TY - GEN
T1 - OPEN-SOURCE ACCELEROMETER-BASED DEVICE AND DATA ANALYSIS FOR PRECISION MONITORING OF SLEEP APNEA EVENTS
AU - Khan, Faizaan
AU - Rashidi, Keyvon
AU - Dongre, Roshan
AU - Razmi, Samuel E.
AU - Shenoi, Jason
AU - Ahmed, Omar G.
AU - Takashima, Masayoshi
N1 - Publisher Copyright:
© 2024 by ASME.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Sleep apnea
KW - Sleep apnea monitoring
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U2 - 10.1115/DMD2024-1082
DO - 10.1115/DMD2024-1082
M3 - Conference contribution
AN - SCOPUS:85205974251
T3 - Proceedings of the 2024 Design of Medical Devices Conference, DMD 2024
BT - Proceedings of the 2024 Design of Medical Devices Conference, DMD 2024
PB - American Society of Mechanical Engineers (ASME)
T2 - 2024 Design of Medical Devices Conference, DMD 2024
Y2 - 8 April 2024 through 10 April 2024
ER -