Nobuo Suzumura

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In this paper, we show techniques to examine the stationarity and the normality of time series as well as results obtained by applying these techniques to EEG data during sleep stages. Many statistical analyses of the EEG data are based on the assumption that the EEG data are stationary and normally distributed. However, the problem is to know the length of(More)
Conventional X-ray tomosynthesis with film can provide a sagittal slice image with a single scan. This technique has the advantage of enabling reconstruction of a sagittal slice which is difficult to obtain from the X-ray CT system. However, only an image on the focal plane is obtained by a single scan. Furthermore, the image is degraded by superimpositions(More)
A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The(More)
Biomedical data, such as EEG, EMG and neural impulse sequences, are regarded as the stochastic phenomena of biological systems, and the statistical properties of such time series are often examined. Most of the statistical analysis processed in the frequency and the time domain are based on the assumption that the time series is weakly stationary and(More)
A data compression algorithm for digital Holter recording using artificial neural networks (ANN) is proposed. A dual three-layer (one hidden layer) neural network which has a few units of hidden layer is used to extract the differences of waveforms as the activation levels of hidden layer units. The network is tuned using supervised signals, which are the(More)