scholarly journals Signal complexity indicators of health status in clinical EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kelly Shen ◽  
Alison McFadden ◽  
Anthony R. McIntosh
Keyword(s):  
Health Status ◽  
Neurological Disorders ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Multiscale Entropy ◽  
Brain Signals ◽  
Clinical Biomarker ◽  
Patient Groups ◽  
Signal Complexity ◽  
Clinical Eeg

AbstractBrain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual’s health status and is a promising avenue for clinical biomarker development.

scholarly journals Signal complexity indicators of health status in clinical-EEG

2021 ◽  
Author(s):  
Kelly Shen ◽  
Alison McFadden ◽  
Anthony R. McIntosh
Keyword(s):  
Health Status ◽  
Neurological Disorders ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Multiscale Entropy ◽  
Brain Signals ◽  
Clinical Biomarker ◽  
Health And Disease ◽  
Signal Complexity ◽  
Clinical Eeg

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across individuals in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development.

scholarly journals Complexity matching: brain signals mirror environment information patterns during music listening and reward

2019 ◽  
Author(s):  
Sarah M. Carpentier ◽  
Andrea R. McCulloch ◽  
Tanya M. Brown ◽  
Petra Ritter ◽  
Zhang Wang ◽  
...  
Keyword(s):  
Emotional Experience ◽  
Essential Element ◽  
Human Mind ◽  
Multiscale Entropy ◽  
Complex Signal ◽  
Perceptual Task ◽  
Additional Information ◽  
Brain Signals ◽  
Signal Complexity ◽  
The Brain

AbstractUnderstanding how the human brain integrates information from the environment with ongoing, internal brain signals in order to produce individual perspective is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain (Carpentier et al., 2016), and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence, or functional isomorphism, between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we termed complexity matching. Music makes an ideal stimulus input because it is a multidimensional, complex signal, and because of its emotion and reward-inducing potential. We found that electroencephalography (EEG) complexity was lower and more closely resembled the musical complexity when participants performed a perceptual task that required them to closely track the acoustics, compared to an emotional task that asked them to think about how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire (Mas-Herrero et al., 2013) correlated with worse complexity matching and higher EEG complexity. Compared to perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those in the external stimulus.Significance StatementExperience of our world is combination of the input from the environment, our expectations, and individual responses. For example, the same piece of music can elict happiness in one person and sadness in another. We researched this by measuring the information in pieces of music and whether listener’s brain more closely followed that, or whether additional information was added by the brain. We noted when listener’s were reacting to how music made them feel, their brains added more information and the degree to which this occurred related to how much they find music rewarding. Thus, we were able to provide clues as to how the brain integrates incoming information, adding to it to provide a richer perceptual and emotional experience.

scholarly journals Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward

2020 ◽  
Vol 32 (4) ◽  
pp. 734-745 ◽  
Author(s):  
Sarah M. Carpentier ◽  
Andrea R. McCulloch ◽  
Tanya M. Brown ◽  
Sarah E. M. Faber ◽  
Petra Ritter ◽  
...  
Keyword(s):  
Essential Element ◽  
Emotional Valence ◽  
Human Mind ◽  
Multiscale Entropy ◽  
Music Listening ◽  
Brain Signals ◽  
Multidimensional Signal ◽  
Information Patterns ◽  
Signal Complexity ◽  
The Brain

Understanding how the human brain integrates information from the environment with intrinsic brain signals to produce individual perspectives is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain, and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we term “complexity matching.” Music is an ideal stimulus because it is a multidimensional signal with a rich temporal evolution and because of its emotion- and reward-inducing potential. When participants focused on acoustic features of music, we found that EEG complexity was lower and more closely resembled the musical complexity compared to an emotional task that asked them to monitor how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire correlated with less complexity matching but higher EEG complexity. Compared with perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those linked to the external stimulus. In other words, the brain adds something when judging the emotional valence of music.

scholarly journals The Changes of Ergonomic Engine Vibroacoustic Response Regarding Their Development

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4215
Author(s):  
Radosław Wróbel ◽  
Lech Sitnik ◽  
Monika Andrych-Zalewska ◽  
Łukasz Łoza ◽  
Radostin Dimitrov ◽  
...  
Keyword(s):  
Human Health ◽  
Power Density ◽  
High Speed ◽  
Internal Combustion Engines ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Steering Wheel ◽  
Combustion Engines ◽  
Head Restraint ◽  
Vibroacoustic Response

The article presents the results of research on the vibroacoustic response of internal combustion engines mounted in a vehicle. The vehicles studied belong to popular models, which became available in successive versions. Each group included vehicles of the same model of an older generation (equipped with a naturally aspirated engine) and of a newer generation, including downsized (and turbocharged) engines. Tests in each group were carried out under repeatable conditions on a chassis-load dynamometer. The vibrations were measured using single-axis accelerometers mounted on the steering wheel, engine, and driver’s head restraint mounting. The primary purpose of the study was to verify whether the new generations of vehicles equipped with additional high-speed elements (compressors) generate additional harmonics (especially those within the range potentially affecting travel comfort and human health) and whether there are significant changes in the distribution of spectral power density in the new generations. As the study showed, new generations of vehicles are characterized by a different vibroacoustic response, and the trend of change is the same in each of the families studied.

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

scholarly journals Control and prediction protocol for bearing failure through spectral power density

2020 ◽  
Vol 22 (4) ◽  
pp. 651-657
Author(s):  
Javier Castilla-Gutiérrez ◽  
Juan Carlos Fortes ◽  
Jose Miguel Davila
Keyword(s):  
Power Density ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Bearing Failure

Development and Testing of the UK SF-12

1997 ◽  
Vol 2 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Crispin Jenkinson ◽  
Richard Layte
Keyword(s):  
Health Status ◽  
Short Form ◽  
Community Sample ◽  
Mental Component Summary Score ◽  
Well Being ◽  
Summary Score ◽  
Sf 36 ◽  
Patient Groups ◽  
The Uk ◽  
Component Scores

Objectives: The 36 item short form health survey (SF-36) has proved to be of use in a variety of settings where a short generic health measure of patient-assessed outcome is required. This measure can provide an eight dimension profile of health status, and two summary scores assessing physical function and mental well-being. The developers of the SF-36 in America have developed algorithms to yield the two summary component scores in a questionnaire containing only one-third of the original 36 items, the SF-12. This paper documents the construction of the UK SF-12 summary measures from a large-scale dataset from the UK in which the SF-36, together with other questions on health and lifestyles, was sent to randomly selected members of the population. Using these data we attempt here to replicate the findings of the SF-36 developers in the UK setting, and then to assess the use of SF-12 summary scores in a variety of clinical conditions. Methods: Factor analytical methods were used to derive the weights used to construct the physical and mental component scales from the SF-36. Regression methods were used to weight the 12 items recommended by the developers to construct the SF-12 physical and mental component scores. This analysis was undertaken on a large community sample ( n = 9332), and then the results of the SF-36 and SF-12 were compared across diverse patient groups (Parkinson's disease, congestive heart failure, sleep apnoea, benign prostatic hypertrophy). Results: Factor analysis of the SF-36 produced a two factor solution. The factor loadings were used to weight the physical component summary score (PCS-36) and mental component summary score (MCS-36). Results gained from the use of these measures were compared with results gained from the PCS-12 and MCS-12, and were found to be highly correlated (PCS: ρ = 0.94, p < 0.001; MCS: ρ = 0.96, p < 0.001), and produce remarkably similar results, both in the community sample and across a variety of patient groups. Conclusions: The SF-12 is able to produce the two summary scales originally developed from the SF-36 with considerable accuracy and yet with far less respondent burden. Consequently, the SF-12 may be an instrument of choice where a short generic measure providing summary information on physical and mental health status is required. Crispin Jenkinson DPhil, Deputy Director

BIOELECTRIC ACTIVITY OF THE BRAIN IN PATIENTS WITH ACUTE CEREBROVASCULAR ACCIDENT

Vestnik ◽  
2021 ◽  
pp. 29-34
Author(s):  
Д.А. Митрохин ◽  
М.М. Ибрагимов ◽  
Б.Р. Нурмухамбетова ◽  
Н.Ш. Буйракулова ◽  
В.В. Харченко ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Hemorrhagic Stroke ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Cerebral Stroke ◽  
Beta Activity ◽  
Bioelectric Activity ◽  
Early Recovery ◽  
Coherence Index ◽  
The Brain

Значимость биоэлектрической активности головного мозга в оценке функционального состояния нервной системы при цереброваскулярных заболеваниях широко известна. В настоящей работе показана характеристика биоэлектрической активности головного мозга у больных, перенесших острое нарушение мозгового кровообращения. В данной статье приведены данные о том, что у больных в остром и раннем восстановительном периодах церебрального инсульта биоэлектрическая активность головного мозга характеризовалась, в основном, десинхронным и дезорганизованным типами электроэнцефалограммы. Вместе с тем, отмечались, выраженная дельта и тета активность, а также единичные острые волны, спайки, преимущественно в пораженном полушарии головного мозга, реже в контралатеральном полушарии, межполушарная асимметрия, повышение мощности спектров в сторону преобладания медленных волн. Показатели индекса когерентности по всем отведениям были снижены, что свидетельствует о нарушении функциональных межполушарных взаимосвязей. Более значительное повышение индекса когерентности в дельта и тета диапазонах у пациентов, перенесших геморрагический инсульт, может указывать на более грубые межполушарные нарушения, в сравнении с ишемическим инсультом. Результаты исследования относительной спектральной плотности мощности диапазонов показали, что при геморрагическом инсульте отмечена более высокая дельта и бета активность, а также более значительное снижение мощности альфа ритма, в сравнении с ишемическим инсультом. В тоже время, отмечается повышение интегрального индекса диапазона низкочастотной медленно-волновой активности, особенно выраженное у больных с геморрагическим инсультом р<0,05. The significance of bioelectric activity of the brain in assessing the functional state of the nervous system in cerebrovascular diseases is widely known. In this paper, the characteristics of the bioelectric activity of the brain in patients with acute cerebral circulatory disorders are shown. This article presents data that in patients with acute and early recovery periods of cerebral stroke , the bioelectric activity of the brain was characterized mainly by desynchronous and disorganized types of electroencephalogram. At the, same time, pronounced delta and theta activity was noted , as well as single acute waves, spikes, mainly in the affected hemisphere of the brain, less often in the contralateral hemisphere, interhemispheric asymmetry, increased spectral power in the direction of predominance of slow waves. The coherence index values for all leads were reduced, which indicates a violation of functional interhemispheric relationships. A more significant increase in the coherence index in the delta and theta ranges in patients who have had a hemorrhagic stroke may indicate more severe interhemispheric disorders compared to ischemic stroke. The results of the study of the relative spectral power density of the ranges showed, that in hemorrhagic stroke, there was a higher delta and beta activity, as well as a more significant decrease in the power of the alpha rhythm, in comparison with ischemic stroke. At the same time, there is an increase in the integral index of the range of low-frequency slow-wave activity, especially pronounced in patients with hemorrhagic stroke p < 0.05.

scholarly journals The Spectral Power Density and Shadowing Function of a Glacial Microrelief at the Decimetre Scale

1979 ◽  
Vol 23 (89) ◽  
pp. 57-66 ◽  
Author(s):  
J.-P. Benoist
Keyword(s):  
Power Density ◽  
Gaussian Distribution ◽  
Spectral Power ◽  
Spectral Power Density ◽  
Total Length ◽  
Theoretical Function ◽  
Significant Difference ◽  
Longitudinal Profiles ◽  
Non Gaussian

Abstract Longitudinal profiles of roches moutonnées have been measured once every centimetre over a total length of more than 100 m. Only wavelengths in the range 3.6 cm &lt; λ &lt; 40 cm have been kept and analysed. Levels and their slopes have a symmetrical, non-Gaussian distribution. The spectral power density varies roughly as γ 0 ν–n (ν ═ wavenumber ═ 1/λ); n being the same for all the profiles (n ═ 2.36) and γ 0 being dependent on the studied area. No significant difference has been found for the shadowing function of the different studied areas. It differs consistently from Smith’s theoretical function.

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