If we can estimate the transition of biomedical system from biomedical signals, it can be very useful in increasing the safety of medical treatment. And so, The examination was performed aiming at estimation of the transition and parameter change of some parametric models such as time-variant linear model and time-variant nonlinear model. In this study, we devised an evaluation index using BP Neural Networks as the technique to detect the transition and the parameter change from the measured data. As results of computer simulations, the availability of system identification of the proposed method was confirmed.

Detection of Transition of Various Time Series Model Using BP Neural Networks

A novel application of artificial neural networks (NNs) to modeling biomedical systems from the time series data that the systems generate is proposed. The systems are allowed to be nonlinear and/or nonstationary. The method consists of training a NN by error backpropagation algorithm for the prediction of a biomedical time series signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. A novel feature, “normalized vector separation” (NVS) is also proposed to measure the separation of two arbitrary states in the CWS and use it to track the state changes of the system. A new method to estimate the order of the system from the time series data including noise by calculating degree of randomness is also proposed. The performance of the methods is examined with synthetic signals, and then successfully applied to sleep-wake rhythm and clinical EEG. Analysis of synthetic data indicates that the NVS can track the system down to a signal-to-noise ratio of 3.5dB. Sleep-wake hour data for three months from normal subjects were analyzed. The underlying system was modeled by NNs and dynamic changes of the model were captured. Secondly, clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled with a small root-means-squared-error by the proposed method, and the blood perfusion state of the brain could be monitored via NVS. Correspondence to:

Analysis of time series of biomedical data by artificial neural networks analiza vremenskih serija biomedicinskih podataka ve(ta^kim neuronskim mre@ama

A deviation detection method of biological systems using neural networks is described. This simple method is to analyze time series signals using moving-average-type neural networks. The result of this analysis indicates the transition of the system as simple values. Purpose of this study is to investigate fundamental properties of this method using simple mathematical models which simulate biological systems. As results of computer simulations, interesting characteristics were shown, and its usefulness was confirmed.

Detection of biological system deviation by BPNN

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 was found that dynamics of regular components can be captured by the model. The captured dynamics are kept in a steady state for some periods. The order of the MA neural network was suppressively controlled by the first 2∼3 orders. Hence it may be useful for medical diagnoses of circadian rhythms and heart related diseases.

Identification of biological signal sources for circadian and cardiac cycle rhythms using BP neural networks

We tested the localization accuracy of electroencephalograph (EEG) for an inert region in a simulation at sizes ranging from 1 to 8 cm at 1 cm intervals. We used international 10-20 system electrodes placements and three concentric shell model to calculate forward problems. From using the data, neural network could be used to solve inverse problems. In this case, we estimate the localization of inert region. To demonstrate the effectiveness of the method, we perform simulations on location of inert region from EEG data, consists of training and test data. Based on the results of extensive studies, we conclude that neural network are high feasible as localization of inert region. These EEG estimation tasks were created by using a set of calculated, artificial EEG signals based on a number of current dipoles. The experimental results indicate that the proposed method has several attractive features. 1) The size of inert region is becoming more large and more the RMS values low. 2) The following the distance is closer, the RMS values is low. That could be considered inert region exists near by the electrode which has low RMS potential. 3) The more larger inert region were, the more small estimation error become.

Localization of an Inert Region in the Brain using Modified Levenberg Marquarts Neural Network

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Identification of biological signal sources for circadian and cardiac cycle rhythms using BP neural networks

Localization of an Inert Region in the Brain using Modified Levenberg Marquarts Neural Network