Controlling and Detection of Sleep Apnea | IEEE Conference Publication | IEEE Xplore

Controlling and Detection of Sleep Apnea


Abstract:

Diagnosing sleep apnea, especially obstructive sleep apnea (OSA), can be difficult. Because ECG analysis can pick up on small changes, it's becoming a useful tool for det...Show More

Abstract:

Diagnosing sleep apnea, especially obstructive sleep apnea (OSA), can be difficult. Because ECG analysis can pick up on small changes, it's becoming a useful tool for detection. The complexity of traditional detection techniques like polysomnography (PSG) has led to the investigation of substitutes. Clinical data is used by machine learning (ML) to predict the severity of OSA, providing effective diagnostic possibilities. A predictive model that used Random Forest classifier and included 11 medical parameters produced a high degree of accuracy. A user interface also makes OSA classification easier. Although CPAP (Continuous positive airway pressure) devices are an effective treatment for OSA, cost remains a concern. The goal of an Arduino-based substitute is to close this accessibility gap.
Date of Conference: 21-23 June 2024
Date Added to IEEE Xplore: 03 September 2024
ISBN Information:
Conference Location: Prayagraj, India

References

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