This paper presents an efficient and easy implemented method
for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal
based respiratory signals extracted from polysomnography recordings
are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on
the 1-minute and 3-minute length recordings. According to the preliminary tests,
the variances of 10th and 11th detail components can be used
as discriminative features for apneas. The features obtained from total 8 recordings
are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS).
Training and testing process have been repeated by using the randomly obtained five
different sequences of whole data for generalization of the ANFIS. According to
results, ANFIS based classification has sufficient accuracy for apnea detection
considering of each type of respiratory. However, the best result is obtained
by analyzing the 3-minute length nasal based respiratory signal. In this study,
classification accuracies have been obtained greater than 95.2% for each of the
five sequences of entire data.