Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).
|Title of host publication||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014|
|Number of pages||4|
|State||Published - Nov 2 2014|
|Event||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States|
Duration: Aug 26 2014 → Aug 30 2014
|Name||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014|
|Conference||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014|
|Period||8/26/14 → 8/30/14|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications
- Biomedical Engineering
- Medicine (all)