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Abstract Background: Sleep apnea, encompassing obstructive, central, and complex forms, significantly impacts global health, affecting a broad segment of the population. This condition can lead to serious cardiovascular and neurological damage due to recurrent hypoxia. Despite its prevalence, many individuals remain undiagnosed, partly due to the absence of accessible and accurate screening tools. Objective: To record and quantify sleep events in patients with an accelerometer that will be placed on the sternal notch. Method: A Python algorithm was developed to collect data from an open-source biosensor which was displaced 6 consecutive times across 20 trials along the y-axis to mimic movement along the anteroposterior axis. This algorithm then processed the data and displayed the results in a user interface, allowing for simple determination of OSA events with timestamps for reference along with plotting. Results: The system demonstrated 100% accuracy, consistently identifying all six disruptions per trial with no false detections despite the variability in displacements. Conclusion: This study validates the potential of an advanced monitoring system in diagnosing and understanding sleep apnea, proposing a promising avenue for improving patient care through precise detection and analysis.
Khan et al. (Wed,) studied this question.