Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO2 Signals

Remo Lazazzera, Margot Deviaene, Carolina Varon, Bertien Buyse, Dries Testelmans, Pablo Laguna, Eduardo Gil, Guy Carrault

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.

Original languageEnglish
Article number9210227
Pages (from-to)1496-1506
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number5
DOIs
Publication statusPublished - 2021

Keywords

  • apnea classification
  • Apnea detection
  • DAP
  • PPG
  • PRV

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