Sleep Pattern Characterization via Cluster Analysis of Audio Data

Abstract

In our research, a new method for sleep pattern characterization based on cluster analysis is proposed. After recording audio data during sleep, we extracted audio clips of events from the audio data and applied various types of Self-Organizing Map (SOM) algorithms on these data, including general SOM, Kullback-Leibler Kernel SOM, or Sequence-based Kernel SOM to be compared, and obtained clusters of sleep related events for a whole night. The sleep related events include snore, bruxism, limb movement, etc. Visualization of events distribution and transition aid in characterization of individual sleep pattern and comprehension of sleep quality evaluation.