Automated sleep apnea detection from snoring and carotid pulse signals using an innovative neck wearable piezoelectric sensor

• A wearable neck piezoelectric sensor (NPS) is designed to detect snoring & pulse. • Advanced algorithms for snoring event detection and correction boost accuracy. • NPS outperforms polysomnography and snoring sound analysis in snoring metrics. • Carotid pulsation SDNN algorithm achieves 85% sleep phase accuracy. • Categorical regressions yield up to 90% accuracy in sleep apnea syndrome severity. This study introduces an innovative wearable neck piezoelectric sensor (NPS) that measures snoring vibrations and carotid pulsations, offering a significant advancement in sleep apnea syndrome (SAS) diagnosis. Utilizing advanced algorithms like discrete wavelet transform and dynamic thresholding, the NPS detects snoring events with 83% accuracy, comparable to polysomnography, and calculates key metrics such as the snoring index (SI) and normalized snoring vibration energy (SVE%). Unlike traditional methods, the SVE% from NPS directly correlates with subjective assessments of snoring severity. It also measures carotid pulsation metrics such as pulse rate and the standard deviation of normal-to-normal intervals, achieving 85% accuracy in sleep phase determination against polysomnography. Moreover, NPS surpasses traditional methods in SI and SVE% accuracy, closely aligning with clinical evaluations of SAS severity. This user-friendly technology automates the measurement of critical snoring metrics, transforming SAS diagnosis and treatment by enhancing accessibility and efficiency for healthcare providers and patients.