Publicaciones en colaboración con investigadores/as de University of Missouri (22)

2024

  1. An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals

    Biomedical Signal Processing and Control, Vol. 87

2023

  1. An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea

    Computers in Biology and Medicine, Vol. 165

  2. Sleep-disordered breathing: Diagnosis

    Sleep Medicine: A Comprehensive Guide for Transitioning Pediatric to Adult Care (Springer International Publishing), pp. 69-95

2022

  1. A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

    Computers in biology and medicine, Vol. 147, pp. 105784

  2. A convolutional neural network to classify sleep stages in pediatric sleep apnea from pulse oximetry signals

    MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings

  3. Heart rate variability as a potential biomarker of pediatric obstructive sleep apnea resolution

    Sleep, Vol. 45, Núm. 2

  4. Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis

    Pediatric Pulmonology, Vol. 57, Núm. 8, pp. 1931-1943

2021

  1. A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea

    IEEE Journal of Biomedical and Health Informatics, Vol. 25, Núm. 8, pp. 2906-2916

  2. Bispectral analysis of heart rate variability to characterize and help diagnose pediatric sleep apnea

    Entropy, Vol. 23, Núm. 8

  3. Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis

    Computers in Biology and Medicine, Vol. 129

  4. Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults

    Applied Soft Computing, Vol. 111

  5. Erratum to: Martín-montero et al. bispectral analysis of heart rate variability to characterize and help diagnose pediatric sleep apnea. entropy 2021, 23, 1016

    Entropy

  6. Heart rate variability spectrum characteristics in children with sleep apnea

    Pediatric Research, Vol. 89, Núm. 7, pp. 1771-1779

  7. Pediatric Sleep Apnea: The Overnight Electroencephalogram as a Phenotypic Biomarker

    Frontiers in Neuroscience, Vol. 15

  8. Wavelet analysis of overnight airflow to detect obstructive sleep apnea in children

    Sensors, Vol. 21, Núm. 4, pp. 1-19

2020

  1. Assessment of airflow and oximetry signals to detect pediatric sleep Apnea-Hypopnea syndrome using AdaBoost

    Entropy, Vol. 22, Núm. 6, pp. 670

  2. Network Analysis on Overnight EEG Spectrum to Assess Relationships Between Paediatric Sleep Apnoea and Cognition

    IFMBE Proceedings

  3. Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis

    Computer Methods and Programs in Biomedicine, Vol. 183

2019

  1. Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, Vol. 2019, pp. 3555-3558

  2. Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS