S U M M A R Y This paper presents a modified form of polarization position correlation (PPC) operator which can be used to separate P-and S-waves in a multicomponent seismic profile. The essence of the method (in seeking S-wave extinction) is to form a dot product between the signal vector and the slowness vector during projection of the seismic section into t -p space, using the P-wave velocity profile measured along the array. The dot product (in effect) is a linear controlled direction reception filter (CDR type 1) which selectively passes only P arrivals.The second step is to use the converse rotation operator, during the forward transform, to compute both the P-wave w-p 'pass plane' and the orthogonal P-wave 'extinction plane'. The two together are needed in order to preserve a measure of the total energy falling within any w-p pixel in the original time sections. The extinction plane on its own gives a measure of the success achieved by the CDRI filter in isolating P-wave energy in a pixel on the pass plane. The best measure of this success is given by performing a cross-spectral matrix analysis of the two w-p planes on a pixel-by-pixel basis (summing over a window dw x dp). The ratio of the eigenvalues yields the rectilinearity of polarization. A 2-D gain function based on rectilinearity may be used as a non-linear boost function in order to enhance strongly polarized P-wave pixels in the w-p pass plane, prior to inverse RADON transformation.The success of this method in achieving wavefield skparation and background noise reduction is illustrated with synthetic and physical model seismic data.
Automated sleep staging based on EEG signal analysis provides an important quantitative tool to assist neurologists and sleep specialists in the diagnosis and monitoring of sleep disorders as well as evaluation of treatment efficacy. A complete visual inspection of the EEG recordings acquired during nocturnal polysomnography is time consuming, expensive, and often subjective. Therefore, feature extraction is implemented as an essential preprocessing step to achieve significant data reduction and to determine informative measures for automatic sleep staging. However, the analysis of the EEG signal and extraction of sensitive measures from it has been a challenging task due to the complexity and variability of this signal. We present three different schemes to extract features from the EEG signal: relative spectral band energy, harmonic parameters, and Itakura distance. Spectral estimation is performed by using autoregressive (AR) modeling. We then compare the performance of these schemes with the view to select an optimal set of features for specific, sensitive, and accurate neuro-fuzzy classification of sleep stages.
APAP appears to be as effective as CPAP in treating OSA patients. APAP delivers the same level of therapy as CPAP, but it reduces the average airway pressure while providing needed peak pressures.
Obstructive sleep apnea (OSA) occurs when airflow ceases because of pharyngeal wall collapse in sleep. Repeated apneic events results in the development of a pathological condition called OSA syndrome. We describe the methodology and design of a prosthetic device, named automatic positive airway pressure (APAP), for treatment of this syndrome. APAP applies a stream of air via a nasal mask at an initial pressure selected by the patient. By sensing specific pressure characteristics of air flow immediately preceding pharyngeal wall collapse, the APAP device automatically raises the applied pressure to maintain a patent upper airway and thus prevent apnea. Conversely, when such conditions are absent, pressure is lowered step wise until a preselected minimum pressure is reached. Performance evaluation of the APAP system in five OSA patients and five normal (asymptomatic for sleep apnea) subjects revealed that it effectively treated OSA syndrome. It lowered the apnea-hypopnea index without disturbing sleep and resulted in a lower mean airway pressure compared to the traditional continuous positive airway pressure (CPAP) therapy. The results also show that the pressure needed to prevent OSA varied significantly throughout the night. For OSA syndrome patients, this pressure ranged from 3 to 18 cm H2O. The mean airway pressure for these patients had a sample average of 6.80 cm H2O and a standard deviation of 3.17 cm H2O. In normal subjects, the device did not raise pressure except in response to Pharyngeal Wall Vibration events.
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