BP ニューラル ネットワーク オ モチイタ サーカディアン リズムゲン ノ システム ドウテイ

Almost all land animals coordinate their behavior with circadian rhythms, matching their functions to the daily cycles of lightness and darkness that result from the rotation of the earth corresponding to 24 hours. Through external stimuli, such as dairy life activities or other sources from our environment may influence the internal rhythmicity of sleep and waking properties. However, the rhythms are regulated to keep their activity constant by homeostasis while fluctuating by incessant influences of external forces. A modeling study has been developed to identify homeostatic dynamics properties underlying a circadian rhythm activity of sleep and wake data measured from normal subjects, using an MA (Moving Average) model associated with backpropagation (BP) algorithm. As a result, we found out that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subject’s behavior. The last two data are usually dominant in the case without strong external forces. The adaptive changes of the dynamics are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamics is kept in a steady state for more than 20 days. Identified properties reflect the subject’s behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations

Application of BP neural networks to transition detection in time series models

Biological signals are used in medical field to assess and track the functional states of vital organs such as the brain. The complexity (eg. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in time critical applications. In this paper we propose a new neural network based technique to address those problems. We show mat a feedforward, multilayered neural network can conveniently capture the states of a nonlinear systems in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated with some systems simulated via a mathematical model, and the system generating real world Electroencephalogram (EEG) signals

Neural networks for snore sounds modeling in sleep apnea

Snoring is the earliest and the most common symptom of Obstructive Sleep Apnea (OSA) which is a serious disease caused by the collapse of upper airways during sleep. Recently, a few pioneering attempts have been made to use snore sounds (SS) is diagnosing OSA. The SS are simple to acquire and rich in features but their analysis is complicated. In this paper, we propose a neural network (NN) based method to model SS via a technique associated with k-step prediction. We also show that the features of a SS can be conveniently captured in the connection-weight-space (CWS) of the NN, after a process of supervised training. The performance of the proposed method is investigated via simulated and clinically measured data

Order estimation and screening of apneic snore sound using the Akaike Information Criterion

Obstructive Sleep Apnea (OSA) is well known as the serious sleep disorder, which is caused by the obstruction of airway during sleep. Polysomnography (PSG) is the current gold standard for clinical diagnosis of OSA. It requires a full-night hospital stay connected to over ten channels of measurements requiring physical contact with sensors. PSG is inconvenient, expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. Snoring is signals generated from biological system, which are allowed to be nonlinear and/or non-stationary. The linear technique is well known/used in the research community and its behavior has been extensively documented. In this paper, we forecast the time series by using the auto-regressive (AR) model theoretically known well by treating the snore sounds and capturing the state of a linear system. We measure the snore sound by using the microphone. Akaike Information Criterion (AIC) is used to know the order of the snore sound. It is one of the techniques to select the best model order. Working on snore sound data, we illustrate that snore sound is conveniently modeled by the proposed method. The parameter obtained based on snoring analysis indicates that snore sound have rich information in features for OSA screening. Moreover, whether we were able to reduce the number of data by changing the sampling rate was examined

Localization of an inert region in the brain using modified Levenberg Marquarts neural network

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. © 2007 IEEE