Abstract

Sleep disorders have become nowadays one of the most important health issues in the community; they will affect many functions of the body and regular physical activities. The goal of our research is implementation improvement of the software for polysomnography signal analysis based on AASM standards published in 2014 to create a comprehensive assessment method for different abnormalities or pathologic symptoms. By using a combination of different learning machine algorithms, program can flexibly update threshold and characteristics of polysomnography signal for each people and reduce errors in calculated results. The program is designed with friendly user interface without support of other special software. The results checked by comparative measurements with other facilities showed high reliability, which give the similarity over 83% for all data. The most advantage of the software is the ability to synchronize data and analysis results with other systems. Program can be decomposed in block modules, which can be easily integrated with other equipment to make independent and continuous diagnostic systems.