主催: The Institute of Systems, Control and Information Engineers
会議名: 2018 国際フレキシブル・オートメーション・シンポジウム
開催地: Kanazawa Chamber of Commerce and Industry, Kanazawa Japan
開催日: 2018/07/15 - 2018/07/19
p. 483-486
The paper presents a new approach to diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) from a singlechannel airflow record. Based on relative entropy also named KL divergence, the proposed algorithm adjusted by local range and identified the sleep-disordered breathing automatically. The development of the proposed approach was based on training sets from the overnight airflow records of 100 different patients. Through the threshold analysis, the critical value of the adjusted relative entropy of the apnea event and hypopnea event could be determined, respectively. The detection performance of the approach was tested by using records from another 12 different patients. The results suggested that the algorithm may be implemented successfully in automated diagnosis. Compared with the traditional clinical method, this approach has significantly shortened the diagnosis time in terms of efficiency and has certain significance for the clinical diagnosis in the field of sleep.
