title: Age and gender do not influence the ability to detect respiration by photoplethysmography.
abstract: OBJECTIVE
  The non-invasive technique photoplethysmography (PPG) can detect changes in blood volume and perfusion in a tissue. Respiration causes variations in the peripheral circulation, making it possible to monitor breaths using an optical sensor attached to the skin. The respiratory-synchronous part of the PPG signal (PPGr) has been used to monitor respiration during anaesthesia, and in postoperative and neonatal care. Studies addressing possible differences in PPGr signal characteristics depending on gender or age are lacking.
  
  
  METHODS
  We studied three groups of 16 healthy subjects each during normal breathing; young males, old males and young females, and calculated the concordance between PPGr, derived from a reflection mode PPG sensor on the forearm, and a reference CO(2 )signal. The concordance was quantified by using a squared coherence analysis. Time delay between the two signals was calculated. In this process, we compared three different methods for calculating time delay.
  
  
  RESULTS
  Coherence values >or=0.92 were seen for all three groups without any significant differences depending on age or gender (p = 0.67). Comparison between the three different methods for calculating time delay showed a correlation r = 0.93.
  
  
  CONCLUSIONS
  These results demonstrate clinically important information implying the possibility to register qualitative PPGr signals for respiration monitoring, regardless of age and gender.
authors.affiliation: Department of Anaesthesiology and Intensive Care, Linköping University Hospital, Linköping, S-581 85, Sweden. lena.nilsson@lio.se
authors.forename: Lena
authors.initials: L
authors.lastname: Nilsson
authors.forename: Tomas
authors.initials: T
authors.lastname: Goscinski
authors.forename: Anders
authors.initials: A
authors.lastname: Johansson
authors.forename: Lars-Göran
authors.initials: LG
authors.lastname: Lindberg
authors.forename: Sigga
authors.initials: S
authors.lastname: Kalman
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000706:statistics & numerical data; D017156:Photoplethysmography / Q000706:statistics & numerical data; D012129:Respiratory Function Tests / Q000706:statistics & numerical data; D012143:Respiratory Physiological Phenomena; D012727:Sex Characteristics; D012815:Signal Processing, Computer-Assisted