Apnea Detection Based on Time Delay Neural Network

Sleep apnea syndrome (SAS) is a very common sleep disorder disease. Reliable detection of apnea is very crucial for subsequent treatment. In this article, a novel method based on artificial neural network is proposed for such purpose. With its time-invariant property the time delay neural network (TDNN) is adopted in this system to employ the temporal trend of apnea event. As airflow and SaO take the most important roles in sleep apnea syndrome diagnosis, features extracted from both of them are simultaneously fed into the neural network. The proposed algorithm was tested with 15 overnight polysomnographic (PSG) records, and with a sensitivity rate of 90.7% and 80.8%, a specificity rate of 86.4% and 81.4% for apnea and hypopnea detection, respectively. Furthermore, the proposed algorithm can accommodate in some manner the airflow sensor failure due to technical errors. But, as the SaO2 changes are commonly delayed by 10 or more seconds compared to the airflow signal, integration of SaO2 make this method only suited for offline detection. In conclusion, systems based on this algorithm can be used as a valuable timesaving adjunct for PSG SAS diagnosis