Sleep apnea detection and classification using fuzzy logic: clinical evaluation.

We have previously reported a system suitable for detection and classification of sleep apnea syndromes. This paper reports the results of the clinical evaluation of the proposed system. In the current implementation, the system uses breathing signals: nasal flow, thorax movement, and abdomen movement. The detection part of the system uses only the nasal flow signal to detect apnea employing two engines used in series. It then feeds segments labeled as abnormal to the classification part of the system, which uses the center of gravity of each segment to determine the type of abnormality: obstructive, central or hypopnea. In comparison to other systems, this implementation can be shown to be simpler and more accurate. When the low implementation cost is taken into consideration, the proposed system has a substantial potential for being used as a screening device.