The use of home sleep apnea testing (HSAT) has been increasingly recognized as a practical and economical alternative to in-laboratory polysomnography (PSG) for diagnosing obstructive sleep apnea (OSA), especially in underserved settings. Challenges associated with PSG—such as high cost, limited accessibility, and patient discomfort—have driven interest in simplified, home-based approaches. Particular attention has been given to electrocardiogram (ECG)-based HSAT, which utilizes heart rate variability and advanced computational algorithms, including machine learning and deep learning, to detect apneic events, distinguish sleep stages, and identify cardiovascular comorbidities such as atrial fibrillation. Although current HSAT devices show promise, limitations in diagnostic accuracy, ease of use, and standardization remain. Recent innovations in algorithmic design, such as transformer-based and object detection models, have enhanced detection capabilities and processing efficiency. Future progress is anticipated through the integration of multimodal physiological signals, improvements in wearable technology, and validation via multicenter clinical studies, ultimately advancing HSAT’s role in individualized OSA diagnosis and management.