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Related Experiment Video

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Central apnea detection in premature infants using machine learning.

Gabriele Varisco1, Zheng Peng1, Deedee Kommers2

  • 1Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Clinical Physics, Máxima Medical Center, Veldhoven, the Netherlands.

Computer Methods and Programs in Biomedicine
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models improve the detection of central apneas (CA) in premature infants by analyzing chest impedance signals. This approach significantly reduces false alarms, enhancing diagnostic accuracy for critical respiratory events.

Keywords:
Apnea of prematurityCentral apneaLate-onset sepsisMachine Learning

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Area of Science:

  • Neonatology
  • Medical Engineering
  • Machine Learning in Healthcare

Background:

  • Apnea of prematurity is a common neonatal intensive care unit diagnosis.
  • Current methods for detecting central apnea (CA) using chest impedance (CI) generate numerous false alarms.
  • Machine learning offers a potential solution to improve CA detection accuracy.

Purpose of the Study:

  • To enhance the automatic detection of central apneas (CAs) among central apnea-suspected events (CASEs) using machine learning.
  • To reduce the high rate of false alarms associated with current automated CA detection methods.
  • To optimize CA detection by analyzing multiple physiological signals and time windows.

Main Methods:

  • Extracted 47 features from ECG, CI, and oxygen saturation signals within four 30-second moving windows around CASEs.
  • Developed and evaluated machine learning models including logistic regression, random forest, and support vector machines.
  • Assessed model performance using leave-one-patient-out and 10-fold cross-validation, measuring the area under the receiver-operating-characteristic curve (AUROC).

Main Results:

  • The logistic regression model with elastic net penalty achieved the highest mean AUROC (0.88-0.90).
  • Features derived from chest impedance (CI) were most relevant for CA detection.
  • A specific threshold achieved high detection rates for CAs (78.2%), especially those with bradycardia (93.4%) or desaturation (95.2%).

Conclusions:

  • Machine learning models significantly improve the accuracy of central apnea detection in premature infants.
  • These models effectively reduce false alarms, leading to more reliable diagnoses.
  • Optimized detection of critical CA events, particularly those with associated bradycardia or desaturation, enhances infant care.