Detection of characteristic waves of sleep EEG by neural network analysis

Abstract

Abstract-In psychiatry, the sleep stage is one of the most important evidence for diagnosing mental disease. However, doctors require much labor and skill for diagnosis, so a quantitative and objective method is required for more accurate diagnosis since it depends on the doctor's experience. For this reason, an automatic diagnosis system must be developed. In this paper, we propose a new type of neural network (NN) model referred to as a sleep electroencephalogram (EEG) recognition neural network (SRNN) which enables us to detect several kinds of important characteristic waves in sleep EEG which are necessary for diagnosing sleep stages. Experimental results indicate that the proposed NN model was much more capable than other conventional methods for detecting characteristic waves.Index Terms-Charachteristic wave, EEG, neural network, sleep stage. I. ITRODUCTION IN PSYCHIATRY, sleep staging is one of the most important means for diagnosis. The sleep staging of electroencephalogram (EEG), however, is liable to be subjective since it depends on the doctor's skill and requires much labor. An automatic diagnosis system must, therefore, be developed to reduce doctor's labor and realize quantitative diagnosis of sleep EEG.For sleep staging by EEG analysis, it is especially important to detect the characteristic waves from EEG. Most conventional methods of diagnosing the sleep stage, however, use long-term spectrum analysis [5], [6]. Such analysis is unable to detect transient and isolated characteristic waves such a hump wave from sleep EEG accurately. As a result, it is not possible to precisely diagnose the sleep stage based on characteristic waves as doctors do, though a roughly diagnosis is possible.Moreover, some methods are based on a kind of template matching. This makes it difficult to cope with the large variation of EEG, such as fluctuations of the frequency pattern and the differences between individuals. The method of detecting the characteristic waves in EEG must, therefore, be able to recognize the time transient of the frequency pattern and be robust to variation of patterns.Manuscript received July 3, 1998; revised September 2, 1999. Asterisk indicates corresponding author.*T. Shimada is with the Applied Superconductivity Research Laboratory, Tokyo Denki University 2-1200 Muzai-Gakuendai, Inzai-shi, Chiba 270-1382, Japan (e-mail: shimada@asrl.dendai.ac.jp).T. Shiina is with the Institute of Information Sciences and Electronics, University of Tsukuba 1-1-1 Tennoudai, Tsukuba-City, Ibaraki 305-8573, Japan.Y. Saito is with the Research Institute for EEG Analysis 1-9-1-101 Nakasato, Kitaku, Tokyo 114-0015, Japan.Publisher Item Identifier S 0018-9294(00)01774-2.Recently, neural networks (NN's) have been applied to various kinds of problems in many fields due to their ability to analyze complicated systems without accurate modeling in advance [4]. In this paper, we propose a new type of NN model to detect several kinds of important characteristic waves in sleep EEG that are needed to diagnose sleep stages (...

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