Automated detection and classification of patient-ventilator asynchrony by means of machine learning and simulated data

[1]  L. Brochard,et al.  Timing of inspiratory muscle activity detected from airway pressure and flow during pressure support ventilation: the waveform method , 2022, Critical Care.

[2]  H. Ge,et al.  Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning , 2021, Sensors.

[3]  M. Mischi,et al.  A model-based approach to generating annotated pressure support waveforms , 2021, Journal of Clinical Monitoring and Computing.

[4]  Chen-Nee Chuah,et al.  Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit , 2020, IEEE Pervasive Computing.

[5]  M. Mischi,et al.  A machine learning method for automatic detection and classification of patient-ventilator asynchrony , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[6]  Xiaolin Zhou,et al.  Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network , 2020, Comput. Biol. Medicine.

[7]  Sol Fernandez-Gonzalo,et al.  Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities , 2019, Intensive Care Medicine Experimental.

[8]  Bambi Stefano,et al.  Patient-ventilator asynchronies: types, outcomes and nursing detection skills , 2018, Acta bio-medica : Atenei Parmensis.

[9]  Wassim M. Haddad,et al.  Replicating human expertise of mechanical ventilation waveform analysis in detecting patient-ventilator cycling asynchrony using machine learning , 2018, Comput. Biol. Medicine.

[10]  I. Ramirez,et al.  Identifying Patient-Ventilator Asynchrony Using Waveform Analysis , 2017 .

[11]  J. Burgerhof,et al.  Patient-Ventilator Asynchrony During Assisted Ventilation in Children , 2016, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

[12]  L. Brochard,et al.  Clinical challenges in mechanical ventilation , 2016, The Lancet.

[13]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[14]  Robert M. Kacmarek,et al.  Asynchronies during mechanical ventilation are associated with mortality , 2015, Intensive Care Medicine.

[15]  Paolo Navalesi,et al.  Efficacy of ventilator waveforms observation in detecting patient–ventilator asynchrony* , 2011, Critical care medicine.

[16]  S. Epstein.  How Often Does Patient-Ventilator Asynchrony Occur and What Are the Consequences? , 2011, Respiratory Care.

[17]  C. Sessler,et al.  Patient-ventilator dyssynchrony: clinical significance and implications for practice. , 2009, Critical care nurse.

[18]  Arnaud W. Thille,et al.  Patient–ventilator asynchrony during non-invasive ventilation for acute respiratory failure: a multicenter study , 2009, Intensive Care Medicine.

[19]  Stefano Nava,et al.  Automatic detection of ineffective triggering and double triggering during mechanical ventilation , 2007, Intensive Care Medicine.

[20]  Arthur S Slutsky.  Mechanical Ventilation , 1993, Chest.

[21]  N. L. Loo,et al.  Classification Patient-Ventilator Asynchrony with Dual-Input Convolutional Neural Network , 2021, IFAC-PapersOnLine.

[22]  Chee Pin Tan,et al.  A machine learning model for real-time asynchronous breathing monitoring , 2018 .

[23]  E. Kondili,et al.  Bedside waveforms interpretation as a tool to identify patient-ventilator asynchronies , 2005, Intensive Care Medicine.

[24]  M. Tobin.  Mechanical ventilation. , 1994, The New England journal of medicine.