Sleep apnea and other sleeping disorders impair health and quality of life. Polysomnography is the primary method for diagnosis, but involves cost and utilization of medical resources, which limit access for potential patients. The clinical environment and sensors of polysomnography hinder typical sleep patterns in many individuals, thus degrading the analysis. Sensors suitable for at-home monitoring of sleep have recently become available. At-home monitoring of sleep may improve diagnosis due to increased familiarity for sleeping and ability for multiple sleep sessions, as well as lowering the cost. However, more robust algorithms would be needed to partially compensate for the less controlled conditions and sensor systems. A mat with a grid of force sensors has become available. This study was developing a state machine algorithm to analyze the activity at multiple force sensors of a mat while the subject was lying in supine position on the mat and undertaking natural, rhythmic respiration. The algorithm monitored the subset of active sensors to detect potential respiratory cycles. The similarity of the timing of the detected cycles between different sensors was used to determine the overall pattern of respiratory activity for the subject. Reliable detection of timing for respiratory cycles would be useful for detection of sleep apnea events.

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