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Improvement implementation a software to analysis polysomnography signal

This study was conducted with the goal of building a complete software functional classification and microscopic structure of sleep. The software is designed with friendly userinterface and useful for doctors to use in examination and clinical treatment. | TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 18, SOÁ K4- 2015 Improvement implementation a software to analysis polysomnography signal Le Quoc Khai Nguyen Thi Minh Huong Nguyen Vu Quang Hien Nguyen Le Trung Hieu Huynh Quang Linh Ho Chi Minh city University of Technology, VNU-HCM (Manuscript Received on August 01st, 2015, Manuscript Revised August 27th, 2015) 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. Key words: polysomnography analysis, learning machine, AASM standards 1. INTRO DUCTIO N Sleep is one of the most popular activities that people spend a third of life time. Insomnias or sleep disorders are often the cause of many other diseases. Besides sleep has a special role in clinical neurological studies. In 1929, Berger was the first scientist, who recognized the human brain electrical activity during sleep by recording the difference between the waking and relax states during