Convolutional long short-term memory neural network integrated with classifier in classifying type of asynchrony breathing in mechanically ventilated patients.

Journal: Computer methods and programs in biomedicine
PMID: 39987666

Abstract

BACKGROUND AND OBJECTIVE: Asynchronous breathing (AB) occurs when a mechanically ventilated patient's breathing does not align with the mechanical ventilator (MV). Asynchrony can negatively impact recovery and outcome, and/or hinder MV management. A model-based method to accurately classify different AB types could automate detection and have a measurable clinical impact.

Authors

  • Nur Sa'adah Muhamad Sauki
    Electrical Engineering Studies, Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
  • Nor Salwa Damanhuri
    Electrical Engineering Studies, Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia. Electronic address: norsalwa071@uitm.edu.my.
  • Nor Azlan Othman
    Electrical Engineering Studies, Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
  • Yeong Shiong Chiew
    School of Engineering, Monash University Malaysia, Selangor, Malaysia; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: chiew.yeong.shiong@monash.edu.
  • Belinda Chong Chiew Meng
    Electrical Engineering Studies, Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
  • Mohd Basri Mat Nor
    Department of Anaesthesiology and Intensive Care, Kulliyah of Medicine, International Islamic University of Malaysia, Kuantan 25200, Malaysia.
  • J Geoffrey Chase
    Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.

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