Abstract: A Cheyne-Stokes (CS) diagnosis system classifies periods of CS-like breathing by examining a signal indicative of a respiratory parameter. For example, nasal flow data is processed to classify it as unambiguously CS breathing or nearly so and to display the classification Processing may detect and display: apnoeas, hypopnoeas, flow-limitation and snore. The signal may be split into equal length epochs and event features are extracted. Statistics are applied to these primary feature(s) to produce secondary feature(s) representing the entire epoch. Each secondary feature is grouped with other feature(s) extracted from the entire epoch rather than from the epoch events. This final group of features is the epoch pattern. The epoch pattern is classified to produce a probability for possible event classes (e.g., Cheyne-Stokes breathing, OSA, etc.). The epoch is assigned to the class with the highest probability, which may both be reported as an indication of disease state.

Abstract: A signal representative of a patient's respiration is split into equal length epochs. A primary feature is extracted from each epoch that acts as a compressed representation of the signal events. Statistics are applied to the primary feature to produce one or more secondary features that represent the entire epoch. Each secondary feature is grouped with one or more other features that are extracted from the entire epoch rather than selected epoch events. This grouping is the epoch pattern. The pattern is manipulated with suitable classifier algorithm to produce a probability for each class within the algorithm, that the signal may be representative of a disease state (Cheyne-Stokes, OSA etc). The epoch is assigned to the class with the highest probability. Also defined are methods of detecting Cheyne-Stokes breathing by analyzing a signal to detect one or regions of hyperpnoea and if the length of a hyperpnoea exceeds a parameter, Cheyne-Stokes is present.

Abstract: A signal representative of a patient's respiration is split into equal length epochs. A primary feature is extracted from each epoch that acts as a compressed representation of the signal events. Statistics are applied to the primary feature to produce one or more secondary features that represent the entire epoch. Each secondary feature is grouped with one or more other features that are extracted from the entire epoch rather than selected epoch events. This grouping is the epoch pattern. The pattern is manipulated with suitable classifier algorithm to produce a probability for each class within the algorithm, that the signal may be representative of a disease state (Cheyne-Stokes, OSA etc). The epoch is assigned to the class with the highest probability. Also defined are methods of detecting Cheyne-Stokes breathing by analysing a signal to detect one or regions of hyperpnoea and if the length of a hyperpnoea exceeds a parameter, Cheyne-Stokes is present.

Abstract: A headgear assembly for attachment to a patient interface that delivers breathable gas to a patient, includes a pair of side portions and a rear portion that interconnects the pair of side portions, each of the side portions including an upper side strap and a lower side strap, the rear portion including an upper strap, a lower strap, and intermediate connecting straps extending between the upper strap and the lower strap. The headgear assembly is designed and configured so as to be used with a plurality of different mask systems, and to accommodate for variations in the anthropometrics across a wide variety of patients.