Spectral Estimation of Nasal Cyclic Rhythm by Nasal Airflow Temperature Measurement

2014 ◽  
Vol 573 ◽  
pp. 848-855
Author(s):  
R. Sukanesh ◽  
E. Muthu Kumaran
Keyword(s):  
Pathological Condition ◽  
Spectral Estimation ◽  
Power Spectral Analysis ◽  
Nasal Airflow ◽  
Airway Patency ◽  
Nasal Cycle ◽  
Power Spectral ◽  
Left And Right ◽  
The Difference ◽  
Cyclic Fluctuation

.The nasal cycle is referred to a cyclic fluctuation in congestion of the nasal mucosa that results in rhythmic and bilateral reciprocal alteration of nasal airway patency. The purpose of this study is to deal with statistical and power spectral analysis of nasal cycle by measuring the temperature difference between the airflow of both left and right nostrils. Five adult voluntary healthy subjects are enrolled for the study. Nasal temperature probe combined with amplifier are used for recording nasal airflow temperature on both nostrils. The highest nasal airflow temperature values are detected at the end of expiration and the lowest values are detected at the end of inspiration. Nasal cycle found in all the subjects and lasted to the minimum of 30 minutes to maximum of 6 hours. The difference in temperature of both nostrils is statistically significant (p<0.05) and spectral estimation is made using autoregressive modeling. The method is used to quantify nasal obstruction in pathological condition and also to correlate the related physiological phenomenon.

scholarly journals A study on wavelet analysis of SSVEP Signals

2018 ◽  
Vol 7 (2.25) ◽  
pp. 10
Author(s):  
Bincy Babu ◽  
R Chandrasekaran ◽  
Josline Elsa Joseph ◽  
Thella Shalem Rahul ◽  
T R Thamizhvani ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Spectral Estimation ◽  
Power Spectral Analysis ◽  
Feature Analysis ◽  
Eeg Signals ◽  
Power Spectral ◽  
Frequency Components ◽  
Bci System

Almost every Brain Control Interfcae (BCI) system is framed based on Steady State Visual Evoked Potential (SSVEP) which is predicted through distinguishing overriding frequency components in Electroencephalography (EEG) signals. The proposed system aims in accurate feature extraction of SSVEP signals. Power spectral analysis and wavelet analysis are done for feature analysis. The feature set variation for male and female subjects are obtained. Compared power spectral estimation and wavelet analysis, merits and demerits of each approach can be identified from the outcomes. It offers a theoretical reference of practical choice for BCI application.  

Human Intelligence and Power Spectral Analysis of Visual Evoked Potentials

1980 ◽  
Vol 50 (1) ◽  
pp. 192-194 ◽  
Author(s):  
Mariko Osaka ◽  
Naoyuki Osaka
Keyword(s):  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Power Spectra ◽  
Power Spectral Analysis ◽  
Mentally Retarded ◽  
Power Spectral ◽  
The Difference ◽  
Mentally Retarded Children ◽  
The Relationship ◽  
Visual Evoked

The relationship between intelligence and power spectra of visual evoked potential was investigated using 8 normal and 8 mentally retarded children as subjects. The results showed the power spectrum of mentally retarded has a peak at 4 to 6 Hz, whereas that of normal has two apparent peaks at 4 and 12 Hz. It appears the peak at 12 Hz reflects the difference of intelligence.

scholarly journals Modulation of the ultradian human nasal cycle by sleep stage and body position

2017 ◽  
Vol 75 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Richard E. Frye ◽  
Deborah F. Rosin ◽  
Adrian R. Morrison ◽  
Fidias E. Leon-Sarmiento ◽  
Richard L. Doty
Keyword(s):  
Rem Sleep ◽  
Sleep Stage ◽  
Body Position ◽  
Video Camera ◽  
Sleep Stages ◽  
Nasal Airflow ◽  
Somatosensory Stimulation ◽  
Nasal Cycle ◽  
Left And Right ◽  
The Right

ABSTRACT Objective: The nasal cycle, which is present in a significant number of people, is an ultradian side-to-side rhythm of nasal engorgement associated with cyclic autonomic activity. We studied the nasal cycle during REM/non-REM sleep stages and examined the potentially confounding influence of body position on lateralized nasal airflow. Methods: Left- and right-side nasal airflow was measured in six subjects during an eight-hour sleep period using nasal thermistors. Polysomnography was performed. Simultaneously, body positions were monitored using a video camera in conjunction with infrared lighting. Results: Significantly greater airflow occurred through the right nasal chamber (relative to the left) during periods of REM sleep than during periods of non-REM sleep (p<0.001). Both body position (p < 0.001) and sleep stage (p < 0.001) influenced nasal airflow lateralization. Conclusions: This study demonstrates that the lateralization of nasal airflow and sleep stage are related. Some types of asymmetrical somatosensory stimulation can alter this relationship.

Correlated and uncorrelated high-frequency oscillations in phrenic and recurrent laryngeal neurograms

1988 ◽  
Vol 59 (4) ◽  
pp. 1188-1203 ◽  
Author(s):  
E. N. Bruce
Keyword(s):  
Central Pattern Generators ◽  
Power Spectra ◽  
Power Spectral Analysis ◽  
Pattern Generation ◽  
Common Source ◽  
Power Spectral ◽  
Left And Right ◽  
Motor Outputs ◽  
Pattern Generators ◽  
40 Hz

1. Power spectral analysis of phrenic and recurrent laryngeal (or efferent vagal) inspiratory discharge activity from anesthetized cats revealed a peak within the 60- to 110-Hz range in all spectra, plus a peak within the 40- to 60-Hz range in the laryngeal (and efferent vagal) spectra, and a peak less than 40 Hz in the phrenic spectra. 2. A 60- to 110-Hz peak was present in coherence spectra between the left and right phrenic neurograms, the left and right recurrent laryngeal (and efferent vagal) neurograms, and all combinations of phrenic-laryngeal (and phrenic-efferent vagal) pairs. It is concluded that the nearly-periodic oscillations represented by these peaks arise from a single source that projects functionally in parallel to many respiratory motor outputs. This source may be part of, or interact with, respiratory central pattern generation. 3. The 40- to 60-Hz oscillations in left and right recurrent laryngeal (and efferent vagal) neurograms were uncorrelated or occasionally were very weakly correlated. Thus it is unlikely that these oscillations arise from a common source such as a second respiratory central pattern generator. 4. The oscillations less than 40 Hz were weakly correlated between left and right phrenic neurograms. This correlation may be due substantially to spinal crossed-phrenic pathways. 5. It is proposed that both the 40- to 60-Hz oscillations in recurrent laryngeal neurograms and the oscillations below 40 Hz in phrenic neurograms originate in neural circuits associated with individual left or right recurrent laryngeal or phrenic motor outputs. 6. Our results do not support the interpretation that multiple peaks in phrenic and recurrent laryngeal power spectra are due to two respiratory central pattern generators whose outputs have parallel pathways to respiratory motoneurons.

A Power Spectral Analysis Method for Wind-Induced Response of Flexible Structures

2013 ◽  
Vol 405-408 ◽  
pp. 1125-1129
Author(s):  
Jing Li ◽  
Xin Wang
Keyword(s):  
Spectral Analysis ◽  
Power Spectrum ◽  
Wavelet Packet ◽  
Spectral Estimation ◽  
Flexible Structures ◽  
Power Spectral Analysis ◽  
Induced Response ◽  
Roof Structure ◽  
Power Spectral ◽  
Spectral Analysis Method

A relational expression of wavelet packet coefficients and power spectrum is derived based on the theories of wavelet packet analysis. The new expression is proposed to compute the power spectrum of wind-induced response of structures. Further, the approach is applied to the power spectral analysis of the response signals of a large-span roof structure, and the accuracy of spectral estimation for stochastic signals is verified.

scholarly journals The Variations in Light Curves of Contact Binary AU SER

1998 ◽  
Vol 11 (1) ◽  
pp. 392-392
Author(s):  
Li Zong-Yun Ding Yue-Rong
Keyword(s):  
Thermal Contact ◽  
Power Spectral Analysis ◽  
Light Curves ◽  
Minimum Phase ◽  
Primary Minimum ◽  
Power Spectral ◽  
Short Period ◽  
Contact Binary ◽  
The Difference ◽  
Sudden Decrease

Photometry of AU Ser, a contact binary in poor thermal contact, was made using the 1-meter reflector of the Yunnan Observatory, China, in 1991, 1992and 1995. Our observations, together with Binnendijk’s (1972), show variations in light curves similar to ones of VW Cep: 1) Changes in sign and value of O’Connell effect. According to Binnendijk(1972), the maximum following primary minimum was higher than the other maximum, i.e., the O’Connell effect was positive. In our 1991 and 1992 light curves it became negative. After three years, however, the situation has reversed again. 2) Variations of the difference between minimum depths.The difference in 1991 was smaller than that in 1969 and 1970. It got smallest in 1992 and largest in 1995. The difference increases by about 0.3 mag in three years, which is hard to explained. 3) The phase shift. The observed minimum phase in 1991, 1992 and 1995 have progressively displaced forward with respect to computed minimum phase according to Binnendijk’s ephemeris. The O-C diagram seems to indicate a sudden decrease of orbital period after 1970. Also, short period oscillations have been found at frequencies of 0.0003, 0.005 and 0.0075 Hz. Flickering. with an average amplitude of about 0.05 mag, can be seen in the light curves. The power spectral analysis suggest the existence of oscillations. It is unusual that such oscillations exist in a contact binary.

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Spectral Estimation ◽  
Interrater Agreement ◽  
Probabilistic Networks ◽  
Probabilistic Network ◽  
Spectral Bands ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

Sign in / Sign up

Export Citation Format

Share Document