Extracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification

A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, RE...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2006-10, Vol.53 (10), p.1954-1962
Main Authors: Held, C.M., Heiss, J.E., Estevez, P.A., Perez, C.A., Garrido, M., Algarin, C., Peirano, P.
Format: Article
Language:English
Subjects:
Algorithms
ANFIS
Data mining
Diagnosis, Computer-Assisted - methods
Electroencephalography
Female
Frequency
Fuzzy Logic
fuzzy rule extraction
Fuzzy sets
Fuzzy systems
Humans
Infant
knowledge discovery
Laboratories
Male
neural nets and expert systems
Pattern Recognition, Automated - methods
Pediatrics
Polysomnography - methods
rule pruning
Signal generators
Sleep
sleep classification
Sleep Stages - physiology
Studies
Testing
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Summary:A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REM-Sleep, Non-REM Sleep Stage 1, Stage 2, and Stage 3-4. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143plusmn39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83.9plusmn0.4% of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleep-wake classification system
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2006.881798