A MEMS Pressure Sensor Array for Sleep Apnea Recognition and Monitoring Based on The Principle of Traditional Chinese Medicine

This paper reports a novel MEMS-based pressure sensor array, inspired by traditional Chinese medicine, for detecting and identifying pulse signals. A multi-dimensional pulse acquisition array was developed by emulating traditional Chinese pulse diagnosis techniques to capture pulse signals at the cun, guan, and chi positions along the radial artery. A database was created with pulse signals recorded under normal breathing and breath-holding conditions. These features were used to train a support vector machine (SVM) classification model. The experimental results show that the model achieved high accuracy (99.33%) and sensitivity (98%) in detecting sleep apnea. Overall, this system could offer innovative technical support and promising applications for modernizing traditional Chinese diagnostic methods and developing smart healthcare devices.