1,720,963 research outputs found
Preliminary automatic analysis of photoplethysmographic and respiratory signals for sleep-wake pattern identification in young infants at risk of SIDS
No full textSudden Infant Death Syndrome (SIDS) is the leading cause of mortality in the first year of life in industrialized countries and occurs predominantly in young infants (1-6 months) during sleep. 24-hour cardiorespiratory monitoring is a valid diagnostic tool as it continuously records the infant's vital parameters in the various phases of the day. This exam included 24-hour direct patient observation by a caregiver, who records the infant's sleep-wake pattern in a diary. These data provide essential insights into the infant's overall health and neurological development. This study presents a preliminary automated analysis of photoplethysmographic (PPG) and respiratory (RESP) signals acquired through the cardiorespiratory monitor to identify sleep-wake patterns in young infants at risk of SIDS. In particular, a threshold-based algorithm for classifying 30-seconds epochs of PPG and RESP signal into sleep or wake states, validated against 24h diary, was developed. Consequently, the proposed approach eliminates the need for 24-hour continuous, direct patient observation and significantly reduces the time for data analysis by the sleep medicine physician. The developed PPG-RESP-based algorithm demonstrated an overall sensitivity of 83%, a specificity of 79%, and an accuracy of 81% with respect to direct patient observation, with lower performances observed in pathological conditions. Furthermore, the measurement of essential parameters related to sleep quality and cardiorespiratory activity during sleep, including total sleep time (TST), sleep efficiency (SE%), mean heart rate (HR), mean breathing rate (BR), and mean SpO2 levels, showed strong concordance with the assessments manually obtained by the physicians. These findings suggest that the herein-developed method is a valuable preliminary tool to define sleep-wake pattern and sleep quality, thus enhancing the effectiveness of cardiorespiratory monitoring in the diagnostic approach of young infants at risk of SIDS
Real-time sleep prediction using a virtual sensor to estimate Heart Rate Variability (HRV) through Respiratory Rate (RR)
Full text linkOne of the most important causes of death while driving is sleepiness. To solve this problem, different kinds of technologies are needed. A recent work presented an approach based on Photoplethysmogram (PPG) analysis to predict the sleep onset. As PPG is not always available, especially in the case of commercial of the shelf wearable devices that provide features such as heart beat and respiration rate, in the paper we present a novel approach to predict sleep onset, which leverages a virtual sensor able to provide an estimation of the PPG-related Heart Rate Variability (HRV) through Respiration Rate (RR) analysis. The experimental results show 100% sensitivity and specificity in the collected data
To be or not to be... awake? A comparison of subjective and objective methods for drowsiness detection in drivers
No full textDrowsiness or fatigue poses a significant risk for road accidents and carries important consequences for overall road safety. Timely warnings to drowsy drivers can be crucial to prevent potentially fatal accidents. By now, the techniques employed for assessing drowsiness can be subjective, such as questionnaires, or objective, such as the analysis of the behavioral conditions of the driver or the vehicle and the physiological parameters of the driver. As a matter of fact, the most robust method for detecting drowsiness is an objective one, in particular the analysis of physiological parameters through full polysomnography. However, the best practice for road safety still utilizes subjective parameters to establish the drowsiness of the driver. A major question still exists whether subjective sleepiness can predict actual fall-asleep events while driving (e.g., micro-sleep events). In this study, a comprehensive comparison between objective and subjective methods for drowsiness assessment is provided by the use of medical-grade devices and a high-fidelity driver simulator design. As a result, the unpredictability and unreliability of subjective methods for drowsiness assessment were demonstrated
On-the-Fly Sleep Scoring Algorithm with Heart Rate, RR Intervals and Accelerometer as Input
Full text linkIn many applications, recognizing the depth of sleep (e.g., light, deep, REM sleep) while the subject is sleeping enables innovative features. For instance, in SAE Level 4 autonomous driving, a driver may need to takeover the vehicle control in case the autopilot is exiting its operational design domain. Depending on the depth of the sleep, the subject may need time to takeover effectively; hence, it is particularly relevant to know in which sleep stage the subject is (e.g., light sleep, deep sleep, and REM sleep), and possibly initiate actions to prevent the subject to remain in those sleep stages that lead to longer takeover time. Sleep stage classification can be achieved through an on-the-fly algorithm, which generates output in response to each input portion without knowledge of future inputs, unlike an off-Line algorithm that provides output just after receiving the entire input sequence. Various studies have analyzed algorithms or devices that identify sleep stages during the night; however, these typically require electroencephalography (EEG), which is obtrusive, or specialized devices. This study describes the development of an on-the-fly sleep-scoring algorithm using Heart Rate (HR), RR intervals, which is the distance between two consecutive heartbeats, and accelerometer data from a smartwatch, widespread, non-invasive, and affordable but accurate device. The subjects involved in our study wore a commercial off-the-shelf wearable device during a full night’s sleep, and were also monitored using a reference medical device to establish the ground truth by means of a full polysomnography (PSG) analysis. The on-the-fly sleep scoring algorithm based on smartwatch data was tested against PSG-based scoring, achieving 88.46% accuracy, 91.42% precision, and 93.52% sensitivity in sleep–wake identification. Deep sleep was correctly identified 69.38% of times, light sleep in 50.62%, REM sleep 62.02% and wakefulness 73.48% of times
Automatic Detection and Prediction of the Transition Between the Behavioural States of a Subject Through a Wearable CPS
Full text linkThe PRESLEEP project is aimed at the fine assessment and validation of the proposed proprietary methodology/technology, for the automatic detection and prediction of the transition between the behavioural states of a subject (e.g. wakefulness, drowsiness and sleeping) through a wearable Cyber Physical System (CPS). The Intellectual Property (IP) is based on a combined multi-factor and multi-domain analysis thus being able to extract a robust set of parameters despite of the, generally, low quality of the physiological signals measured through a wearable system applied to the wrist of the subject. An application experiment has been carried out at AVL, based on reduced wakefulness maintenance test procedure, to validate the algorithm’s detection and prediction capability once the subject is driving in the dynamic vehicle simulator
Bio-orthogonal double-crosslinked alginate-gelatin/MXenes hydrogels as biomimetic viscoelastic and electroconductive substrates supporting cardiac regeneration
Full text linkMyocardial infarction is one of the leading causes of death worldwide and represents a major clinical challenge [1]. In recent works, cell culture hydrogel substrates enhanced miRNA-mediated direct reprogramming efficiency of human cardiac fibroblasts (HCFs) into induced cardiomyocyte (iCMs) [2][3]. Notably, these hydrogels were purely elastic or highly soft, whereas several studies highlighted the importance of ECM-like stress-relaxing viscoelastic hydrogels in regulating cell behavior [4]. Furthermore, electroconductive hydrogel substrates enhanced the maturation of cardiac contractile cells [5]. Herein, alginate-gelatin/MXenes hydrogels based on bio-orthogonal click-chemistry, with tunable viscoelastic and electroconductive properties were developed with the aim to support cardiac regeneration by enhancing direct reprogramming of HCFs into iCMs.
Firstly, a solution of alginate-azide and gelatin-azide conjugates, was mixed with a 4-arm-PEG-DBCO solution to form spontaneous hydrogels via bio-orthogonal strain-promoted azide-alkyne click reaction (SPAAC). Double-crosslinked hydrogels were obtained, through additional ionic cross-linking by Ca2+ ions. Furthermore, MXene quantum dots (MQDs) were incorporated into hydrogels, to impart electrical conductivity. Physicochemical properties of hydrogels were deeply investigated. In vitro cell viability and adhesion were performed by embedding HCFs into hydrogels.
Bio-orthogonal alginate-gelatin hydrogels were successfully obtained through SPAAC click reaction. By varying azide:DBCO molar ratio, physicochemical properties of hydrogels were modulated. At increasing azide:DBCO ratio, hydrogels showed increased mechanical stiffness and more pronounced elastic response, mimicking healthy cardiac tissue. All hydrogels showed physiological rates of stress relaxation. Particularly, substrate viscoelasticity could be tuned by double-crosslinking strategy, which enabled viscous stress dissipation by unzipping of ionically packed molecules. MQDs could be finely dispersed within the hydrogel network enhancing substrate electroconductivity. Thanks to SPAAC bio-orthogonality, all hydrogels supported cell viability, adhesion and spreading. The study of in vitro direct reprogramming of HCFs into iCMs in contact with the developed hydrogels is ongoing.
As a conclusion, bio-orthogonal double-crosslinked alginate-gelatin/MXenes hydrogels developed in this study are promising candidates as biomimetic substrates for cardiac direct reprogramming applications, deserving future investigations.
1. Jayawardena, T. M. et al., Circ. Res.110, 1465–1473 (2012).
2. Paoletti, C. et al., Cells 11(5), 800 (2022).
3. Kurotsu, S. et al., Stem Cells Reports 15, 612-628 (2020).
4. Chaudhuri, O. et al., Nat. Mater. 15(3), 326-334 (2016).
5. Roshanbifar, K. et al., Adv. Funct. Mater. 28 (2018).
BIORECAR project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme grant agreement No 7721
Bio-orthogonally double cross-linked alginate-gelatin hydrogels with tunable viscoelasticity for cardiac tissue engineering
Full text linkGrowing evidence has shown that cells respond to the viscoelastic properties of the extracellular matrix (ECM), particularly its stress-relaxation, which influences their spreading, proliferation, and remodeling. Since cardiac tissue viscoelasticity plays a key role in modulating cellular mechanosensing, the development of biomimetic viscoelastic hydrogels is highly needed in cardiac tissue engineering (CTE). This work presents bio-orthogonal double cross-linked alginate-gelatin hydrogels with tunable viscoelasticity, designed to replicate the dynamic mechanical properties of cardiac ECM. Alginate and gelatin were functionalized with azide groups and cross-linked by a 4-arm-dibenzocyclooctyne (DBCO) crosslinker using strain-promoted azide-alkyne cycloaddition (SPAAC) with 0.5:1 (AG_Click(R0.5)) and 1:1 (AG_Click(R1)) DBCO:azide molar ratios. Calcium ions were also introduced to obtain double cross-linked hydrogels (AG_DC(R0.5) and AG_DC(R1)). Rheology showed that hydrogels exhibited tunable stiffness and stress relaxation, closely mimicking the properties of native cardiac tissue. The behavior of human cardiac fibroblasts (HCFs), seeded on hydrogels, was analyzed. When compared to purely elastic polyacrylamide (pAAm) hydrogels with comparable stiffness, soft stress-relaxing hydrogels (AG_Click(R0.5) and AG_DC(R0.5)) were found to promote cell spreading area, while stiffer stress-relaxing hydrogels (AG_Click(R1) and AG_DC(R1)) enhanced asymmetric cell elongation, reflecting substrate-mediated mechanosensing. Additionally, HCFs showed high viability when cultured in 3D hydrogels over 7 days. Overall, rapid gelation, biocompatibility, and tunable viscoelastic properties of bio-orthogonal double cross-linked alginate-gelatin hydrogels support their use as injectable formulations or engineered cardiac tissues for CTE
Contactless System for Sleep Prediction in Drivers
No full textDrowsy driving contributes to 10–30% of all vehicle crashes, making it a major road safety concern. Driver Monitoring Systems (DMS) aim to assess driver alertness and are typically categorized into vehicle-, behavior-, and physiology-based approaches. While physiology-based systems offer the highest accuracy, most of them rely on costly and intrusive contact-based cardiac sensors. This study demonstrates, for the first time, the feasibility of predicting driver sleep events using a fully contactless, physiology-based approach that analyzes breathing patterns in real time. Data were collected using a short-range 60 GHz standalone automotive radar in a driving-seat mockup. Crucially, sleep events were objectively validated for the first time in this context using polysomnography (PSG) data reviewed by a medical expert, following American Academy of Sleep Medicine (AASM) guidelines for the Maintenance of Wakefulness Test (MWT) — marking a departure from previous reliance on subjective behavioral observations. The proposed heuristic algorithm achieved 85% overall accuracy, with 100% (95% CI 29%–100%) specificity and 80% (95% CI 44%–97%) sensitivity. This work presents a validated, non-intrusive solution for sleep event prediction in drivers, underscoring its potential for enhancing road safety through practical, clinically supported DMS technologies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
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