Automated detection of obstructive apnea and hypopnea events from oxygen saturation signal

Our objective is to automate the detection of apnea and hypopnea events in obstructive sleep apnea hypopnea (OSAH) syndrome based on analysis of arterial oxygen saturation signal alone. This is the first attempt where wavelet is used to detect OSAH events. Detection of OSAH events through wavelet depends on the fluctuations in the magnitude of the transformed coefficients, thus circumventing the problem of variability in the criteria on the magnitude and duration of the signal. Our work evaluates the performance of the wavelet transform to detect OSAH events against three conventional amplitude and duration algorithms. High performance in the detection of OSAH events can be achieved through the wavelet algorithm (score 96.55%, sensitivity 95.74% and specificity 97.02%) if the threshold on wavelet coefficients is individually tuned for each study. However, this is impossible in clinical practice. It is interesting to observe that the conventional methods based on amplitude and duration are able to attain a performance as close as this. The Nervus algorithm obtains the best result (score 96.66%, sensitivity 95.26% and specificity 97.46%) compared to the amplitude duration algorithm, the drop duration algorithm and the wavelet algorithm with global threshold, in descending order of performance.