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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier.

Haitham M Al-Angari1, Alan V Sahakian

  • 1Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA. hangari@northwestern.edu

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|January 31, 2012
PubMed
Summary

Obstructive sleep apnea (OSA) detection can be improved using machine learning. Support vector machines analyzing respiratory, heart rate, and oxygen saturation signals achieved high accuracy in classifying sleep apnea events and patients.

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Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
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Published on: December 6, 2016

Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Respiratory Medicine

Background:

  • Obstructive sleep apnea (OSA) is a prevalent respiratory disorder characterized by upper airway obstruction during sleep.
  • Physiological signals such as heart rate variability, oxygen saturation, and respiratory effort are affected by OSA.

Purpose of the Study:

  • To evaluate the effectiveness of Support Vector Machine (SVM) classifiers in detecting obstructive sleep apnea using physiological signals.
  • To compare the performance of linear and polynomial kernels for both minute-by-minute and subject-level OSA classification.

Main Methods:

  • Features were extracted from heart rate variability, oxygen saturation, and respiratory effort signals from 50 OSA patients and 50 controls from the Sleep Heart Health Study database.
  • A Support Vector Machine (SVM) classifier with linear and second-order polynomial kernels was employed for classification tasks.
  • Performance was assessed using sensitivity, specificity, and accuracy for minute and subject classifications.

Main Results:

  • For minute classification, respiratory features yielded the highest sensitivity, and oxygen saturation provided the highest specificity.
  • The polynomial kernel consistently outperformed the linear kernel, achieving a maximum accuracy of 82.4% with combined features.
  • For subject classification, the polynomial kernel significantly improved oxygen saturation accuracy, reaching 95% with either oxygen saturation alone or combined features.

Conclusions:

  • SVM classifiers, particularly with polynomial kernels and combined physiological features, demonstrate high accuracy for automated obstructive sleep apnea detection.
  • Oxygen saturation and combined features are highly effective for subject-level classification of OSA.
  • Further research exploring diverse SVM kernel types could further optimize automated OSA detection algorithms.