Sleep Apnea Syndrome Detection based on Biological Vibration Data from Mattress Sensor

This paper proposes the new Sleep Apnea Syndrome (SAS) detection method based on Random Forest (RF) by estimating WAKE stage (shallow sleep) and analyzing characteristics of biological vibration data at WAKE stage. In particular, the proposed method estimates the WAKE stage from the biological vibration data acquired by the mattress sensor, and detects SAS based on the differences in the distribution of contribution of each frequency to classify the WAKE stage. To investigate the effectiveness of the proposed method, in cooperation with medical institutions, we applied the proposed method to a total of 18 subjects (nine SAS patients and nine healthy subjects). The results derive the following implications: (1) SAS patients have WAKE with small biological vibrations, and the contribution of the corresponding low frequency components is high while the high frequency components, which is corresponded to large biological vibrations, is low contribution; (2) the proposed method could correctly detect SAS with 100% accuracy and non-SAS with 77.8% accuracy.