Identification of biological signal sources for circadian and cardiac cycle rhythms using BP neural networks
Biological oscillatory activity in neural networks has been intensively studied over the past years. Neuronal oscillations are the basis of many different behavioral patterns and sensory mechanism. Understanding the dynamic properties of these mechanisms is useful for analyses of biological functions and medical diagnoses. The dynamic characteristics of wake-sleep circadian rhythms and ECG's cardiac cycle data measured for normal subjects are identified here, using MA-BP neural network model It teas found that dynamics of regular components can be captured by the model. The captured dynamics ave kept in a steady state for some periods. The order of the MA. neural network was suppressively controlled by the first 2 similar to3 orders. Hence it may be useful for medical diagnoses of circadian rhythms and heart related diseases.
identification neural networks medical cybernetics