• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.41-51
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• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• Biomolecules & Therapeutics
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• v.7 no.3
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• pp.242-248
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• 1999

Diazepam, a benzodiazepine (BDZ) agonist, produces sedation and flumazenil, a BDZ antagonist, blocks these actions. The aim of this study was to examine the effects of BDZs on cortical electroencephalogram (EEG) in rats. The recording electrodes were implanted over the frontal and parietal cortices bilaterally, and the reference and ground electrodes over cerebellum under ketamine anesthesia. To assess the effects of diazepam and flumazenil, rats were injected with diazepam (1 mgHg, i.p.) and/or flumazenil ( 1 mg/kg, i.p.), and the EEG was recorded before and after drugs. Normal awake had theta peak in the spectrum and low amplitude waves, while normal sleep showed large amplitude of slow waves. The powers of delta, theta and alpha bands were increased during sleep compared with during awake. Diazepam reduced the mobility of the rat and induced sleep with intermittent fast spindles and large amplitude of slow activity, and it produced broad peak over betaL band and increased the power of gamma band, which were different from EEG patterns in normal sleep. Saline injection awakened rats and abolished fast spindles for a short period about 2-5 min from EEG pattern during diazepam-induced sleep. Flumazenil blocked both diazepam-induced sleep and decreased the slow activities of delta, theta, alpha and betaL, but not of gamma activity for about 10 min or more. This study may indicate that decrease in power of betaL and betaH bands can be used as the measure of central action of benzodiazepines, and that the EEG parameters of benzodiazepines have to be measured without control over the behavioral state by experimenter.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.4
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• pp.333-341
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• 2021

Benzodiazepines (BDZs) drugs act on the GABA_A receptor, function as nerve suppressors, and are used to treat anxiety, insomnia, and panic disorder. We analyzed the data of 30 individuals to determine any differences in the sleep-electroencephalogram findings among individuals varying in age, benzodiazepine use, and duration of benzodiazepine use. Comparisons between users and non-users of benzodiazepines, short-term and long-term users, older and younger users, and older short-term and older long-term users, were achieved using electroencephalographic findings obtained through polysomnography. The parameters evaluated included sleep latency, sleep efficiency, sleep-stage percentages, number of sleep spindles, and average frequency of sleep-spindle. The difference between benzodiazepine users and non-users was significant with respect to sleep-stage percentages and average frequency of sleep-spindle. Older and younger users differed significantly with respect to sleep efficiency and sleep-stage percentages, whereas significant difference for sleep efficiency was obtained between long-term and short-term users. Taken together, our results indicate that BDZ consumption suppresses slow-wave sleep and increases the frequency of sleep spindles.