Classification NREM in EEG Signal for Detection Depth of Sleep Using HJORTH Descripto


  • Naufal Rizky Pratama Telkom University
  • Raditiana Patmasari Telkom University
  • Sugondo Hadiyoso Telkom University


Abstract— Sleep is one of rest which is marked by decrease in physical activity, awareness, and decrease in respond for external stimulation. Sleep is important in returning body energy, maintain body resilience even cognitive function in the body. There is two kind of sleep that is Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM) that must be experienced by someone for reach depth of sleep. If one of that not achieved then it can disturbance in the body. Detection of NREM and REM can be done with analyze EEG signal. In this research has been successfully classify NREM and REM wave based on EEG signal for delta wave. A time series method analysis that is HJORTH Descriptor used to get signal feature of that. HJORTH activity, mobility, and complexity showing many value for each category. From simulation has been reach 70,9% accuracy using Fine Gaussian Support Vector Machine.
Keywords—NREM, REM, HJORTH Descriptor, SVM