Controlling and Detection of Sleep Apnea

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.