scholarly journals A New Index of Coordinated Posterior and Anterior Evoked EEG to Detect Recall Under Sedation – A Pilot Study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dana Baron Shahaf ◽  
Gregory M. T. Hare ◽  
Andrew J. Baker ◽  
Violina Chenosia ◽  
Leonid Priven ◽  
...  
Keyword(s):  
Pilot Study ◽  
General Anesthesia ◽  
Signal To Noise Ratio ◽  
Positive Control ◽  
Negative Control ◽  
Clinical Use ◽  
Eeg Signal ◽  
Signal To Noise ◽  
Similar Drug ◽  
Noise Ratio

AbstractEEG-based technologies may be limited in identifying recall under sedation (RUS). We developed a novel index, posteriorization/anteriorization (P/A) index, based on auditory evoked EEG signal and assessed whether it could differentiate between patients with or without RUS. Methods: EEG and BIS were sampled from 3 groups: 1. Patients undergoing sedation (n = 26); 2. Awake volunteers (n = 13, positive control for recall) 3. Patients undergoing general anesthesia (GA, n = 12, negative control for recall). In recovery, recall was assessed using the BRICE questionnaire. Of the 26 sedated patients, 12 experienced recall. Both The P/A index and BIS differentiated between patients with recall and no recall. However, BIS differentiation may have been sensitive to the main drug used for sedation (midazolam vs. propofol) and the P/A index did not show similar drug-based sensitivity. Furthermore, only BIS results were correlated with EMG. Conclusion: This pilot study provided support for the association between P/A index and recall after sedation. Further research is needed in integrating the index into clinical use: (1) it should be derived by an easy-to-use EEG system with a better signal-to-noise ratio; (2) its applicability to other drugs must be shown.

scholarly journals Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing

2019 ◽  
Vol 8 (10) ◽  
pp. 3568-3578
Keyword(s):  
Performance Analysis ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Data Retrieval ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Noise Ratio

A very small amplitude (μV) of the electroencephalography (EEG) signal is infected by diverse artifacts. These artifacts have an effect on the distinctiveness of the signal because of which medical psychoanalysis and data retrieval is difficult. Therefore, EEG signals are initially preprocessed to eliminate the artifacts to produce signals that can serve as a base for further processing and analysis. Different filters are implemented to eliminate the artifacts present in the EEG signal. Recent research shows that window technique Finite Impulse Response (FIR) filter is usually used. In this paper, digital Infinite Impulse Response (IIR) filter and different Finite Impulse Response (FIR) window filters (Hanning, Hamming, Kaiser, Blackman) of various orders are implemented to eradicate the random noise added to EEG signals. Their performance analysis has been done in Matlab (R2016a) by calculating the mean square error, mean absolute error, signal to noise ratio, peak signal to noise ratio and cross-correlation. The results show that Kaiser Window based finite impulse response filter outperforms in removing the noise from the electroencephalogram signal. This research focuses on eradicating random noise in electroencephalogram signals but this approach will be extended to a different source of electroencephalogram contamination.

scholarly journals A new paradigm for temporal masking assessment: pilot study

CoDAS ◽  
2014 ◽  
Vol 26 (4) ◽  
pp. 302-307 ◽  
Author(s):  
Renata Filippini ◽  
Eliane Schochat
Keyword(s):  
Pilot Study ◽  
Undergraduate Students ◽  
Auditory Processing ◽  
Signal To Noise Ratio ◽  
Target Tone ◽  
Backward Masking ◽  
New Paradigm ◽  
Signal To Noise ◽  
Temporal Masking ◽  
Noise Ratio

PURPOSE: To determine the feasibility and applicability of a clinical backward masking test, focusing on the analysis of inter-stimuli interval, and not on the intensity thresholds as it has been traditionally done, thus proposing a new paradigm for temporal masking assessment.METHOD: The test consisted of the presentation of a target tone of 1.000 Hz followed by a broadband masking noise (950-1.050 Hz), with inter-stimuli interval of 0, 10, 20, 50 and 100 ms. The stimuli were presented monaurally to both ears, with intensity ratio between masker and target tone varying between -10, -20, -30 and -40 dB. Twenty undergraduate students, without hearing or auditory processing complaints, participated in this study.RESULTS: Regardless of the signal-to-noise ratio, we observed decrease of average performance according to the decrease of the interval between stimuli. We also observed the indication that little or no masking occurs at the 100 ms interval, suggesting this interval is unsuitable for temporal masking assessment. The average interval threshold was below 27 ms for all investigated intensities, and increased 9 ms with every increase of 10 dB at signal-to-noise ratio. The signal-to-noise ratios of -20 and -30 were the best ratios for the test application.CONCLUSION: The paradigm proposed in this pilot study proved to be feasible, easy to apply, and trustworthy, being compatible with other researches which are the foundation for the study of temporal masking. This theme deserves further studies, continuing the analysis initiated here.

Identification of Brain disorders by Sub-band Decomposition of EEG signals and Measurement of Signal to Noise Ratio

2016 ◽  
Vol 4 (3) ◽  
pp. 568
Author(s):  
Hadaate Ullah ◽  
Shahin Mahmud ◽  
Rubana Hoque Chowdhury
Keyword(s):  
Feature Extraction ◽  
Signal To Noise Ratio ◽  
Automated System ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Signal To Noise ◽  
Pass Filter ◽  
Noise Ratio

<p>In the case of medical science, one of the most restless researches is the identification of abnormalities in brain. Electroencephalogram (EEG) is the main tool for determining the electrical activity of brain and it contains rich information associated to the varieties physiological states of brain. The purpose of this task is to identify the EEG signal as order or disorder. It is proposed to enrich an automated system for the identification of brain disorders. An EEG signal of a patient has been taken as a sample. The simulation has been done by MATLAB. The file which consists of the signal has been called in and plotted the signals in MATLAB. The proposed system covers pre-processing, feature extraction, feature selection and classification. By the pre-processing the noises are ejected. In this case the signal has been filtered using band pass filter. The Discrete Wavelet Transform (DWT) has been used to decompose the EEG signal into Sub-band signal. The feature extraction methods have been used to extract the EEG signal into frequency domain and the time domain features. The SNR (Signal to Noise ratio) is obtained in this work is 1.1281dB.<strong></strong></p>

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Speech Reception in the Presence of Classroom Noise

1979 ◽  
Vol 10 (4) ◽  
pp. 221-230 ◽  
Author(s):  
Veronica Smyth
Keyword(s):  
Signal To Noise Ratio ◽  
Learning Processes ◽  
Speech Discrimination ◽  
Signal To Noise ◽  
Speech Reception ◽  
Monosyllabic Words ◽  
Noise Ratio

Three hundred children from five to 12 years of age were required to discriminate simple, familiar, monosyllabic words under two conditions: 1) quiet, and 2) in the presence of background classroom noise. Of the sample, 45.3% made errors in speech discrimination in the presence of background classroom noise. The effect was most marked in children younger than seven years six months. The results are discussed considering the signal-to-noise ratio and the possible effects of unwanted classroom noise on learning processes.

Quantifying the Effects of Background Speech Babble on Preschool Children's Novel Word Learning in a Multisession Paradigm: A Preliminary Study

2020 ◽  
Vol 63 (1) ◽  
pp. 345-356
Author(s):  
Meital Avivi-Reich ◽  
Megan Y. Roberts ◽  
Tina M. Grieco-Calub
Keyword(s):  
Word Learning ◽  
Signal To Noise Ratio ◽  
Preschool Age ◽  
Exposure Condition ◽  
Signal To Noise ◽  
Verbal Recall ◽  
Referent Selection ◽  
Main Effect ◽  
Noise Ratio ◽  
Novel Word Learning

Purpose This study tested the effects of background speech babble on novel word learning in preschool children with a multisession paradigm. Method Eight 3-year-old children were exposed to a total of 8 novel word–object pairs across 2 story books presented digitally. Each story contained 4 novel consonant–vowel–consonant nonwords. Children were exposed to both stories, one in quiet and one in the presence of 4-talker babble presented at 0-dB signal-to-noise ratio. After each story, children's learning was tested with a referent selection task and a verbal recall (naming) task. Children were exposed to and tested on the novel word–object pairs on 5 separate days within a 2-week span. Results A significant main effect of session was found for both referent selection and verbal recall. There was also a significant main effect of exposure condition on referent selection performance, with more referents correctly selected for word–object pairs that were presented in quiet compared to pairs presented in speech babble. Finally, children's verbal recall of novel words was statistically better than baseline performance (i.e., 0%) on Sessions 3–5 for words exposed in quiet, but only on Session 5 for words exposed in speech babble. Conclusions These findings suggest that background speech babble at 0-dB signal-to-noise ratio disrupts novel word learning in preschool-age children. As a result, children may need more time and more exposures of a novel word before they can recognize or verbally recall it.

On the Signal-to-Noise Ratio for Boolean Functions

2020 ◽  
Vol E103.A (12) ◽  
pp. 1659-1665
Author(s):  
Yu ZHOU ◽  
Wei ZHAO ◽  
Zhixiong CHEN ◽  
Weiqiong WANG ◽  
Xiaoni DU
Keyword(s):  
Boolean Functions ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

An Over 120dB Dynamic Range Linear Response Single Exposure CMOS Image Sensor with Two-stage Lateral Overflow Integration Trench Capacitors

2020 ◽  
Vol 2020 (7) ◽  
pp. 143-1-143-6 ◽  
Author(s):  
Yasuyuki Fujihara ◽  
Maasa Murata ◽  
Shota Nakayama ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Linear Response ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Signal To Noise ◽  
Single Exposure ◽  
Two Stage ◽  
Noise Ratio ◽  
Maximum Signal

This paper presents a prototype linear response single exposure CMOS image sensor with two-stage lateral overflow integration trench capacitors (LOFITreCs) exhibiting over 120dB dynamic range with 11.4Me- full well capacity (FWC) and maximum signal-to-noise ratio (SNR) of 70dB. The measured SNR at all switching points were over 35dB thanks to the proposed two-stage LOFITreCs.

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