Time filter

Source Type

Utsunomiya Tochigi, Japan

Fukami T.,Yamagata University | Iyoda H.,Yamagata University | Shimada T.,Tokyo Denki University | Ishikawa F.,Hotokukai Utsunomiya Hospital
International Journal of Innovative Computing, Information and Control | Year: 2014

In this study, vie propose an analysis method for electroencephalography (EEG) during hyperventilation (HV) test, a routine EEG examination. The HV test was performed for 4 min during HV and 4 min at rest after HV (POST HV). Our analysis method introduced an index (Z-score) to evaluate enhancement/suppression at a specific frequency during HV or POST HV compared with the reference EEG obtained 30 s before the HV test. We investigated the optimal frequency band, electrode, and period in the HV test for discriminating epilepsy patients from healthy subjects. The best EEG was theta wave measured at Cz in the period 1 min from HV start to 1.5 min after HV. In 44 healthy subjects and 23 epilepsy patients, using a support vector machine and evaluation of the performance by the leave-one-out cross-variation method, vie obtained a 71.7% accuracy. © 2014, IJICIC Editorial Office. All rights reserved.

Fukami T.,Yamagata University | Watanabe J.,Yamagata University | Ishikawa F.,Hotokukai Utsunomiya Hospital
Computer Methods and Programs in Biomedicine | Year: 2016

Background and objective: In clinical examinations and brain-computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution. Methods: We constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller. Results: A 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging. Conclusions: We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller. © 2015 Elsevier Ireland Ltd.

Fukami T.,Yamagata University | Shimada T.,Tokyo Denki University | Ishikawa F.,Hotokukai Utsunomiya Hospital | Ishikawa B.,Hotokukai Utsunomiya Hospital
International Journal of Innovative Computing, Information and Control | Year: 2013

In the current study, we quantitatively evaluated aging and schizophrenia using statistical indices reecting enhancement and suppression at arbitrary frequencies using photic stimulation (PS). This index corresponds to the Z-score, which reects the distance between two amplitude distributions at an arbitrary frequency at rest and during PS. We measured EEGs from three groups: 50 healthy subjects aged between 20-30, 30 healthy subjects aged 60 and over, and 31 schizophrenia patients aged 60 and over. We obtained frequency characteristics of Z-scores in each group. Prominent characteristics of fundamental and harmonic components that were sensitive to schizophrenia disease and aging were observed at 6, 7 and 10 Hz PS. We then defined two indices to evaluate schizophrenia symptoms and aging: the average of Z-scores at fundamental and all harmonic frequencies except the harmonics in alpha band and gamma band over 30 Hz (Index A), and the subtraction value of the minimum Z-score in alpha band from the average of the Z-scores in gamma band (Index B). The results revealed a significant difference between the older healthy group and the schizophrenia group in Index A, and between the younger and older groups in Index B. © ISSN 1349-4198.

Fukami T.,Yamagata University | Shimada T.,Tokyo Denki University | Ishikawa F.,Hotokukai Utsunomiya Hospital | Ishikawa B.,Hotokukai Utsunomiya Hospital | Saito Y.,Institute for EEG Analysis
2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010 | Year: 2010

In this paper, we examined the possibility to estimate brain aging by EEG photic driving response. Normal subjects are classified into 3 age groups by age, 20's, 21-59, and over 60. We obtained the Z-score from EEG at rest and during photic stimulus (PS) by using the method for evaluating intraindividual EEG we have already proposed. Here, we showed the averaged Z-score of 3 age groups for 6 Hz PS as a representative example. As a results, in 20's, significant difference (|Z|>1.96, p<0.05) was recognized at fundamental and higher harmonic frequencies of PS frequency except second harmonic. With increase of age, we could see the decrease of Z-score at fundamental frequency and higher harmonics. In the group over 60, no significant difference at any frequencies was recognized. © 2010 IEEE.

Fukami T.,Yamagata University | Shimada T.,Tokyo Denki University | Ishikawa F.,Hotokukai Utsunomiya Hospital | Ishikawa B.,Hotokukai Utsunomiya Hospital | Saito Y.,The Institute for the EEG Analysis
International Journal of Innovative Computing, Information and Control | Year: 2011

Routine electroencephalography (EEG) is useful for diagnosing and following patients, but individual differences and extraneous factors such as arousal level can complicate the interpretation of EEG data. This study aimed to quantitatively evaluate the degree of enhancement and suppression of alpha waves in the eye opening and closure test. EEG was measured with eyes open and shut three times at 10-second intervals. Frequency analysis of the EEG was performed by Fourier transform, and the amplitude at the alpha peak frequency (8 ~ 13Hz) was obtained. We calculated a relative statistical value (Z-value) for the two states, allowing normalization of the data to the individual. We then introduced a simple evaluation index derived from the average and standard deviation of Z-values and evaluated our method in data from 120 healthy subjects and 59 patients that were divided into four groups: awake/drowsy and patients/healthy subjects. By using two indices, we recognized a statistically significant difference between the average of the healthy awake group and other groups. We also recognized a significant difference between the standard deviation of the healthy drowsy group and other groups. Therefore, these indices can be used to quantitatively evaluate eye opening and closure in routine EEG examinations. © ICIC International 2011.

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