Open AccessSYSTEM AND ALGORITHM OF INTELLIGENT BIOMEDICAL SIGNAL PROCESSING AND ANALYSIS FOR HUMAN HEALTH STATUS REMOTE MONITORING SYSTEM
Author(s) -
Nguyen Trong Tuyen,
Tran Trong Huu,
Nguyen Mau Thach,
Z. M. Yuldashev
Publication year - 2018
Publication title -
izvestiâ vysših učebnyh zavedenij rossii. radioèlektronika
Language(s) - English
Resource type - Journals
eISSN - 2658-4794
pISSN - 1993-8985
DOI - 10.32603/1993-8985-2018-21-5-71-80
Subject(s) - wearable computer , computer science , feature (linguistics) , signal (programming language) , exacerbation , signal processing , real time computing , remote patient monitoring , artificial intelligence , algorithm , pattern recognition (psychology) , embedded system , medicine , computer hardware , digital signal processing , philosophy , linguistics , immunology , radiology , programming language
Continuous and steady running of health status remote monitoring systems is essential not to omit episodes of acute exacerbation of chronic disease. Running time of such systems is largely determined by performance capabilities of the patient's wearable system elements. To ensure its long-term operation and efficient performance, the monitoring system must have multilayered structure with the elements realizing recording and picking off biomedical signals, signal processing and analysis, estimation of patient current condition, dynamics of the disease and its prognosis. For this purpose, it is necessary to use smart monitoring algorithms. A specific feature of such algorithms is change of the number of channels used for biomedical signal recording and processing according to the change of patient’s condition. To detect the exacerbation first symptoms by means of the patient's wearable computer, additional channels are activated for recording biomedical signals used to evaluate the expanded complex of diagnostically significant parameters of the disease and their integration when specifying the patient's condition. The system and intelligent monitoring algorithm is tested with the use of heart rate remote control and atrial fibrillation episode detection system. The testing results of the developed system and algorithm are discussed.