scholarly journals A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Epileptic Seizures ◽  
Seizure Detection ◽  
New Approach ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
The Time Domain ◽  
Teager Energy ◽  
The Brain

A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.

scholarly journals Comparison of Baseline Cepstral Vector and Composite Vectors in the Automatic Seizure Detection Using Probabilistic Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Probabilistic Neural Network ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Classification Problems ◽  
Dynamic Features ◽  
Cepstral Coefficients ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
Electroencephalogram Eeg ◽  
The Brain

Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.

scholarly journals Fronto-Temporal Analysis of EEG Signals of Patients with Depression: Characterisation, Nonlinear Dynamics and Surrogate Analysis

2020 ◽  
Author(s):  
Subha D. Puthankattil
Keyword(s):  
Time Series ◽  
Unipolar Depression ◽  
Temporal Analysis ◽  
Mental States ◽  
Eeg Signals ◽  
Biosignal Analysis ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
The Brain

The recent advances in signal processing techniques have enabled the analysis of biosignals from brain so as to enhance the predictive capability of mental states. Biosignal analysis has been successfully used to characterise EEG signals of unipolar depression patients. Methods of characterisation of EEG signals and the use of nonlinear parameters are the major highlights of this chapter. Bipolar frontopolar-temporal EEG recordings obtained under eyes open and eyes closed conditions are used for the analysis. A discussion on the reliability of the use of energy distribution and Relative Wavelet Energy calculations for distinguishing unipolar depression patients from healthy controls is presented. The potential of the application of Wavelet Entropy to differentiate states of the brain under normal and pathologic condition is introduced. Details are given on the suitability of ascertaining certain nonlinear indices on the feature extraction, assuming the time series to be highly nonlinear. The assumption of nonlinearity of the measured EEG time series is further verified using surrogate analysis. The studies discussed in this chapter indicate lower values of nonlinear measures for patients. The higher values of signal energy associated with the delta bands of depression patients in the lower frequency range are regarded as a major characteristic indicative of a state of depression. The chapter concludes by presenting the important results in this direction that may lead to better insight on the brain activity and cognitive processes. These measures are hence posited to be potential biomarkers for the detection of depression.

scholarly journals Shifts in broadband power and alpha peak frequency observed during long-term isolation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jan Weber ◽  
Timo Klein ◽  
Vera Abeln
Keyword(s):  
Sensory Deprivation ◽  
Peak Frequency ◽  
Neuronal Population ◽  
Confined Space ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Induced Changes ◽  
Alpha Peak

Abstract Prolonged periods of social isolation and spatial confinement do not only represent an issue that needs to be faced by a few astronauts during space missions, but can affect all of us as recently shown during pandemic situations. The fundamental question, how the brain adapts to periods of sensory deprivation and re-adapts to normality, has only received little attention. Here, we use eyes closed and eyes open resting-state electroencephalographic (EEG) recordings to investigate how neural activity is altered during 120 days of isolation in a spatially confined, space-analogue environment. After disentangling oscillatory patterns from 1/f activity, we show that isolation leads to a reduction in broadband power and a flattening of the 1/f spectral slope. Beyond that, we observed a reduction in alpha peak frequency during isolation, but did not find strong evidence for isolation-induced changes that are of oscillatory nature. Critically, all effects reversed upon release from isolation. These findings suggest that isolation and concomitant sensory deprivation lead to an enhanced cortical deactivation which might be explained by a reduction in the mean neuronal population firing rate.

Unique Contributions of Brain Stimulation to the Study of Consciousness: Where Neuroscience Meets Philosophy

CNS Spectrums ◽  
2010 ◽  
Vol 15 (3) ◽  
pp. 154-156
Author(s):  
Stefano Pallanti
Keyword(s):  
Ancient Greek ◽  
Eyes Closed ◽  
Technical Procedure ◽  
Eyes Open ◽  
Definition Of ◽  
The Individual ◽  
The Brain ◽  
Integrated Information Theory ◽  
Field Of Inquiry ◽  
Integrated Information

True progress in understanding how experience arises from the brain has been relatively slow when viewed from a historical perspective. Recently, several technologies to study and stimulate the brain have been applied to this field of inquiry. Such progress was made only 2,500 years after the ancient Greek philosopher Parmenides first adopted a technical procedure involving the application of formal logic instruments to explore the perception of experiences.At the phenomenological level, consciousness has been referred to as “what vanishes every night when we fall into dreamless sleep and reappears when we wake up or when we dream. It is also all we are and all we have: lose consciousness and, as far as you are concerned, your own self, and the entire world dissolves into nothingness”. According to the integrated information theory, consciousness is integrated information.The term “consciousness” therefore has two key senses: wakefulness and awareness. Wakefulness is a state of consciousness distinguished from coma or sleep. Having one's eyes open is generally an indication of wakefulness and we usually assume that anyone who is awake will also be aware. Awareness implies not merely being conscious but also being conscious of something. The broad definition of consciousness includes a large range of processes that we normally regard as unconscious (eg, blindsight or priming by neglected or masked stimuli).Both sleep and anesthesia are reversible states of eyes-closed unresponsiveness to environmental stimuli in which the individual lacks both wakefulness and awareness. In contrast to sleep, where sufficient stimulation will return the individual to wakefulness, even the most vigorous exogenous stimulation cannot produce awakening in a patient under an adequate level of general anesthesia.

Nonlinear Dynamic Analysis of EEG Using a Stochastic Duffing-van der Pol Oscillator Model

2014 ◽  
Author(s):  
Parham Ghorbanian ◽  
Subramanian Ramakrishnan ◽  
Alan Whitman ◽  
Hashem Ashrafiuon
Keyword(s):  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Analysis ◽  
Van Der Pol Oscillator ◽  
Eeg Signals ◽  
Van Der Pol ◽  
Modeling And Analysis ◽  
Eyes Closed ◽  
Nonlinear Dynamic Characteristics ◽  
Eyes Open ◽  
Eeg Recordings

In this work, we model electroencephalography (EEG) signals as the stochastic output of a double Duffing - van der Pol oscillator networks. We develop a novel optimization scheme to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions and derive models with outputs that show very good agreement with EEG signals in terms of both frequency and information contents. The results, reinforced by statistical analysis, shows that the EEG recordings under EC and EO resting conditions are distinct realizations of the same underlying model occurring due to parameter variations. Furthermore, the EC and EO EEG signals each exhibit distinct nonlinear dynamic characteristics. In summary, it is established that the stochastic coupled nonlinear oscillator network can provide a useful framework for modeling and analysis of EEG signals that are recorded under variety of conditions.

scholarly journals Assessment of changes in the electrical activity of the brain during general anesthesia using portable electroencephalography

2020 ◽  
Vol 49 (2) ◽  
Author(s):  
Verónica Gaviria García ◽  
Daniel Loaiza López ◽  
Carolina Serna Rojas ◽  
Sara Ríos Arismendy ◽  
Eduardo Montoya Guevara ◽  
...  
Keyword(s):  
Electrical Activity ◽  
General Anesthesia ◽  
Brain Activity ◽  
Low Cost ◽  
Conscious State ◽  
Depth Of Anesthesia ◽  
Cross Sectional ◽  
Eyes Closed ◽  
Eeg Recordings ◽  
The Brain

Introduction: The analysis of the electrical activity of the brain using scalp electrodes with electroencephalography (EEG) could reveal the depth of anesthesia of a patient during surgery. However, conventional EEG equipment, due to its price and size, are not a practical option for the operating room and the commercial units used in surgery do not provide access to the electrical activity. The availability of low-cost portable technologies could provide for further research on the brain activity under general anesthesia and facilitate our quest for new markers of depth of anesthesia. Objective: To assess the capabilities of a portable EEG technology to capture brain rhythms associated with the state of consciousness and the general anesthesia status of surgical patients anesthetized with propofol. Methods: Observational, cross-sectional trial that reviewed 10 EEG recordings captured using OpenBCI portable low-cost technology, in female patients undergoing general anesthesia with propofol. The signal from the frontal electrodes was analyzed with spectral analysis and the results were compared against the reports in the literature. Results: The signal captured with frontal electrodes, particularly α rhythm, enabled the distinction between resting with eyes closed and with eyes opened in a conscious state, and sustained anesthesia during surgery. Conclusions: It is possible to differentiate a resting state from sustained anesthesia, replicating previous findings with conventional technologies. These results pave the way to the use of portable technologies such as the OpenBCI tool, to explore the brain dynamics during anesthesia.

Automatic epileptic seizure detection using LSTM networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kishori Sudhir Shekokar ◽  
Shweta Dour
Keyword(s):  
Short Term Memory ◽  
Detection System ◽  
Research Work ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Content Type ◽  
Memory Network ◽  
The Common ◽  
Long Short Term Memory ◽  
The Brain

Purpose The purpose of this work is to make a computer aided detection system for epileptic seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more unprovoked seizures. The common and significant tool for aiding in the identification of epilepsy is Electroencephalography (EEG). The EEG signals contain information about the electrical activity of the brain. Conventionally, clinicians study the EEG waveforms manually to detect epileptic abnormalities, which is very time-consuming and error-prone. Design/methodology/approach The authors have presented a three-layer long short-term memory network for the detection of epileptic seizures. Findings The network classifies between seizure and non-seizure with 99.5% accuracy only in 30 epochs. This makes the proposed methodology useful for real-time seizure detection. Originality/value This research work is original and not plagiarized.

CAN WE MEASURE CONSCIOUSNESS WITH EEG COMPLEXITIES?

2003 ◽  
Vol 13 (03) ◽  
pp. 733-742 ◽  
Author(s):  
FANJI GU ◽  
XIN MENG ◽  
ENHUA SHEN ◽  
ZHIJIE CAI
Keyword(s):  
Mutual Information ◽  
Epileptic Seizures ◽  
Approximate Entropy ◽  
Deep Sleep ◽  
Complexity Measures ◽  
Eyes Closed ◽  
Significant Difference ◽  
Eyes Open ◽  
Brain States ◽  
Light Sleep

Several complexity measures, especially approximate entropy (ApEn) and a new defined complexity measure [Formula: see text], of EEG signals or the ones of the mutual information transmission between different channels of EEGs were calculated to distinguish different consciousness levels for different brain functional states. All of the measures decreased with the following order of brain states: rest with eyes open, eyes closed, light sleep and deep sleep. They decreased during epileptic seizures. On the contrary, the averaged mutual information between different channels increased significantly during the epileptic seizure; there is no significant difference among the averaged mutual information for the subject resting with eyes open, closed, being in light sleep and in deep sleep. Thus, the former indexes seem to be promising candidates to characterize different consciousness levels, while the latter seems not.

scholarly journals Dynamics of brain function in chronic pain patients assessed by microstate analysis of resting-state electroencephalography

2020 ◽  
Author(s):  
Elisabeth S. May ◽  
Cristina Gil Ávila ◽  
Son Ta Dinh ◽  
Henrik Heitmann ◽  
Vanessa D. Hohn ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Resting State ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Relevant Information ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Microstate Analysis

AbstractChronic pain is a highly prevalent and severely disabling disease, which is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of brain activity during the resting-state. However, brain activity varies over time and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography (EEG) data of 101 patients suffering from chronic pain and 88 age- and gender-matched healthy controls. Microstate analysis describes EEG activity as a sequence of a limited number of topographies termed microstates, which remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, described resting-state brain activity in both groups and conditions. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in healthy participants. This difference was consistently found in eyes-open and eyes-closed EEG recordings. No evidence for differences in other microstates was found. As microstate D has been previously related to attentional networks and functions, abnormalities of microstate D might relate to dysfunctional attentional processes in chronic pain. These findings add to the understanding of the pathophysiology of chronic pain and might eventually contribute to the development of an EEG-based biomarker of chronic pain.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

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