Sustainable Diagnosis of Sleep Apnea Using ECG and Deep Learning

Sleep apnea is one of the commonly occurring sleep disorders whose delayed treatment leads to a whole host of critical health problems, and it usually diagnosed with the help of PSG.PSG is both costly and market intruding and resource requiring. The single-signal ECG proposed here, when coupled with deep learning, provides a new sustainable paradigm for detection of sleep apnea that is cheaper and more environmentally friendly than PSG. High performance 90% accuracy the proposed method employs CNN based with LSTM architectures to extract features from ECG data This idea enables scalable and low-cost health solutions, reducing the burden of costly and resource-draining diagnostic testing, thus ensuring implementation of Sustainable Development Goal—Good Health and Well-Being. Based on our analysis, this move towards ECG-based sleep apnea diagnosis will aid in personalized treatment for the patient and broader reach for diagnostic measures that allow early intervention while solidifying sustainable medical practices.