Detecting and differentiating central and obstructive respiratory events is an important aspect of the diagnosis of sleep-related breathing disorders with respect to the choice of an appropriate treatment. The purpose of this study was to evaluate the performance of a new algorithm for automated detection and classification of apneas and hypopneas, compared with visual analysis of standard polysomnographic signals. The algorithm is based on time series analysis of nasal mask pressure and a forced oscillation signal related to mechanical respiratory input impedance, measured at a frequency of 20 Hz throughout the night. The method was applied to all-night measurements on 19 subjects. Two experts in sleep medicine independently scored the corresponding simultaneously recorded polysomnographic signals. Evaluating the agreement between two scorers by a weighted kappa statistic on a second-by-second basis, we found that inter-expert variability and the discrepancy between automatic analysis and visual analysis performed by an expert were not significantly different. Implementation of this algorithm in a device for home monitoring of breathing during sleep might aid in the differential diagnosis of sleep-related breathing disorders and/or as a means for follow-up and treatment control.
Repetitive occurrence of partial or total upper airway obstruction characterizes several respiratory dysfunctions such as the obstructive sleep apnea syndrome (OSAS). In OSAS patients, pharyngeal collapses are linked to a decrease in upper airway muscle activity during sleep which causes decreased upper airway wall stiffness. Continuous positive airway pressure (CPAP) is recommended as the treatment of choice. Advancements in CPAP therapy require early detection of respiratory events in real time to adapt the level of the applied pressure to airway collapsibility. The forced oscillation technique (FOT) is a noninvasive method which reflects patients' airway patency by measuring respiratory impedance. The aim of this study was to evaluate by a mathematical model of the respiratory system if FOT can provide an early detection index of total or partial upper airway obstruction. Furthermore, the simulation should suggest which characteristic features are relevant for early apnea detection in measured clinical data. The respiratory system has been treated as a series of cylindrical segments. The oropharynx analog of the model allows simulation of upper airway collapse, mimicking the situation in patients with OSAS. We calculated the input impedance for different degrees of upper airway obstruction ranging from unobstructed airways to total occlusion. Furthermore, we simulated different upper airway wall compliances. We compared the simulation with real data. The results of the study suggest that FOT is a valuable tool for assessing the degree of upper airway obstruction in patients with OSAS. Especially, the phase angle of the impedance seems to be a potentially useful tool for early apnea detection by assessing the upper airway wall collapsibility.
Background: The forced oscillation technique (FOT) allows analysis of the upper airway impedance and, hence, detection of obstructive sleep apnea. Objective: To evaluate FOT with respect to sensitivity and to specificity in online detection of sleep-disordered breathing patterns and to compare algorithmic onset detection time with manual onset time markers of staff physicians. Methods: We compared the absolute value ∣Z∣ of the impedance with three routinely obtained polysomnographic signals – nasal airflow v̇nasal, thoracic excursion Thox and esophageal pressure Pes – by retrospective analysis of the diagnostic polysomnograms of 51 patients. For each signal we evaluated algorithms for online detection of respiratory events. For each out of five apnea classes, 50 respiratory events marked by staff physicians were drawn randomly from the 51 polysomnograms to optimize the online detection algorithms (learning set). The algorithm analyzes relative changes of signal baseline and amplitude. Again 50 respiratory events were drawn randomly for each apnea class to examine to what extent it is possible to detect event onsets with the algorithms (test set). Results: The sensitivity of the signals varied between 56 and 94% and was on average 74%. The specificity was 96 ± 1.5% on average. The onset was detected 4–6 s after the initially evaluated onset of the staff physicians. Conclusion: We conclude that nasal airflow and FOT are equivalent sensitive measurands for detection of respiratory events.
Patients with pulmonary disease are often unable to complete forced expiration manoeuvres. The aim of the study is to evaluate whether forced vital capacity (FVC), the volume exhaled at the end of completed forced expiration, can be estimated by extrapolating volume-time curves obtained from uncompleted manoeuvres. The suitability of mono-, bi-, and tri-exponential functions to characterise complete volume-time curves from 50 subjects is investigated. Mono-exponential modelling is insufficient, whereas bi-exponential fitting yields an adequate description for 47 data sets. Tri-exponential models lead to overfitting in all but three cases (normalised sum of least squares: 50.2 +/- 34.5 for mono-, 2.76 +/- 4.11 for bi-, 2.74 +/- 4.19 for tri-exponential modelling; condition number of the correlation matrix: 1.0025 +/- 0.0004 for mono-, 1.08 +/- 0.08 for bi-, 34.7 +/- 100.1 for tri-exponential fitting (mean +/- SD)). Thus, FVC is estimated by the extrapolation of 27 uncompleted spirograms using bi- or tri-exponential models, depending on their accordance with measured data and on the identifiability of their parameters. This algorithm yields unbiased estimates (difference from measured inspiratory vital capacity: 0.01 +/- 0.21 L). This method can be used for investigation of the lung function of subjects who cannot complete the forced expiration manoeuvre.
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