Methods for obtaining and analyzing unattended polysomnography data for a multicenter study

  • Susan Redline
  • , Mark H. Sanders
  • , Bonnie K. Lind
  • , Stuart F. Quan
  • , Conrad Iber
  • , Daniel J. Gottlieb
  • , William H. Bonekat
  • , David M. Rapoport
  • , Philip L. Smith
  • , James P. Kiley

Research output: Contribution to journalArticlepeer-review

475 Scopus citations

Abstract

This paper reviews the data collection, processing, and analysis approaches developed to obtain comprehensive unattended polysomnographic data for the Sleep Heart Health Study, a multicenter study of the cardiovascular consequences of sleep-disordered breathing. Protocols were developed and implemented to standardize in-home data collection procedures and to perform centralized sleep scoring. Of 7027 studies performed on 6697 participants, 5 534 studies were determined to be technically acceptable (failure rate 5.3%). Quality grades varied over time, reflecting the influences of variable technician experience, and equipment aging and modifications. Eighty-seven percent of studies were judged to be of 'good' quality or better, and 75% were judged to be of sufficient quality to provide reliable sleep staging and arousal data. Poor submental EMG (electromyogram) accounted for the largest proportion of poor signal grades (9% of studies had <2 hours artifact free EMG signal). These data suggest that with rigorous training and clear protocols for data collection and processing, good-quality multichannel polysomnography data can be obtained for a majority of unattended studies performed in a research setting. Data most susceptible to poor signal quality are sleep staging and arousal data that require clear EEG (electroencephalograph) and EMG signals.

Original languageEnglish (US)
Pages (from-to)759-767
Number of pages9
JournalSleep
Volume21
Issue number7
DOIs
StatePublished - Nov 1 1998

Keywords

  • Epidemiology
  • Polysomnography
  • Sleep apnea

Fingerprint

Dive into the research topics of 'Methods for obtaining and analyzing unattended polysomnography data for a multicenter study'. Together they form a unique fingerprint.

Cite this