Fu-Chung Yen

A noninvasive technique for detecting obstructive and central sleep apnea

A new noninvasive method to detect obstructive and central sleep apnea [(OSA) and (CSA)] events is described. Data were collected from ten volunteer subjects with a previous diagnosis of OSA while they were titrated for continuous positive airway pressure (CPAP) therapy. Apneic events were identify by analyzing of estimated airway impedance determined from pressure and airflow signals delivered from CPAP. To enhance performance of this technique, a single-frequency (5 Hz with 0.5 cmH/sub 2/O peak-to-peak amplitude) probing signal was superimposed on the applied CPAP pressure. The results indicated that estimated airway impedance during OSA (mean: 17.9, SD: 3.4, N=50) was significantly higher then during CSA (mean: 4.1, SD: 1.7, N=50). When the estimated impedance of OSA and CSA events were compared to a fixed threshold, 100% of all events can be correctly categorized. These results indicate that it may be possible to diagnose OSA and CSA noninvasively based upon this technique. The instrument and the algorithm required are relatively simple and can be incorporated in a home-based device. If this method was used for prescreening apnea patients, it could reduce cost, waiting time, and discomfort associated with traditional diagnostic procedures.

Sleep apnea detection using flow spectral analysis and fuzzy logic

Diagnosis of sleep apnea is currently performed by a full night polysomnography study at sleep laboratories. The majority of apnea patients are then treated by constant positive airway pressure (CPAP) device. A new algorithm to detect and classify apnea that could be easily incorporated into CPAP was developed. The study used data from ten subjects (6 males, 4 females, age mean (SD): 52 (13), BMI mean (SD): 34 (8)) who were previously diagnosed with sleep apnea. The CPAP flow data was used for the algorithm development. Spectral analysis of data was performed and area, weighted mean frequency and amplitude were determined. These were fed as inputs to a fuzzy logic program, which was used to detect normal, obstructive sleep apnea (OSA) and central sleep apnea (CSA). The data analyzed included 591 normal breaths and 306 apnea events (165 obstructive and 140 central). Of these 299 normal breaths and 148 apnea events (77 obstructive and 71 central) were used for algorithm development and the remainder for testing. The algorithm had correct detection rates of 99.6% for normal breaths, 69.6% for CSA and 71.9% for OSA. The results suggest that the algorithm could be successfully used to determine if a subject had apnea or not. It has limited success in differentiating apnea types.

Pharyngeal wall vibration detection using an artificial neural network

An artificial-neural-network-based detector of pharyngeal wall vibration (PWV) is presented. PWV signals the imminent occurrence of obstructive sleep apnoea (OSA) in adults who suffer from OSA syndrome. Automated detection of PWV is very important in enhancing continuous positive airway pressure (CPAP) therapy by allowing automatic adjustment of the applied airway pressure by a procedure called automatic positive airway 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 mask pressure data from five positively diagnosed OSA patients. The performance of the ANN-based detector is evaluated using data from five different OSA patients. The results show that on the average it correctly detects the presence of PWV events at a rate of ≅92% and correctly distinguishes normal breaths ≅98% of the time. Further, the ANN-based detector accuracy is not affected by the pressure level required for therapy.

Long term performance evaluation of an automatic airway positive pressure device

Long term studies of automatic continuous positive airway pressure (auto CPAP or APAP) device is presented. An APAP device is designed to eliminate OSA events automatically. It detects the pharyngeal wall vibration (PWV) signal and uses it as a feedback signal to adjust airway pressure. Two positively diagnosed obstructive sleep apnea patients participated in this study. A computer-based data acquisition system was used to collect the APAP pressure signal during the sleeping hours. Pressure trends for 182 nights for the first patient and 260 nights for the second patient were obtained. Both patients reported satisfaction with the APAP therapy. The pressure trends obtained from these two subjects revealed that the pressure level required for eliminating PWV changes thoughout the night and from night to night as well as the mean APAP pressure level was significant lower than the CPAP prescribed pressure (1st subject: 3.2 cm H/sub 2/O of mean APAP pressure vs. 7 cm H/sub 2/O of CPAP prescribed pressure; 2nd subject: 3.7 cm H/sub 2/O of mean APAP pressure vs. 15 cm H/sub 2/O of CPAP prescribed pressure). Therefore, adjusting the blower pressure automatically based on PWV signal detection may provide comparable treatment to that of a conventional CPAP device with lower airway pressure.

Adaptive positive airway pressure (APAP) therapy for obstructive sleep apnea

A new adaptive positive pressure airway (MAP) therapy system is described that automatically adjusts the pressure needed to alleviate obstructive sleep apnea. It provides an alternative mode of therapy to continuous positive airway pressure (CPAP) which does not accommodate the patients need for different pressure through out the night or from night to night.