was optimized by combining the marginal classification outcomes obtained from the different VFTs using a voting scheme. Results: Our results show that we successfully classify the ADHD+BD comorbidity and the ADHD subtypes according to clinician diagnosis. The results are discussed in the light of possible benefits of developing such approach within clinical research. Conclusion: Significant information is carried out by speech audio features acquired with VFTs, allowing to classify ADHD subtypes and comorbid patterns. This work clearly shows that the audio analysis of speech, along with properly designed speech tasks, is a candidate for the development of clinical decision support systems in psychiatry. Significance: This work represents a major contribution to the applications of speech analysis in ADHD subjects and could support clinicians by identifying objective biomarkers
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