AI Article Synopsis
- The research presents an automated tool designed to identify Rapid Eye Movements (REMs) in polysomnogram recordings by applying expert modeling criteria.* -
- The tool first locates segments likely containing REMs, then finds high-energy REMs and subsequently searches for lesser-energy REMs in defined surrounding areas, effectively reducing false positives.* -
- Validated on a database of 20 polysomnogram recordings from healthy ten-year-olds, the tool achieved 85.5% sensitivity and a 16.2% false positive rate, and operates without preprocessing or prior knowledge of sleep stages.*
Article Abstract
We present an automated multiple-step tool to identify Rapid Eye Movements (REMs) in the polysomnogram, based on modeling expert criteria. It begins by identifying the polysomnogram segments compatible with REMs presence. On these segments, high-energy REMs are identified. Then, vicinity zones around those REMs are defined, and lesser-energy REMs are sought in these vicinities. This strategy has the advantage that it can detect lesser-energy REMs without increasing much the false positive detections. Signal processing, feature extraction, and fuzzy logic tools are used to achieve the goal. The tool was trained and validated on a database consisting of 20 all-night polysomnogram recordings (160 hr) of healthy ten-year-old children. Preliminary results on the validation set show 85.5% sensitivity and a false positive rate of 16.2%. Our tool works on complete polysomnogram recordings, without the need of preprocessing, prior knowledge of the hypnogram, or noise-free segments selection.
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| http://dx.doi.org/10.1109/EMBC.2012.6346414 | DOI Listing |
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