1,720,955 research outputs found
Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks
Get PDFIdentification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography
Get PDFEvidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation
Multimodal sleep analysis: deep learning for wearables and cardiovascular dynamics of auditory-evoked NREM patterns
No full textDOTTORATONegli ultimi anni, il rapido progresso dei dispositivi indossabili e della tecnologia computazionale ha aperto nuove prospettive per la comprensione dei processi fisiologici durante il sonno. Questo lavoro si muove lungo due direzioni parallele e complementari: da un lato la progettazione di strumenti basati su deep learning per l’elaborazione multimodale dei segnali del sonno; dall’altro l’indagine delle dinamiche cardiovascolari nel sonno NREM, con particolare attenzione al ruolo della stimolazione acustica nella modulazione delle slow waves.
La prima linea di ricerca ha portato allo sviluppo di modelli capaci di analizzare in modo automatico i dati raccolti, in ambiente domestico, da un dispositivo toracico indossabile. Le architetture di deep learning proposte assolvono simultaneamente più compiti: valutazione della qualità del segnale, rilevazione di eventi respiratori e correlati al sonno, oltre alla stima preliminare della pressione arteriosa. I risultati dimostrano la fattibilità di un monitoraggio del sonno accurato, interpretabile e scalabile anche al di fuori del contesto clinico.
Il secondo filone di ricerca ha portato alla definizione di un modello analitico per quantificare il legame tra slow waves e risposte cardiovascolari durante il sonno. Grazie a registrazioni ad alta risoluzione di EEG, ECG e pressione arteriosa, ottenute in condizioni controllate con stimolazione acustica a ciclo chiuso, le slow waves sono state classificate in base al grado di sincronizzazione e analizzate rispetto alle risposte autonomiche evento-correlate. È emerso che le onde più sincronizzate, soprattutto se potenziate acusticamente, producono variazioni più marcate della frequenza cardiaca e della pressione sanguigna — segnali associati a un miglioramento della funzione cardiaca osservato al risveglio. L’approccio evento-centrico adottato ha permesso di scomporre le dinamiche di interazione cervello-cuore durante il sonno, offrendo un quadro quantitativo dell’impatto di specifici pattern elettroencefalografici sulla regolazione cardiovascular.
Nel loro insieme, questi contributi delineano un percorso che va dall’innovazione tecnologica alla scoperta fisiologica, rafforzando gli strumenti per studiare il sonno come interfaccia dinamica tra sistema nervoso e apparato cardiovascolare. Integrando sviluppo algoritmico e comprensione biologica, la tesi si colloca nel più ampio contesto della ricerca traslazionale, contribuendo a chiarire come la stimolazione acustica e i diversi fenotipi delle slow waves influenzino l’equilibrio autonomico e la funzione cardiaca.Recent progress in wearable technologies and computational modeling has created new pathways for deriving meaningful physiological insights from sleep. This research follows two interconnected avenues: developing deep learning methods for analyzing multimodal sleep signals, and examining cardiovascular dynamics during NREM sleep influenced by auditory stimulation.
The wearable-focused investigations aimed to extract sleep-related information using a chest-mounted device in real-world, home settings. Custom deep learning models were crafted for multitask classification, addressing signal quality evaluation, detection of respiratory and sleep-associated events, and early-stage, cuffless blood pressure estimation. These solutions showcased promising capabilities for delivering precise, explainable, and scalable sleep monitoring beyond traditional clinical environments.
The second part of the work established an analytical pipeline to quantify how discrete slow wave events interact with cardiovascular dynamics during sleep. Using high-resolution EEG, ECG, and blood pressure signals collected under closed-loop auditory stimulation, slow waves were classified by synchronization type and analyzed in relation to wave-locked autonomic responses. This approach revealed that highly synchronized slow waves, when enhanced through auditory stimulation, elicited stronger heart rate and blood pressure modulations — physiological signatures that correlated with the next-morning cardiac performance. These results highlight how event-based analysis of multimodal sleep signals can disentangle the dynamics of neural-cardiovascular coupling, offering a quantitative framework to study how specific sleep patterns influence cardiovascular physiology.
Taken together, these contributions advance the methodological and conceptual tools needed to investigate sleep as a dynamic interface between neural and cardiovascular systems. By bridging algorithmic development with physiological insight, this work contributes to a growing effort to characterize and contextualize sleep as a window into cardiovascular function — both through scalable monitoring tools and deeper understanding of how auditory stimulation and slow wave dynamics shape autonomic regulation.DIPARTIMENTO DI ELETTRONICA, INFORMAZIONE E BIOINGEGNERIA37ALIVERTI, ANDREADELLACA', RAFFAEL
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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