SUMMARYThere have been many trials in which the waveform recognition method, which is intended to replace expert observation by computer processing, has been used to extract the features of biological signals during sleep, and to automatically score sleep stages based on feature parameters. This paper proposes a waveform recognition method which extracts the feature parameters based on the characteristics of the biological signal during sleep, and a method of automatic sleep stage scoring by decision-tree learning, which is currently considered to be one of the most successful machine learning methods in practice. As the first step in the method, the features corresponding to the state of the EEG, the EOG, and the EMG during sleep are compared to the features of characteristic waves such as the α-wave, δ-wave, sleep spindle, K-complex, and REM, and the feature parameters needed in order to judge the sleep stage are extracted. Using canonical discriminant analysis and discretization method RWS based on the random walk, the feature parameters are converted to a small number of discrete variables. Based on training instances, obtained by the bootstrap method, a set of multiple small decision trees (a committee) is formed, and the sleep stage is scored by majority decision in the classification results. The method is applied to the PSG chart digital data provided by the Japan Sleep Society, and the performance of the system is evaluated experimentally. It is verified that the proposed method is promising as a method of automatic sleep stage scoring with high accuracy, requiring little expenditure of time in learning and classification.
SUMMARYMAM (Multidirectional Associative Memory) is an extended BAM (Bidirectional Associative Memory), and an associative memory model which can deal with multiple associations. If the training set has common terms, the conventional MAM often recalls the convolutional patterns. IMAM (Improved Multidirectional Associative Memory) can store them, but the structure is complex and the storage capacity is extremely small because it must use correlation matrix. In this paper, we propose a MAM with a hidden layer and its learning method. The structure is as simple as MAM and can store the training set which includes common terms. By computer simulation, we show the storage capacity is far larger than correlation learning and it is robust against noise.
Hopfield model is a representative associative memory. It was improved to Bidirectional Associative Memory(BAM) by Kosko and Multidirectional Associative Memory(MAM) by Hagiwara. They have two layers or multilayers. Since they have symmetric connections between layers, they ensure to converge. MAM can deal with multiples of many patterns, such as (x 1 , x 2 , · · · ), where x m is the pattern on layer-m.Noest, Hirose and Nemoto proposed complex-valued Hopfield model. Lee proposed complex-valued Bidirectional Associative Memory. Zemel proved the rotation invariance of complex-valued Hopfield model. It means that the rotated pattern also stored.In this paper, the complex-valued Multidirectional Associative Memory is proposed. The rotation invariance is also proved. Moreover it is shown by computer simulation that the differences of angles of given patterns are automatically reduced.At first we define complex-valued Multidirectional Associative Memory. Then we define the energy function of network. By using energy function, we prove that the network ensures to converge.Next, we define the learning law and show the characteristic of recall process. The characteristic means that the differences of angles of given patterns are automatically reduced. Especially we prove the following theorem. In case that only a multiple of patterns is stored, if patterns with different angles are given to each layer, the differences are automatically reduced.Finally, we invest that the differences of angles influence the noise robustness. It reduce the noise robustness, because input to each layer become small. We show that by computer simulations.
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