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

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.

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.

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).

The patch-type HRV analyzer with AI analysis provides accurate, low-interference OSA screening, suitable for large-scale clinical and home use.