• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1190-1195
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• 1990

A new system for automatic interpretation of the awake electroencephalogram(EEG) was developed in this work. We first clarified all the necessary items for EEG interpretation in accordance with an analysis of visual inspection of the rhythms by a qualified electroencephalographer (EEGer), and then defined each item quantitatively. Concerning the automatic interpretation, we made an effort to find out specific EEG parameters which faithfully represent the procedure of visual interpretation by the qualified EEGer. Those specific EEG parameters were calculated from periodograms of the EEG time series. By using EEG data of 14 subjects, the automatic EEG interpretation system was constructed and compared with the visual interpretation done by the EEGer. The automatic EEG interpretation thus established was proved to be in agreement with the visual interpretation by the EEGer.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.19-23
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• 1996

A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.503-508
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• 1992

A new method for making automatic electroencephalogram(EEG) report based on the automatic quantitative interpretation of awake EEG was developed. We first analysed a. relationship between EEG reports and quantitative EEG interpretation done by a qualified electroencephalographer(EEGer) for 22 subjects. Based on the analysed relationship and usual process of report making by the EEGer, we defined all terminology necessary for EEG report and established rules for EEG report making. By the combined use of the proposed EEG report making and the method for automatic quantitative EEG interpretation presented at '90 KACC, we were able to make the automatic EEG reports which were equivalent to the EEG reports written by the EEGer. As all the procedures were programmed in a personal computer equipped with an AD (analogue-to-digital) converter, the automatic EEG reports were obtained in almost real time in usual actual EEG recording situation with only a few seconds time lag for the analysis in the computer. The proposed report making method and the quantitative EEG interpretation method will be effectively applicable to the clinical use as an assistant tool for physicians.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.505-508
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• 1995

The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.572-577
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• 1994

The automatic detection of artifacts and vigilance level as for pre-processing of the automatic EEG interpretation are discussed. The equations for detecting artifacts and vigilance level were determined such that they would conform to the procedures that an EEGer (one of the authors, H.S.) usually adopts for visual inspection of the actual EEG record. The automatic EEG interpretation was found to be improved by the newly developed pre-processing method even will artifact contamination or in drowsy condition of the subjects.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.967-971
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• 1989

A new system for automatic scoring of 'organization' of the EEG dominant rhythm was constructed and applied to 18 normal subjects and 15 patients. Organization parameters which best represented the 'organization' as judged by 5 neurologists' visual inspection were calculated and the automatic organization scoring was obtained by a linear regression of those organization parameters. Furthermore, values of the regression coefficients were used to study the characteristics of EEG interpretation by each neurologist, and this scoring technique can also be applied to the training of EEG interpretation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.106.5-106
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• 2001

A method for automatically selecting the electroencephalogram(EEG) segments was developed by grading each segment based on organization of EEG, vigilance level, open versus closed eyes condition and the artifact contamination.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.493-493
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• 2000

Multi-valued decision making for transitional stochastic events was newly derived based on conditional probability of database. The two values (on-off) decision making method without transition had been proposed by one of the author in a previous work for a purpose of realizing human on-off decision making. The current method is an extension of the previous on-off decision making. By combining the conditional probability and the transitional probability, the closed form of the algorithm for the multi-valued transitional decision making was derived. The proposed multi-valued decision making was successfully applied to the determination of the five levels of the vigilance of a subject during the EEG recording; awake stage, drowsy stage and sleeping stages (stage 1, stage 2/3, REM (rapid eye movement)). The method for determining the vigilance level can be directly usable for the two purposes; selection of awake EEG segments for automatic EEG interpretation, and determination of sleep stages through sleep EEG. The proposed multi-valued decision making with a mathematical background of the probability can be applicable widely, in industries and in medical fields for purposes of the multi-valued decision making.

• The Korean Journal of Nuclear Medicine
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• v.37 no.1
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• pp.24-33
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• 2003

Finding epileptogenic zone is the most important step for the successful epilepsy surgery. F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and single photon emission computed tomography (SPECT) can be used in the localization of epileptogenic foci. In medial temporal lobe epilepsy, the diagnostic sensitivity of FDG-PET and ictal SPECT is excellent. However, detection of hippocampal sclerosis by MRI is so certain that use of FDG-PET and ictal SPECT in medial temporal lobe epilepsy is limited for some occasions. In neocortical epilepsy, the sensitivities of FDG-PET or ictal SPECT are fair. However, FDG-PET and ictal SPECT can have a crucial role in the localization of epileptogenic foci for non-lesional neocortical epilepsy. Interpretation of FDG-PET has been recently advanced by voxel-based analysis and automatic volume of interest analysis based on a population template. Both analytical methods can aid the objective diagnosis of epileptogenic foci. Ictal SPECT was analyzed using subtraction methods and voxel-based analysis. Rapidity of injection of tracers, ictal EEG findings during injection of tracer, and repeated ictal SPECT were important technical issues of ictal SPECT. SPECT can also be used in the evaluation of validity of Wada test.

• Sleep Medicine and Psychophysiology
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• v.10 no.1
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• pp.52-60
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• 2003

Objectives: Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. The lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, utilization of simultaneous EEG and EOG processing and analyses to detect stage 1 sleep automatically were attempted. Methods: Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. A relative power of alpha waves less than 50% or relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM(slow eye movement) was defined as the duration of both-eye movement ranging from 1.5 to 4 seconds, and was also regarded as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results were compared to the manual rating results done by two polysomnography experts. Results: A total of 169 epochs were analyzed. The agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen’s Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Conclusion: Generally, digitally-scored sleep staging shows accuracy up to 70%. Considering potential difficulty in stage 1 sleep scoring, accuracy of 79.3% in this study seems to be strong enough. Simultaneous analysis of EOG differentiates this study from previous ones which mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnari remains a valid one in this study.