Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942187 | Artificial Intelligence in Medicine | 2017 | 10 Pages |
Abstract
We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hongle Wu, Takafumi Kato, Tomomi Yamada, Masayuki Numao, Ken-ichi Fukui,