No abstract available.
Genetics* ; Hyperaldosteronism*

OBJECTIVE: Among various methods developed to quantitatively explore electroencephalograms (EEG), we focused on a wavelet method that was known to yield robust results under nonstationary conditions. The aim of this study was thus to introduce the wavelet method and demonstrate its potential use in clinical sleep studies. METHOD: This study involved artificial EEG specifically designed to validate the wavelet method. The method was performed to obtain time-dependent spectral power and phase angles of the signal. Synchrony of multichannel EEG was analyzed by an order parameter of the instantaneous phase. The standard methods, such as Fourier transformation and coherence, were also performed and compared with the wavelet method. The method was further validated with clinical EEG and ERP samples available as pilot studies at academic sleep centers. RESULT: The time-frequency plot and phase synchrony level obtained by the wavelet method clearly showed dynamic changes in the EEG waveforms artificially fabricated. When applied to clinical samples, the method successfully detected changes in spectral power across the sleep onset period and identified differences between the target and background ERP. CONCLUSION: Our results suggest that the wavelet method could be an alternative and/or complementary tool to the conventional Fourier method in quantifying and identifying EEG and ERP biomarkers robustly, especially when the signals were nonstationary in a short time scale (1-100 seconds).
Biomarkers ; Electroencephalography ; Fourier Analysis ; Pilot Projects

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA; 3) electrophysiological properties of narcolepsy could be characterized by the DFA; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.
Brain ; Electroencephalography ; Humans ; Hypogonadism ; Life Cycle Stages ; Mitochondrial Diseases ; Narcolepsy ; Ophthalmoplegia ; Polysomnography ; Sleep Stages

No abstract availalbe.
Heart Failure* ; Heart*

No abstract available.

This study examined the relation between anger-coping types and hypertension in employed men aged 40-60 who consented to participate during the biannual physical checkup in the department of health management in 1988. The subjects analyzed were five hundred thirteen excluding those having hypertension history and/or current antihypertensive medications. Anger-coping types were constructed from the Harburg's model with two hypothetical anger-provoking situations involving wife and boss. Hypertensives were defined more than 140mmHg systolic blood pressure and/or 90mmHg diastolic blood pressure. Hypertensives were one hundred fifty two(29.6%) and those who suppressed their anger were 61.6%0 and 62.8% in wife and boss situations respectively. Items of anger, guilt, protest, and suppressed anger in wife situation showed odds ratios of 0.78-0.94 without statistical significance. But four items in boss situation showed odds ratios more than 1, especially anger-in types of anger item had 1.58 times the prevalence of hypertension of anger-out types(95% confidence intervals(CI) 1.06~2.35) and subjects who indicated that suppressed their anger had 1.55 times the prevalence of hypertension of those who expressed their anger(95% CI l.03~2.32). For anger suppressed vs. expressed types of total suppressed anger index, prevalence of hypertension was 1.31(95% CI 0.83~2.08). After adjusting for age, body mass index, smoking and drinking, the odds ratios were slightly increased in both situations except guilt items compared with univariate analysis. These results suggest that the relation between Harburg's anger-coping model and hypertension is replicated partially in this subjects.
Anger ; Blood Pressure ; Body Mass Index ; Drinking ; Guilt ; Humans ; Hypertension* ; Male ; Odds Ratio ; Prevalence ; Smoke ; Smoking ; Spouses

No abstract available.
Jaw*

No abstract available.
Ischemia* ; Lower Extremity*

No abstract available.
Osteotomy* ; Rabbits*

No abstract available.
Osteotomy* ; Rabbits*