ESTIMATION OF HEART RATE USING SIGNAL FUSION OF ECG AND BP SIGNALS

BhargaviB.G. S, DeviNallammai RM, PalaniThanaraj. K.
2017 International Journal of Advanced Research  
Polysomnography is a sleep study. This test records certain body functions as we sleep, or try to sleep. Polysomnography is used to diagnose sleep disorders. A change in the cycle may make it hard for a person to sleep soundly. Multiple sleep latency test (MSLT) test measures how long it takes for us to fall asleep. It also determines whether you enter REM sleep [1] . Maintenance of wakefulness test (MWT) measures whether we can stay awake during a time when we are normally awake. If shift work
more » ... wake. If shift work sleep disorder or another problem with our body's internal clock (circadian rhythm) is suspected, then the test performed is called actigraphy. Among the three main tests suggested above, polysomnography is perhaps the most elaborate and time consuming, due to the sheer amount of data collected and stored .But this is also the most valuable and reliable test in terms of the accuracy of sleep information obtained, provided they can be analyzed and interpreted in the clinically correct manner. Signal fusion involves the consolidation of all the various signals obtained in the polysomnogram in such a constructive manner so that it can be used constructively for diagnosis of sleep disorders. Signal processing is an enabling technology that encompasses the fundamental theory, applications, algorithms, and implementations of processing or transferring information contained in many different physical, symbolic, or abstract formats broadly designated as signals. It uses mathematical, statistical, computational representations and techniques for representation, modeling, analysis, synthesis, discovery, recovery, sensing, acquisition, extraction, learning, security, or forensics. Dynamical mathematical model in this context is a mathematical description of the dynamic behavior of a system or process in either the time or frequency domain
doi:10.21474/ijar01/3631 fatcat:aekvsse4bbfkncf6kojm6j3sl4