Grupo de Ingeniería Biomédica
Grupo de Ingeniería Biomédica
https://uvadoc.uva.es/handle/10324/23459
2024-03-28T13:32:30Z
2024-03-28T13:32:30Z
Reliability of Machine Learning to Diagnose Pediatric Obstructive Sleep Apnea: Systematic Review and Meta-Analysis
Gutierrez-Tobal, Gonzalo C.
Álvarez, Daniel
Kheirandish Gozal, Leila
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/66130
2024-02-12T20:02:25Z
2022-01-01T00:00:00Z
Background
Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice.
Objective
To assess the reliability of machine-learning-based methods to detect pediatric OSA.
Data Sources
Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references.
Eligibility Criteria
Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard.
Appraisal and Synthesis Methods
Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2) was evaluated, and publication bias was corrected (trim and fill).
Results
Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.
2022-01-01T00:00:00Z
Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults
Gutierrez-Tobal, Gonzalo C.
Álvarez, Daniel
Vaquerizo Villar, Fernando
Crespo, Andrea
Kheirandish Gozal, Leila
Gozal, David
del Campo, Félix
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/66123
2024-02-12T20:02:24Z
2021-01-01T00:00:00Z
Overnight pulse oximetry has shown usefulness to simplify obstructive sleep apnea (OSA) diagnosis when combined with machine-learning approaches. However, the development and evaluation of a single model with ability to reach high diagnostic performance in both community-based non-referral and clinical referral cohorts are still pending. Since ensemble-learning algorithms are known for their generalization ability, we propose a least-squares boosting (LSBoost) model aimed at estimating the apnea–hypopneaindex (AHI), as the correlate clinical measure of disease severity. A thorough characterization of 8,762 nocturnal blood-oxygen saturation signals (SpO 2) obtained at home was conducted to extract the oximetric information subsequently used in the training, validation, and test stages. The estimated AHI derived from our model achieved high diagnostic ability in both referral and non-referral cohorts reaching intra-class correlation coefficients within 0.889–0.924, and Cohen’s
within 0.478–0.663 when considering the four OSA severity categories. These resulted in accuracies ranging 87.2%–96.6%, 81.1%–87.6%, and 91.6%–94.6% when assessing the three typical AHI severity thresholds, 5 events/hour (e/h), 15 e/h, and 30 e/h, respectively. Our model also revealed the importance of the SpO 2 predictors, thereby minimizing the ‘black box’ perception traditionally attributed to the machine-learning approaches. Furthermore, a decision curve analysis emphasized the clinical usefulness of our proposal. Therefore, we conclude that the LSBoost-based model can foster development of clinically applicable and cost saving protocols for detection of patients attending primary care services, or to avoid full polysomnography in specialized sleep facilities, thus demonstrating the diagnostic usefulness of SpO 2 signals obtained at home.
2021-01-01T00:00:00Z
Linear and nonlinear analysis of airflow recordings to help in sleep apnoea–hypopnoea syndrome diagnosis
Gutiérrez Tobal, Gonzalo César
Hornero Sánchez, Roberto
Álvarez, Daniel
Marcos, José Víctor
Campo Matias, Félix del
https://uvadoc.uva.es/handle/10324/65879
2024-02-07T20:02:33Z
2012-01-01T00:00:00Z
This paper focuses on the analysis of single-channel airflow (AF) signal to help in sleep apnoea–hypopnoea syndrome (SAHS) diagnosis. The respiratory rate variability (RRV) series is derived from AF by measuring time between consecutive breathings. A set of statistical, spectral and nonlinear features are extracted from both signals. Then, the forward stepwise logistic regression (FSLR) procedure is used in order to perform feature selection and classification. Three logistic regression (LR) models are obtained by applying FSLR to features from AF, RRV and both signals simultaneously. The diagnostic performance of single features and LR models is assessed and compared in terms of sensitivity, specificity, accuracy and area under the receiver-operating characteristics curve (AROC). The highest accuracy (82.43%) and AROC (0.903) are reached by the LR model derived from the combination of AF and RRV features. This result suggests that AF and RRV provide useful information to detect SAHS.
2012-01-01T00:00:00Z
Unveiling the alterations in the frequency-dependent connectivity structure of MEG signals in mild cognitive impairment and Alzheimer’s disease
Rodríguez González, Víctor
Núñez Novo, Pablo
Gómez Peña, Carlos
Hoshi, Hideyuki
Shigihara, Yoshihito
Hornero Sánchez, Roberto
Poza Crespo, Jesús
https://uvadoc.uva.es/handle/10324/62563
2023-11-02T20:01:11Z
2024-01-01T00:00:00Z
Mild cognitive impairment (MCI) and dementia due to Alzheimer’s disease (AD) are neurological disorders that affect cognition, brain function, and memory. Magnetoencephalography (MEG) is a neuroimaging technique used to study changes in brain oscillations caused by neural pathologies. However, MEG studies often use fixed frequency bands, assuming a common frequency structure and overlooking both subject-specific variations and the potential influence of pathologies on frequency distribution. To address this issue, a novel methodology called Connectivity-based Meta-Bands (CMB) was applied to obtain a subject-specific functional connectivity-based frequency bands segmentation. Resting-state MEG activity was acquired from 161 participants: 67 healthy controls, 44 MCI patients, and 50 AD patients. The CMB algorithm was used to identify “meta-bands” (i.e., recurrent network topologies across frequencies). The meta-bands were used to extract an individualised frequency band segmentation. The network topology of the meta-bands and their sequencing were analysed to identify alterations associated with MCI and AD in the underlying frequency-dependent connectivity structure. We found that MCI and AD alter the neural network topology, leading to connectivity patterns both more widespread in the frequency spectrum and heterogeneous. Furthermore, the meta-band frequency sequencing was modified, with MCI and AD patients exhibiting sequences with increased complexity, suggesting a progressive dilution of the frequency structure. The study highlights the relevance of considering the impact of neural pathologies on the frequency-dependent connectivity structure and the potential bias introduced by using fixed frequency bands in MEG studies.
2024-01-01T00:00:00Z
An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals
Jiménez García, Jorge
García Gadañón, María
Gutiérrez Tobal, Gonzalo César
Kheirandish Gozal, Leila
Vaquerizo Villar, Fernando
Álvarez González, Daniel
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/62524
2023-10-31T20:00:49Z
2024-01-01T00:00:00Z
Deep-learning algorithms have been proposed to analyze overnight airflow (AF) and oximetry (SpO2) signals to simplify the diagnosis of pediatric obstructive sleep apnea (OSA), but current algorithms are hardly interpretable. Explainable artificial intelligence (XAI) algorithms can clarify the models-derived predictions on these signals, enhancing their diagnostic trustworthiness. Here, we assess an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) to detect pediatric OSA and its severity. AF and SpO2 were obtained from the Childhood Adenotonsillectomy Trial (CHAT) public database (n = 1,638) and a proprietary database (n = 974). These signals were arranged in 30-min segments and processed by the CNN + RNN architecture to derive the number of apneic events per segment. The apnea-hypopnea index (AHI) was computed from the CNN + RNN-derived estimates and grouped into four OSA severity levels. The Gradient-weighted Class Activation Mapping (Grad-CAM) XAI algorithm was used to identify and interpret novel OSA-related patterns of interest. The AHI regression reached very high agreement (intraclass correlation coefficient > 0.9), while OSA severity classification achieved 4-class accuracies 74.51% and 62.31%, and 4-class Cohen’s Kappa 0.6231 and 0.4495, in CHAT and the private datasets, respectively. All diagnostic accuracies on increasing AHI cutoffs (1, 5 and 10 events/h) surpassed 84%. The Grad-CAM heatmaps revealed that the model focuses on sudden AF cessations and SpO2 drops to detect apneas and hypopneas with desaturations, and often discards patterns of hypopneas linked to arousals. Therefore, an interpretable CNN + RNN model to analyze AF and SpO2 can be helpful as a diagnostic alternative in symptomatic children at risk of OSA.
2024-01-01T00:00:00Z
An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Vaquerizo Villar, Fernando
Gutiérrez Tobal, Gonzalo César
Calvo, Eva
Álvarez, Daniel
Kheirandish Gozal, Leila
Campo Matias, Félix del
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/61507
2023-09-11T19:01:06Z
2023-01-01T00:00:00Z
Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests.
2023-01-01T00:00:00Z
Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses
Rodríguez González, Víctor
Núñez Novo, Pablo
Gómez Peña, Carlos
Shigihara, Yoshihito
Hoshi, Hideyuki
Tola Arribas, Miguel Ángel
Cano, Mónica
Guerrero Peral, Angel Luis
García Azorín, David
Hornero Sánchez, Roberto
Poza Crespo, Jesús
https://uvadoc.uva.es/handle/10324/61503
2023-09-11T19:01:03Z
2023-01-01T00:00:00Z
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as “canonical” frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the “Connectivity-based Meta-Bands” (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the “canonical” frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
2023-01-01T00:00:00Z
Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs
Martínez Cagigal, Víctor
Santamaría Vázquez, Eduardo
Pérez Velasco, Sergio
Marcos Martínez, Diego
Moreno Calderón, Selene
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/59955
2023-06-26T19:00:40Z
2023-01-01T00:00:00Z
Code-modulated visual evoked potentials (c-VEPs) have marked a milestone in the scientific literature due to their ability to achieve reliable, high-speed brain–computer interfaces (BCIs) for communication and control. Generally, these expert systems rely on encoding each command with shifted versions of binary pseudorandom sequences, i.e., flashing black and white targets according to the shifted code. Despite the excellent results in terms of accuracy and selection time, these high-contrast stimuli cause eyestrain for some users. In this work, we propose the use of non-binary p-ary m-sequences, whose levels are encoded with different shades of gray, as a more pleasant alternative than traditional binary codes. The performance and visual fatigue of these p-ary m-sequences, as well as their ability to provide reliable c-VEP-based BCIs, are analyzed for the first time.
2023-01-01T00:00:00Z
Exploring the alterations in the distribution of neural network weights in dementia due to alzheimer’s disease
Revilla Vallejo, Marcos
Poza Crespo, Jesús
Gómez Pilar, Javier
Hornero Sánchez, Roberto
Tola Arribas, Miguel Ángel
Cano, Mónica
Gómez Peña, Carlos
https://uvadoc.uva.es/handle/10324/59686
2023-05-23T19:00:43Z
2021-01-01T00:00:00Z
Alzheimer’s disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.
2021-01-01T00:00:00Z
ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces
Marcos Martínez, Diego
Santamaría Vázquez, Eduardo
Martínez Cagigal, Víctor
Pérez Velasco, Sergio
Rodríguez González, Víctor
Martín Fernández, Ana
Moreno Calderón, Selene
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/59560
2023-05-17T19:00:38Z
2023-01-01T00:00:00Z
Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user’s brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms.
2023-01-01T00:00:00Z
Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study
Martín Rodríguez, Francisco
Vaquerizo Villar, Fernando
López Izquierdo, Raúl
Castro Villamor, Miguel Ángel
Sanz García, Ancor
Pozo Vegas, Carlos del
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/59470
2023-05-03T19:07:21Z
2023-01-01T00:00:00Z
Identifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation—validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51–81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841–0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.
2023-01-01T00:00:00Z
Assessment of airflow and oximetry signals to detect pediatric sleep apnea-hypopnea syndrome using AdaBoost
Jiménez García, Jorge
Gutiérrez Tobal, Gonzalo César
García Gadañón, María
Kheirandish Gozal, Leila
Martín Montero, Adrián
Álvarez, Daniel
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/58999
2023-03-22T20:03:23Z
2020-01-01T00:00:00Z
The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO2) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO2 signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens’s kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea–hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO2 was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO2 enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.
2020-01-01T00:00:00Z
MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research
Santamaría Vázquez, Eduardo
Martínez Cagigal, Víctor
Marcos Martínez, Diego
Rodríguez Gonzílez, Víctor
Pérez Velasco, Sergio
Moreno Calderón, Selene
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/58350
2023-03-29T19:02:28Z
2023-01-01T00:00:00Z
Background and objective. Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations. Methods. We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages. Results. MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility. Conclusions. MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.
2023-01-01T00:00:00Z
Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability
Martín Montero, Adrián
Armañac Julián, Pablo
Gil, Eduardo
Kheirandish Gozal, Leila
Álvarez, Daniel
Lázaro, Jesús
Bailón, Raquel
Gozal, David
Laguna, Pablo
Hornero Sánchez, Roberto
Gutiérrez Tobal, Gonzalo César
https://uvadoc.uva.es/handle/10324/58346
2023-03-29T19:02:27Z
2023-01-01T00:00:00Z
Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-minute segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1–5 e/s; 5–10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.
2023-01-01T00:00:00Z
Irregularity and variability analysis of airflow recordings to facilitate the diagnosis of paediatric sleep apnoea-hypopnoea syndrome
Barroso García, Verónica
Gutiérrez Tobal, Gonzalo César
Kheirandish Gozal, Leila
Álvarez González, Daniel
Vaquerizo Villar, Fernando
Crespo Sedano, Andrea
Campo Matias, Félix del
Gozal, David
Hornero Sánchez, Roberto
https://uvadoc.uva.es/handle/10324/56750
2023-03-08T08:07:42Z
2017-01-01T00:00:00Z
The analysis of Metacognitive skills is a key element to guide the learning process. Current research has shown the initiation of these skills from an early age. The present study had two aims: (1) to validate a Scale Measuring Precursor Metacognitive Skills (SMPMS) in children with diverse disabilities, and (2) to study possible significant different between different disabilities in precursor metacognitive skill use. We worked with 87 children with different disabilities, with an average age range of 24–37 months. The results have shown high indicators of reliability and validity of the SMPMS. We isolated two factors related to cognitive and metacognitive and self-regulation skills response to an adult. We also found significant differences in the acquisition of metacognitive and self-regulation skills among children with global developmental retardation as compared to children with expressive language and comprehension disability.
2017-01-01T00:00:00Z
Multiscale entropy analysis of unattended oximetric recordings to assist in the screening of paediatric sleep apnoea at home
Crespo Sedano, Andrea
Álvarez González, Daniel
Gutiérrez Tobal, Gonzalo César
Vaquerizo Villar, Fernando
Barroso García, Verónica
Alonso Álvarez, María Luz
Terán Santos, Joaquín
Hornero Sánchez, Roberto
Campo Matias, Félix del
https://uvadoc.uva.es/handle/10324/56708
2023-04-20T10:57:18Z
2017-01-01T00:00:00Z
Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.
2017-01-01T00:00:00Z