뇌파에 기반한 수면케어 서비스에서 수면유도음향의 분류기법

Abstract

Sounds that have been evaluated to be effective in inducing sleep are helpful to reduce sleep disorders. Generally, several sounds have been verified the effects by brainwave experiments, but it cannot be considered on all users because of individual variation for effects. Moreover, the effectiveness for inducing sleep is not known for all new sounds made by creative activities. Therefore, new classification system is required to collect new effective sounds with considering personal brainwave characteristics. In this paper, we propose a new sound classification method by applying improved MinHash cluster to brain waves. The proposed method will classify them through whether it is effective for sleep care by evaluation his brainwave during listening for each sound. In order to prove effectiveness of the proposed classification method, we conducted accuracy experiment for sleep sound classification using verified sleep induction sound. In addition, we have compared time for existing method and proposed method. The former is scored 85% accuracy in the experiment. We confirmed the latter one that the average processing time was reduced to 70%. It is expected to be one of method for pre-screening whether it is effective when a new sound is introduced as a sound for sleep induction.