John R. Burk

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.

EEG feature extraction for classification of sleep stages

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.

Automatic control of airway pressure for treatment of obstructive sleep apnea

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. The authors describe the methodology and design of a prosthetic device, named automatic positive airway pressure (APAP), for treatment of this syndrome. HPAP 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 H/sub 2/O. The mean airway pressure for these patients had a sample average of 6.80 cm H/sub 2/O and a standard deviation of 3.17 cm H/sub 2/O. In normal subjects, the device did not raise pressure except in response to pharyngeal wall vibration events.<<ETX>>

Itakura Distance: A Useful Similarity Measure between EEG and EOG Signals in Computer-aided Classification of Sleep Stages

Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic (EEG) and electro-oculographic (EOG) signals interfere with each other and they share a temporal similarity. In this investigation we used the EEG and EOG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by certified sleep specialists based on RK rules. In this pilot study, we performed spectral estimation of EEG signals by autoregressive (AR) modeling, and then used Itakura distance to measure the degree of similarity between EEG and EOG signals. We finally calculated the statistics of the results and displayed them in an easy to visualize fashion to observe tendencies for each sleep stage. We found that Itakura distance is the smallest for sleep stages 3 and 4. We intend to deploy this feature as an important element in automatic classification of sleep stages

Miliary tuberculosis diagnosed by fibreoptic bronchoscopy and transbronchial biopsy

Fibreoptic bronchoscopy with bronchial washing and trasnbronchial biopsy was performed in 8 patients with miliary tuberculosis and helped establish the diagnosis in 6 (75%). This procedure may provide a valuable adjunct to the diagnosis of miliary tuberculosis.

A Method to Detect Obstructive Sleep Apnea Using Neural Network Classification of Time-Frequency Plots of the Heart Rate Variability

This paper presents a new method of analyzing time-frequency plots of heart rate variability to detect sleep disordered breathing from nocturnal ECG. Data is collected from 12 normal subjects (7 males, 5 females; age 46 plusmn 9.38 years, AHI 3.75 plusmn 3.11) and 14 apneic subjects (8 males, 6 females; age 50.28 plusmn 9.60 years; AHI 31.21 plusmn 23.89). The proposed algorithm uses textural features extracted from normalized gray-level co-occurrence matrices (NGLCM) of images generated by short-time discrete Fourier transform (STDFT) of the HRV. Using feature selection, seventeen features extracted from 10 different NGLCMs representing four characteristically different gray-level images are used as inputs to a three-layer Multi-Layer Perceptron (MLP) classifier. After a 1000 randomized Monte-Carlo simulations, the mean training classification sensitivity, specificity and accuracy are 99.00%, 93.42%, and 96.42%, respectively. The mean testing classification sensitivity, specificity and accuracy are 94.42%, 85.40%, and 90.16%, respectively.

EOG and EMG: two important switches in automatic sleep stage classification.

Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged a well trained neural network classifier to distinguish between classes when only EEG-derived signal features were used. To meet this challenge and improve the classification rate, extra features extracted from EOG and EMG signals were fed to the classifier. In this study, two simple feature extraction algorithms were applied to EOG and EMG signals. The statistics of the results were calculated and displayed in an easy to visualize fashion to observe tendencies for each sleep stage. Inclusion of these features show a great promise to improve the classification rate towards the target rate of 100%

Time domain analysis of R-wave attenuation envelope for sleep apnea detection

Time domain analysis was carried out on the R-wave attenuation (RWA) envelope of the subjects with and without obstructive sleep apnea. The RWA envelope is derived from the morphology of the electrocardiogram (ECG) obtained during polysomnography data collection of the subjects. Nocturnal polysomnography was performed on 16 normal subjects and 14 obstructive sleep apnea (OSA) patients. The ECG from the polysomnography data was divided into fifteen minute epochs for analysis. The QRS detection was carried out by an algorithm using Hilbert transform. Standard deviation of each of the fifteen one minute epochs in fifteen minute epoch of the RWA envelope was calculated. Standard deviation of these fifteen parameters was observed to have considerably good sensitivity and specificity to sleep apnea. For the clips selected from normal subjects, the parameter produced a sensitivity of 78.57% and specificity of 70.33% for the training set and sensitivity of 87.5% and specificity of 80.95 for the testing set. For the clips selected from OSA subjects, the parameter produced a sensitivity of 72.46% and specificity of 73.53% for the training set and sensitivity of 82.86% and specificity of 66.67% for the testing set.

The use of R-wave morphology in the detection of sleep-disordered breathing using the electrocardiogram - a comparison between leads

Power spectral analysis of time series derived from the R-wave morphology of the ECG was employed to identify a suitable lead configuration for the detection of sleep-disordered breathing (SDB) using the electrocardiogram (ECG). 16 subjects (46 /spl plusmn/ 9.2 yrs, 8 males), who did not report problems during sleep, and 13 subjects previously diagnosed with SDB (49 /spl plusmn/ 8.8 yrs, 7 males) underwent an overnight sleep study at an accredited sleep center. Power values derived from the spectra of the R-peaks envelope were tested for their sensitivity and specificity in discriminating between epochs containing normal breathing from epochs containing SDB. Of the three tested lead configurations using two parameters NB1 and NB2 derived from the power spectrum, lead I produced the best results with a sensitivity of 92.8% and a specificity of 88.0% for the case of parameter NB1 and a sensitivity of 85.7% and a specificity of 76.0% for the case of parameter NB2.

Frequency domain analysis of heart rate variability in sleep disordered breathing

Frequency domain analysis of the R-R intervals was performed in subjects with and without sleep disordered breathing (SDB). Data from Physionets Apnea-ECG database as well as data collected at our sleep disorders clinic were used for this study. 15-minute ECG epochs were selected from polysomnography data. Power spectrum analysis of the resampled R-R interval series (1.2 Hz) was performed using Welchs averaged periodogram method. Normalized power in two bands, AB/sub 1/ (0.019 to 0.071 Hz) and AB/sub 2/ (0.019 to 0.036 Hz) were used to compare the impact of SDB events on the spectrum of R-R intervals. Normalized AB/sub 1/ power produced a sensitivity of 76% and a specificity of 100% in the data from the Apnea-ECG database. The same parameter produced a sensitivity of 76% and specificity of 52% in the data collected at our sleep laboratory. This difference in the results may be attributed to the severity and duration of SDB events present in the two datasets.