Analysis of spectral power distributions for multichannel platforms in a patient room application

2017 ◽  
Author(s):  
James J. Kim ◽  
Patricia Rizzo ◽  
Dorene Maniccia ◽  
Tony Esposito
Keyword(s):  
Spectral Power ◽  
Patient Room

Associations Between Self-Report of Emotional State and the EEG Patterns in Affective Disorders Patients

2010 ◽  
Vol 24 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Jan Kaiser ◽  
Anton M. L. Coenen
Keyword(s):  
State Energy ◽  
Emotional State ◽  
Spectral Power ◽  
Bipolar Affective Disorder ◽  
Affective State ◽  
Manic Episode ◽  
High Energy ◽  
Self Report ◽  
Major Depressive ◽  
High Tension

The study determines the associations between self-report of ongoing emotional state and EEG patterns. A group of 31 hospitalized patients were enrolled with three types of diagnosis: major depressive disorder, manic episode of bipolar affective disorder, and nonaffective patients. The Thayer ADACL checklist, which yields two subjective dimensions, was used for the assessment of affective state: Energy Tiredness (ET) and Tension Calmness (TC). Quantitative analysis of EEG was based on EEG spectral power and laterality coefficient (LC). Only the ET scale showed relationships with the laterality coefficient. The high-energy group showed right shift of activity in frontocentral and posterior areas visible in alpha and beta range, respectively. No effect of ET estimation on prefrontal asymmetry was observed. For the TC scale, an estimation of high tension was related to right prefrontal dominance and right posterior activation in beta1 band. Also, decrease of alpha2 power together with increase of beta2 power was observed over the entire scalp.

Variations Across the Menstrual Cycle in EEG Activity During Thinking and Mental Relaxation

1999 ◽  
Vol 13 (3) ◽  
pp. 163-172 ◽  
Author(s):  
R. Krug ◽  
M. Mölle ◽  
H.L. Fehm ◽  
J. Born
Keyword(s):  
Menstrual Cycle ◽  
Divergent Thinking ◽  
Spectral Power ◽  
Plasma Concentrations ◽  
Task Demands ◽  
Menstrual Phase ◽  
Convergent Thinking ◽  
Eeg Activity ◽  
Dimensional Complexity ◽  
Mental Relaxation

Abstract Previous studies have indicated: (1) peak performance on tests of divergent creative thinking during the ovulatory phase of the menstrual cycle; (2) compared to convergent analytical thinking, divergent thinking was found to be associated with a distinctly increased dimensional complexity of ongoing EEG activity. Based on these findings, we hypothesized that cortical information processing during the ovulatory phase is characterized by an increased EEG dimensionality. Each of 16 women was tested on 3 occasions: during the ovulatory phase, the luteal phase, and menses. Presence of the phases was confirmed by determination of plasma concentrations of estradiol, progesterone, and luteinizing hormone. The EEG was recorded while the women performed: (1) tasks of divergent thinking; (2) tasks of convergent thinking; and (3) during mental relaxation. In addition to EEG dimensional complexity, conventional spectral power analysis was performed. Behavioral data confirmed enhanced creative performance during the ovulatory phase while convergent thinking did not vary across cycle phases. EEG complexity was higher during divergent than convergent thought, but this difference remained unaffected by the menstrual phase. Influences of the menstrual phase on EEG activity were most obvious during mental relaxation. In this condition, women during the ovulatory phase displayed highest EEG dimensionality as compared with the other cycle phases, with this effect being most prominent over the central and parietal cortex. Concurrently, power within the alpha frequency band as well as theta power at frontal and parietal leads were lower during the luteal than ovulatory phase. EEG results indicate that task demands of thinking overrode effects of menstrual cycle. However, with a less demanding situation, an ovulatory increase in EEG dimensionality became prominent suggesting a loosening of associative habits during this phase.

Supercontinuum laser-based monochromatic radiant source for detector spectral responsivity characterization in the range 0.9–1.6 μm using absolute cryogenic radiometer

2020 ◽  
pp. 28-33
Author(s):  
A. Yu. Dunaev ◽  
A. S. Baturin ◽  
V. N. Krutikov ◽  
S. P. Morozova
Keyword(s):  
Power Distribution ◽  
Spectral Power ◽  
Feedback System ◽  
Operating Mode ◽  
Spectral Responsivity ◽  
Spectral Power Distribution ◽  
Cryogenic Radiometer ◽  
Supercontinuum Laser ◽  
Radiant Power ◽  
Double Monochromator

An improved monochromatic radiant source with spectral bandwidth of 4 nm based on supercontinuum laser and a double monochromator was included in absolute cryogenic radiometer-based facility to improve the accuracy of spectral responsivity measurement in the range 0.9–1.6 μm. The developed feedback system ensures stabilization of monochromatic radiant power with standard deviation up to 0.025 %. Radiant power that proceeds detector under test or absolute cryogenic radiometer varies from 0.1 to 1.5 mW in dependence of wavelength. The spectral power distribution of its monochromatic source for various operating mode is presented.

Inflections of Periodicities of Background Photon Spectral Power Densities Are Predicted by the Lorentz Contraction for a Potential Indicator for the Intrinsic Structure of Space

2016 ◽  
pp. 4014-4017
Author(s):  
Michael A Persinger
Keyword(s):  
Spectral Power ◽  
Neutral Hydrogen ◽  
Intrinsic Structure ◽  
Lorentz Contraction ◽  
Intrinsic Nature ◽  
Basic Interval ◽  
Power Spectral ◽  
The Difference ◽  
Background Photon ◽  
Power Densities

                The value for the Lorentz contraction to produce a discrepancy for a hypothetical number that reflects a property (21.3π4) of sub-matter space was calculated. When applied to time the contraction would be ~35 min. The difference in mass-equivalent energy for an electron at c (the velocity of light in a vacuum) and the required v was ~2 ·10-20 J which has emerged as a significant quantity that may permeate from the force at Planck’s Length when applied across the wavelength of the neutral hydrogen line. Two separate types of photomultiplier instruments (digital and analogue) measuring with different sampling rates for background photon quantities over 50 randomly selected days demonstrated averaged conspicuous inflections of standardized spectral power densities around 35 min. This is the same basic interval where microvariations in the value of the gravitational constant (G) approached a limit at which white noise dominated.  The possibility is considered that this value for temporal inflections in photon power spectral densities may reflect the intrinsic nature of space-time contractions that relate gravity and photons.

EEG correlates of cognitive functions and neuropsychiatric disorders: A review of oscillatory activity and neural synchrony abnormalities

2020 ◽  
Vol 16 ◽  
Author(s):  
Meysam Amidfar ◽  
Yong-Ku Kim
Keyword(s):  
Psychiatric Disorders ◽  
Cognitive Functions ◽  
Spectral Power ◽  
Large Body ◽  
Oscillatory Activity ◽  
Brain Oscillations ◽  
Emotional Symptoms ◽  
Obsessive Compulsive ◽  
Neuropsychiatric Diseases ◽  
Obsessive Compulsive Disorders

Background: A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms. Objective: Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for treatment of cognitive impairments. Methods: This review includes description of brain oscillations in Alzheimer’s disease, bipolar disorder, attentiondeficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism. Results: The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations. Conclusion: In this article we examined nature of brain oscillations, association of brain rhythms with cognitive functions and relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

scholarly journals EEG Measures of Value-Directed Strategic Processing in Older Adults With and Without Mild Cognitive Impairment

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 292-293
Author(s):  
Lydia Nguyen ◽  
Shraddha Shende ◽  
Daniel Llano ◽  
Raksha Mudar
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Aging ◽  
Spectral Power ◽  
Group Differences ◽  
Cognitive Resources ◽  
Alpha Power ◽  
Strategic Processing ◽  
Unique Word

Abstract Value-directed strategic processing is important for daily functioning. It allows selective processing of important information and inhibition of irrelevant information. This ability is relatively preserved in normal cognitive aging, but it is unclear if mild cognitive impairment (MCI) affects strategic processing and its underlying neurophysiological mechanisms. The current study examined behavioral and EEG spectral power differences between 16 cognitively normal older adults (CNOA; mean age: 74.5 ± 4.0 years) and 16 individuals with MCI (mean age: 77.1 ± 4.3 years) linked to a value-directed strategic processing task. The task used five unique word lists where words were assigned high- or low-value based on letter case and were presented sequentially while EEG was recorded. Participants were instructed to recall as many words as possible after each list to maximize their score. Results revealed no group differences in recall of low-value words, but individuals with MCI recalled significantly fewer high-value words and total number of words relative to CNOA. Group differences were observed in theta and alpha bands for low-value words, with greater synchronized theta power for CNOA than MCI and greater desynchronized alpha power for MCI than CNOA. Collectively, these findings demonstrate that more effortful neural processing of low-value words in the MCI group, relative to the CNOA group, allowed them to match their behavioral performance to the CNOA group. Individuals with MCI appear to utilize more cognitive resources to inhibit low-value information and might show memory-related benefits if taught strategies to focus on high-value information processing.

scholarly journals Mining Camera Traces to Estimate Interactions Between Healthcare Workers and Patients

2020 ◽  
Vol 41 (S1) ◽  
pp. s12-s12
Author(s):  
D. M. Hasibul Hasan ◽  
Philip Polgreen ◽  
Alberto Segre ◽  
Jacob Simmering ◽  
Sriram Pemmaraju
Keyword(s):  
Time Of Day ◽  
Microsoft Kinect ◽  
Critical Parameters ◽  
Model Parameters ◽  
Patient Room ◽  
University Of Iowa ◽  
Velocity Change ◽  
Fine Grained ◽  
Visit Frequency ◽  
Contact Patterns

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.Funding: NoneDisclosures: None

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