[2K6-OS-1b] 医療におけるAIの社会実装に向けて(2/2)

[2K6-OS-1b-03] Feature Extraction based on Comparison of Learned Random Forests Model of Sleep Apnea Syndrome Patients with Healthy Subjects and Its Interpretable SAS Detection

〇Iko Nakari¹, Keiki Takadama¹ (1. The University of Electro-Communications)

Keywords:Random Forests, Health care, Interpretability, Sleep Apnea Syndrome, Sleep

This paper proposes the novel Sleep Apnea Syndrome (SAS) detection method based on the comparison of Random Forests (RF). Concretely, the method compares two RFs between SAS and non-SAS subjects to discover the physiological characteristics and detects SAS according to the difference of the RFs. The method employs the bio-vibration data acquired by the mattress sensor during sleep as the input of RF and the WAKE (shallow sleep) or non-WAKE as the output of RF for learning characteristics of WAKE. Through the human experiment with nine SAS and nine non-SAS subjects, the following implications have been revealed: (1) the accuracy of SAS detection with the proposed method is 88.9% and (2) a comparison of RFs between SAS and non-SAS subjects discovers that it is difficult to estimate WAKE for SAS based solely on the magnitude of body movement, whereas it is easy to estimate WAKE based on that for non-SAS.