Bunnoshin Ishikawa

Quantitative evaluation of photic driving response for computer-aided diagnosis

The aim of our research is the quantification of the photic driving response, a routine electroencephalogram (EEG) examination, for computer-aided diagnosis. It is well known that the EEG responds not only to the fundamental frequency but also to all sub and higher harmonics of a stimulus. In this study, we propose a method for detecting and evaluating responses in screening data for individuals. This method consists of two comparisons based on statistical tests. One is an intraindividual comparison between the EEG at rest and the photic stimulation (PS) response reflecting enhancement and suppression by PS, and the other is a comparison between data from an individual and a distribution of normals reflecting the position of the individuals data in the distribution of normals in the normal database. These tests were evaluated using the Z-value based on the Mann-Whitney U-test. We measured EEGs from 130 normal subjects and 30 patients with any of schizophrenia, dementia and epilepsy. Normal data were divided into two groups, the first consisting of 100 data for database construction and the second of 30 data for test data. Using our method, a prominent statistical peak of the Z-value was recognized even if the harmonics and alpha band overlapped. Moreover, we found a statistical difference between patients and the normal database at diagnostically helpful frequencies such as subharmonics, the fundamental wave, higher harmonics and the alpha frequency band.

Evaluation of brain aging by EEG analysis of photic driving response

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, 20s, 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 20s, 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.

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

OBJECTIVE In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). APPROACH We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. MAIN RESULTS Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. SIGNIFICANCE Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

P24-17 Combination use of directed information and directed coherence for EEG analysis

of dystonia patients in our clinic, but the mechanism of action of acupuncture has not been clear. Therefore, we investigated the mechanism of action of acupuncture by quantitative EEG (qEEG) analysis. Methods: Six healthy volunteers who had already undergone acupuncture participated in this study. All participants were fully informed about this study and gave the written consent to us. Acupuncture stimulation was performed by a proficient acupuncturist on the right LI4 (Heku) for 10 minutes. Eye closed spontaneous EEG data was recorded from 19 scalp locations according to the international 10/20 system before, during and after acupuncture stimulation. Twenty artifact-free 2-seconds epochs (a total of 40 seconds) EEG data per condition were selected for qEEG analysis. Standardized low resolution brain electromagnetic tomography (sLORETA) was applied to qEEG analysis. Three-dimensional current density of brain calculated by sLORETA was compared among before, during and after acupuncture stimulation. Results: Alpha1 (8.5 10 Hz) current density in both sides of dorsolateral frontal area significantly increased after the acupuncture stimulation compared to before acupuncture. Conclusions: This result may indicate that acupuncture stimulation on LI4 (Heku) let brain electric activity of the both sides of dorsolateral frontal area to stable preparations state from activated state. There is possibility that a central nerve function varied from a strain state to a stable state by acupuncture. This finding may contribute to clarify the mechanism of action of acupuncture.

P37-20 Absence of P300 and presence of N1 in two patients after herpes encephalitis

Objective: White matter hyperintensities (WMH) reflect cerebral vascular pathology and can compromise cognitive function. The relationship between extent of WMH and severity of dementia is not very strong however. Analysis of functional brain characteristics may provide additional information to study the clinical effects of WMH. Methods: Resting-state EEGs of 17 vascular dementia patients with confluent WMH (mean mini mental state examination (MMSE) 22) and 17 subjects with subjective memory complaints only, serving as controls (MMSE 29), were individually matched for age and gender and compared with respect to relative power of delta, theta, alpha and beta activity (in each group: mean age 74, 9 males). Group differences were analysed using Student’s T-test for paired samples. Relationship between MMSE and (log-transformed) relative powers in the VaD patients was established with linear regression analysis (age and gender as co-variates). Sensitivity and specificity of mean relative power was established by plotting ROC curves. Results: Relative alpha and beta power was significantly higher in controls (mean alpha power 0.38 versus 0.25 in patients, mean beta power 0.19 versus 0.08). Relative theta and delta power was significantly higher in WMH patients (mean theta power 0.32 versus 0.14 in controls and delta power 0.31 versus 0.23). Higher relative beta power was a independent predictor of higher MMSE (t = 3.2, p = 0.007). Highest diagnostic accuracy was found in the beta band (area-under-the-curve 0.95) with sensitivity of 0.82 and specificity of 1.0 using a cut-off point at 0.145. Conclusion: Quantitative power analysis is able to show large and highly significant differences between WMH patients and controls. Also, we found the relative beta power associated with MMSE and with excellent discriminative characteristics. This indicates that oscillatory brain dynamics are disturbed in subjects with WMH and might aid in the researching the role of WMH in dementia.

P4-14 P300 ERP of normal young adults

Objective: Our aim is to obtain reference data of P300 ERP from normal young adults for future study of patients with psychiatric or neurological diseases. Methods: Subjects are normal young adults ranged from ages of 18 years old to 26 years old. Their average age is 22.0±2.1 years of age. All of them are candidates of automobile test drivers. Stimuli and recording P300 ERP: stimuli are pure tones. Target stimulus is 2 kHz tone burst and non target stimulus is 1 kHz tone burst. The tone burst envelop is 1 100 1 msec. Recording sessions in order. Session 1: Pushing a button by the right thumb at target stimuli only. Session 2: Pushing a button by the left thumb at target stimuli only. Session 3: no pushing a button by the thumb but counting mentally number of target stimuli. The analysis time is 100 msec. Results: (1) Appearance percent of P300 at target stimuli by pushing a button was 88%. (2) Appearance percent of P300 at target stimuli by mentally counting numbers of target stimuli was only 30%. (3) Peak latencies and amplitude of P300 between target and non target were statistically significant (P< 0.005). (4) Peak latencies and amplitudes of P300 between pushing right button and left button were not statistically significant. (5) Peak latencies and amplitudes of P300 between pushing a button and mentally counting were statistically significant. Conclusion: Appearance percent of P300 in normal young adults is very high. Task of pushing a button is better to elicit P300 rather than task of mentally counting.

P6-16 Silent epileptic EEG areal seizure — long term EEG observation of repeated silent seizure in clinical environment

Objective: BESA Epilepsy uses a new hypercluster technique and a file independent data management to combine similar events over 24 hours of EEG. Each day, the physician inspects the hyperclusters and decides whether they are epileptiform or not. Optimized source waveform and EEG segment displays, 3D maps, and localization in a head scheme allow for fast decision and assessment of the region of origin. Methods: A new spike detection and clustering algorithm based on an EEG transformation into 29 regional brain sources was developed. Clusters were calculated in 2 hour epochs and combined into daily hyperclusters using empirical rules on similarity in waveshape and topography. 24 hour EEG data of 44 epilepsy patients (21 children) were evaluated by independent raters using traditional visual inspection versus fast hypercluster evaluation. Results: Visual rating yielded 107 epileptiform spike types. Hypercluster rating agreed in 85% (temporal lobe spikes 94%, extratemporal 78%). In a 24 h recording, about 15 25 hyperclusters had to be inspected to decide whether they reflected epileptiform discharges, artifacts, or other EEG patterns. Due to the rapid inspection tools, the decision and reporting process was typically completed within 5 minutes by experienced physicians. Conclusion: The traditional hourly evaluation of 2 5 minute epochs of long-term EEG can be readily supplemented by a computer-based hypercluster evaluation. This adds a fast, comprehensive overview and report, an independent control of the existence of one or multiple spike foci, and an estimation of their origin. The involvement of the physician in the decision process allowed to increase sensitivity at the cost of reviewing and rejecting more artifact hyperclusters. Longterm EEG evaluation is facilitated by a file independent graphical data management. This enables the rapid clinical inspection of the same, fixed 24 h interval every day without the time-consuming need to open and close different EEG files.

Correction of fMRI signals by using the vessel diameter extracted from MRA images

Functional MRI (fMRI) is already one of the most useful tools to investigate brain activities. Normally, activations are defined by the significant difference between distributions of signal intensities on the two kinds of conditions. However, intensities of these signals depend on the vessel diameter because they reflect blood flow volume. Correction using vessel diameter will be needed when we compare signal intensities at two different areas accurately. In this study, we measured the vessel diameter by magnetic resonance angiography (MRA) and correct the fMRI signals by using the diameter. We then evaluated the signals at different areas. Here, we considered the spatial resolution of MRA images and focused on the thick vessel around motor area.