Hong-Beom Shin

Detection of apneic events from single channel nasal airflow using 2nd derivative method

Detection of sleep apnea is one of the major tasks in sleep studies. Several methods, analyzing the various features of bio-signals, have been applied for automatic detection of sleep apnea, but it is still required to detect apneic events efficiently and robustly from a single nasal airflow signal under varying situations. This study introduces a new algorithm that analyzes the nasal airflow (NAF) for the detection of obstructive apneic events. It is based on mean magnitude of the second derivatives (MMSD) of NAF, which can detect respiration strength robustly under offset or baseline drift. Normal breathing epochs are extracted automatically by examining the stability of SaO(2) and NAF regularity for each subject. The standard MMSD and period of NAF, which are regarded as the values at the normal respiration level, are determined from the normal breathing epochs. In this study, 24 Polysomnography (PSG) recordings diagnosed as obstructive sleep apnea (OSA) syndrome were analyzed. By analyzing the mean performance of the algorithm in a training set consisting of three PSG recordings, apnea threshold is determined to be 13% of the normal MMSD of NAF. NAF signal was divided into 1-s segments for analysis. Each segment is compared with the apnea threshold and classified into apnea events if the segment is included in a group of apnea segments and the group satisfies the time limitation. The suggested algorithm was applied to a test set consisting of the other 21 PSG recordings. Performance of the algorithm was evaluated by comparing the results with the sleep specialists manual scoring on the same record. The overall agreement rate between the two was 92.0% (kappa=0.78). Considering its simplicity and lower computational load, the suggested algorithm is found to be robust and useful. It is expected to assist sleep specialists to read PSG more quickly and will be useful for ambulatory monitoring of apneas using airflow signals.

Quantitative study of the sleep onset period via detrended fluctuation analysis: Normal vs. narcoleptic subjects

OBJECTIVE To examine the process of the sleep onset quantitatively and explore differences between narcoleptics and controls during the sleep onset period (SOP). METHOD Dynamic detrended fluctuation analysis (DFA) was applied to electroencephalograms recorded during multiple sleep latency tests of 11 drug-free narcoleptic patients (19.3+/-4.4 yrs; 8 males) and 9 healthy controls (23.8+/-6.3 yrs; 6 males). The SOP of each group was estimated by fitting the time courses of the DFA scaling exponents to a parametric curve. RESULTS The sequence of DFA exponents showed that electrophysiological brain activity was changing rapidly across the SOP. This transition was also verified by a conventional method (i.e., dynamic spectral analysis). The SOP durations of narcoleptics and controls were estimated as 239+/-25 s and 145+/-20 s, respectively. CONCLUSIONS The significantly larger SOP of narcoleptics, compared to controls, is consistent with the wake state of narcolepsy being more susceptible to sleep due to a lower barrier to transitioning to sleep. SIGNIFICANCE Our results suggest that electrophysiological signatures of narcolepsy could be quantified by dynamic DFA, so the method may have promise as a potential tool to help the diagnosis of narcolepsy despite the present studys limited sample size.

Compact continuum brain model for human electroencephalogram

A low-dimensional, compact brain model has recently been developed based on physiologically based mean-field continuum formulation of electric activity of the brain. The essential feature of the new compact model is a second order time-delayed differential equation that has physiologically plausible terms, such as rapid corticocortical feedback and delayed feedback via extracortical pathways. Due to its compact form, the model facilitates insight into complex brain dynamics via standard linear and nonlinear techniques. The model successfully reproduces many features of previous models and experiments. For example, experimentally observed typical rhythms of electroencephalogram (EEG) signals are reproduced in a physiologically plausible parameter region. In the nonlinear regime, onsets of seizures, which often develop into limit cycles, are illustrated by modulating model parameters. It is also shown that a hysteresis can occur when the system has multiple attractors. As a further illustration of this approach, power spectra of the model are fitted to those of sleep EEGs of two subjects (one with apnea, the other with narcolepsy). The model parameters obtained from the fittings show good matches with previous literature. Our results suggest that the compact model can provide a theoretical basis for analyzing complex EEG signals.

Characteristic time scales of electroencephalograms of narcoleptic patients and healthy controls

Sleep electroencephalograms (EEGs) typically showed correlated fluctuations that became random-like oscillations beyond a characteristic time scale. To investigate this behavior quantitatively, the detrended fluctuation analysis (DFA) was applied to EEGs of 10 narcoleptic patients (22.0 ± 4.0 yrs; 6 males) and 8 healthy controls (24.0 ± 2.0 yrs; 5 males). The characteristic time scales of the narcoleptics and controls were estimated as 1.8 ± 0.7 and 4.4 ± 1.2s, respectively (significance level, p<0.01). We further performed DFA of the EEGs segmented into 30s epochs and found that the DFA scaling exponents increased in deep sleep stages. These results were verified with power spectrum and auto-correlation analysis, and reproduced by a mathematical model. We thus concluded that characteristics of EEGs of narcoleptic patients could be differentiated from those of healthy subjects, suggesting a potential application of DFA in diagnosing narcolepsy.