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Please use this identifier to cite or link to this item: http://hdl.handle.net/2328/25761

Title: A fuzzy inference system for detection of obstructive sleep apnea
Authors: Nazeran, Homer
Almas, Asher
Behbehani, Khosrow
Burk, John
Lucas, Edgar
Keywords: Data mining
Event detection
Fuzzy control
Fuzzy logic
Apnoea
Issue Date: 2001
Publisher: Institute of Electrical and Electronics Engineers Computer Society (IEEE Publishing)
Citation: Nazeran, H., Almas, A., Behbehani, K., Burk, J. and Lucas, E. 2001. A fuzzy inference system for detection of obstructive sleep apnea. 2001 Proceedings of the 23rd Annual Engineering in Medicine and Biology Society (EMBS) International Conference, Vol 2, 1645 - 1648.
Abstract: A fuzzy inference system (FIS) was developed to detect obstructive sleep apnea (OSA) by analyzing the respiratory airflow signal in adults. The parameters analyzed were the normalized area and the standard deviation of consecutive 3-second intervals of baseline adjusted and rectified airflow signal. Fuzzy logic was used to process these parameters to detect apnea and hypopnea when the output values were within a specified range extracted from OSA patient data. The FIS comprised three major stages of computation: fuzzification, fuzzy rule evaluation and defuzzification. The results demonstrated that the FIS reached an overall correct detection rate of 83% across all patients. The false negative rate was 17% and the false positive rate was 12%. The correct detection rate varied from patient to patient and correct rates greater than 90% were achieved in three patients. This study suggests that fuzzy inference could provide an intelligent algorithm for control of a continuous positive airway pressure (CPAP) machine. It would detect apnea and hypopnea events and automatically adjust the pressure to eliminate them. The performance of the algorithm could be further optimized to give increased detection rates. This could be achieved by performing further studies on a larger OSA patient population and utilizing augmentative methods such as neural networks to better sense the fuzzy patterns in the OSA data.
URI: http://hdl.handle.net/2328/25761
ISSN: 1094-687X
Appears in Collections:Computer Science, Engineering and Mathematics - Collected Works

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