Corpus ID: 16431778

Sleep stages classification using vital signals recordings

@article{Klein2015SleepSC,
  title={Sleep stages classification using vital signals recordings},
  author={A. Klein and Oana Ramona Velicu and N. M. Madrid and R. Seepold},
  journal={2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)},
  year={2015},
  pages={47-50}
}

A. Klein, Oana Ramona Velicu, +1 author R. Seepold

Published 2015

Computer Science

2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)

To evaluate the quality of a person's sleep it is essential to identify the sleep stages and their durations. [...] Key Method The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%.Expand Abstract

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