Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea
Artificial Intelligence in Medicine
An amplitude signal based technique for hypopneas detection
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Model Comparison for the Detection of EEG Arousals in Sleep Apnea Patients
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A novel sleep apnea detection system in electroencephalogram using frequency variation
Expert Systems with Applications: An International Journal
Reducing dimensionality in a database of sleep EEG arousals
Expert Systems with Applications: An International Journal
Algorithms for the analysis of polysomnographic recordings with customizable criteria
Expert Systems with Applications: An International Journal
A method for the automatic analysis of the sleep macrostructure in continuum
Expert Systems with Applications: An International Journal
Automated detecting and classifying of sleep apnea syndrome based on genetic-SVM
International Journal of Hybrid Intelligent Systems
| Hi-index | 12.06 |
The sleep apnea/hypopnea syndrome is a very common sleep disorder, characterised by disrupted breathing during sleep. Depending on the extent of the disruptions to sleep, these are classified as apneas or hypopneas. In order to locate these apneic events an analysis of respiratory signals recorded for an entire night's sleep is necessary. However, identifying and classifying apneic events is a complex task, given the error associated with the process for digitising signals, variability in expert criteria and the complexity of the signals themselves. This article describes a fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas. The aim is to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty. The ultimate goal was to assist the physician in diagnosing the sleep apnea/hypopnea syndrome. Results in terms of locating events in the polysomnogram showed sensitivity and specificity of 0.87 and 0.89, respectively. A receiver operating curve index of 0.88 was obtained for the classification of events as apneas or hypopneas.