PHARYNGEAL WALL VIBRATION DETECTION USING AN ARTIFICIAL NEURAL-NETWORK

An artificial-neural-network-based detector of pharyngeal wall vibrati on (PWV) is presented. PWV signals the imminent occurrence of obstruct ive sleep apnoea (OSA) in adults who suffer from OSA syndrome, Automat ed detection of PWV is very important in enhancing continuous positive airway pressure (CPAP) therapy by allowing automatic adjustment of th e applied airway pressure by a procedure called automatic positive air way pressure (APAP) therapy. A network with 15 inputs, one output, and two hidden layers, each with two Adaline nodes, is used as part of a PWV detection scheme. The network is initially trained using nasal mas k pressure data from five positively diagnosed OSA patients. The perfo rmance of the ANN-based detector is evaluated using data from five dif ferent OSA patients. The results show that on the average it correctly detects the presence of PWV events at a rate of similar or equal to 9 2% and correctly distinguishes normal breaths similar or equal to 98% of the time, Further, the ANN-based detector accuracy is not affected by the pressure level required for therapy.