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de Haro C, Santos-Pulpón V, Telías I, Xifra-Porxas A, Subirà C, Batlle M, Fernández R, Murias G, Albaiceta GM, Fernández-Gonzalo S, Godoy-González M, Gomà G, Nogales S, Roca O, Pham T, López-Aguilar J, Magrans R, Brochard L, Blanch L, Sarlabous L. Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques. Crit Care 2024; 28:75. [PMID: 38486268 PMCID: PMC10938655 DOI: 10.1186/s13054-024-04845-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/19/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. METHODS Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. RESULTS 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O. CONCLUSIONS Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.
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Affiliation(s)
- Candelaria de Haro
- Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain.
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.
| | - Verónica Santos-Pulpón
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
| | - Irene Telías
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
- Division of Respirology, Department of Medicine, University Health Network and Sinai Health System, Toronto, ON, Canada
| | - Alba Xifra-Porxas
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
| | - Carles Subirà
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Critial Care Department, Althaia Xarxa Assistencial Universtaria de Manresa, Manresa, Spain
- IRIS - Catalunya Central I Grup de Recerca de Malalt Crític, Manresa, Spain
| | - Montserrat Batlle
- Critial Care Department, Althaia Xarxa Assistencial Universtaria de Manresa, Manresa, Spain
- IRIS - Catalunya Central I Grup de Recerca de Malalt Crític, Manresa, Spain
| | - Rafael Fernández
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Critial Care Department, Althaia Xarxa Assistencial Universtaria de Manresa, Manresa, Spain
- IRIS - Catalunya Central I Grup de Recerca de Malalt Crític, Manresa, Spain
| | - Gastón Murias
- Critical Care Department, Hospital Británico, Buenos Aires, Argentina
| | - Guillermo M Albaiceta
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias. Universidad de Oviedo, Oviedo, Spain
| | - Sol Fernández-Gonzalo
- Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Gemma Gomà
- Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Nogales
- Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Oriol Roca
- Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Tai Pham
- Service de Médecine Intensive-Réanimation, Hôpital de Bicêtre, DMU CORREVE, FHU SEPSIS, Groupe de Recherche Clinique CARMAS, Université Paris-Saclay, AP-HP, Le Kremlin-Bicêtre, France
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm U1018, Equipe d'Epidémiologie Respiratoire Intégrative, Center de Recherche en Epidémiologie et Santé Des Populations, Villejuif, France
| | - Josefina López-Aguilar
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
| | | | - Laurent Brochard
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Lluís Blanch
- Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Leonardo Sarlabous
- Centro Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
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Chen Z, Zhang X, Huang W, Gao J, Zhang S. Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification. Front Neurorobot 2021; 15:654519. [PMID: 34108871 PMCID: PMC8180855 DOI: 10.3389/fnbot.2021.654519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 04/14/2021] [Indexed: 11/23/2022] Open
Abstract
Deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenarios, due to the low diversity of several known training samples, they are prone to be dominated by specificity, thus leading to one-sidedness local features instead of the reliable global feature of the actual categories they belong to. To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages the contextual information as a supplement and learns context awareness transfer in few-shot image classification scenes, which fully utilizes the information in heterogeneous data. The similarity measure in the image classification task is reformulated via fusing textual semantic modal information and visual semantic modal information extracted from images. This performs as a supplement and helps to inhibit the sample specificity. Besides, to better extract local visual features and reorganize the recognition pattern, the deep transfer scheme is also used for reusing a powerful extractor from the pre-trained model. Simulation experiments show that the introduction of cross-modal and intra-modal contextual information can effectively suppress the deviation of defining category features with few samples and improve the accuracy of few-shot image classification tasks.
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Affiliation(s)
- Zhikui Chen
- The School of Software Technology, Dalian University of Technology, Dalian, China
- The Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
| | - Xu Zhang
- The School of Software Technology, Dalian University of Technology, Dalian, China
| | - Wei Huang
- Department of Critical Care Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jing Gao
- The School of Software Technology, Dalian University of Technology, Dalian, China
- The Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
| | - Suhua Zhang
- The School of Software Technology, Dalian University of Technology, Dalian, China
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Wang C, Sun H, Zhao R, Cao X. Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series. SENSORS 2020; 20:s20247031. [PMID: 33302521 PMCID: PMC7764092 DOI: 10.3390/s20247031] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/25/2020] [Accepted: 12/05/2020] [Indexed: 11/18/2022]
Abstract
In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization.
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Affiliation(s)
- Changdong Wang
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (C.W.); (X.C.)
- Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education, Northeastern University, Shenyang 110819, China
| | - Hongchun Sun
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (C.W.); (X.C.)
- Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education, Northeastern University, Shenyang 110819, China
- Correspondence:
| | - Rong Zhao
- College of Sciences, Northeastern University, Shenyang 110819, China;
| | - Xu Cao
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (C.W.); (X.C.)
- Key Laboratory of Vibration and Control of Aero-Propulsion Systems of Ministry of Education, Northeastern University, Shenyang 110819, China
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