Automatic detection of sleep spindles with quadratic discriminant analysis

Sleep, is in the event of temporary loss of consciousness. Sleep and wakefulness causes some different kind of potential changes in brain. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The main objective of this study is to develop a method to detect sleep spindle, which is one of these structures, with high-accuracy. Sleep spindles that require the expertise to determine visually, is a process that can be time-consuming and subjective results. In this study, electroencephalography records, scored by expert sleep physicians, were analyzed by different methods. Two features have been determined that express the presence of sleep spindle. Sleep spindles were detected by these features and quadratic discriminant analysis. As a result, the performance of the algorithm was evaluated and sensitivity, specificity and accuracy were determined as 95.74%, 98.08% and 97.76%, respectively.