An NN Based Feature Analysis Model for Sleep Apnea Identification
Author Name : Bhanu Priya, Ms. Isha
ABSTRACT ECG signal is able to identify different kind of heart disease. One of the critical heart problem is the abnormal heart beat behavior during sleep. This problem is recognized as sleep apnea. In this work, a feature adaptive model is presented for Sleep apnea identification. To generate the effective signal features, the spectral subtraction and DWT methods are applied at earlier phase. After generating the low level features, the neural network is applied for signal recognition and disease class identification. The implementation results are obtained on real time sleep apnea dataset. The results show that the work model has provided the recognition rate over 80%.