Patch-Type Heart Rate Variability Analysis with Artificial Intelligence for Detection of Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) affects millions in Taiwan, but common screening tools, such as oximeters and ApneaLink ® , may reduce sleep quality and have limited accuracy. Methods: We enrolled 277 adults with self- or family-observed snoring. All underwent home sleep apnea testing (HSAT) via ApneaLink ® and simultaneous overnight monitoring with a patch-type heart rate variability (HRV) analyzer. After strict data quality control, 86 subjects remained. HRV indices from ECG signals were processed using time-, frequency-, and nonlinear-domain analyses. An artificial intelligence (AI) model, incorporating a novel Cardiovascular Hypopnea Index (CVHI), was developed using leave-one-out validation. Results: The AI model achieved 81.4% accuracy, outperforming demographic-based (73%) and previous ECG-based (70.6%) screening. At an apnea–hypopnea index (AHI) cutoff of 15, it showed strong classification for moderate-to-severe OSA (AUC > 0.8). Conclusion: The patch-type HRV analyzer with AI analysis provides accurate, low-interference OSA screening, suitable for large-scale clinical and home use. Keywords: home sleep testing, autonomic nervous system function, patch type heart rate analyzer