Approximate Entropy of the Electroencephalogram in Healthy Awake Subjects and Absence Epilepsy Patients

2005 ◽  
Vol 36 (3) ◽  
pp. 188-193 ◽  
Author(s):  
Naoto Burioka ◽  
Germaine Cornélissen ◽  
Yoshihiro Maegaki ◽  
Franz Halberg ◽  
Daniel T. Kaplan ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Approximate Entropy ◽  
Surrogate Data ◽  
Absence Epilepsy ◽  
Eeg Signals ◽  
Eyes Closed ◽  
The Mean ◽  
Electroencephalogram Eeg

The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (± SD) ApEn in EEG was 0.83 ± 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 ± 0.05) than during seizure-free intervals (0.80 ± 0.13) (P<0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.

IMPROVED ARTIFICIAL NEURAL NETWORK FOR EPILEPTIC SEIZURES DETECTION

2021 ◽  
pp. 2150045
Author(s):  
YASMINE BENCHAIB
Keyword(s):  
Classification Accuracy ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Significant Information ◽  
Relief Algorithm ◽  
Electroencephalogram Eeg ◽  
Interesting Alternative

Electroencephalogram (EEG) is a fundamental and unique tool for exploring human brain activity in general and epileptic mechanism in particular. It offers significant information about epileptic seizures source known as epileptogenic area. However, it is often complicated to detect critical changes in EEG signals by visual examination, since this signal aspect of epileptic persons seems to be normal out of the seizure. Thus, the challenge is to design such a robust and automatic system to detect these unseen changes and use them for diagnosis. In this research, we apply the Artificial Metaplasticity Multi-Layer Perceptron (AMMLP) together with discrete wavelet transform (DWT) to Bonn EEG signals for seizure detection goal. Significant features were then extracted from the well-known EEG brainwaves. Aiming to decrease the computational time and improve classification accuracy, we performed a features ranking and selection employing the Relief algorithm. The obtained AMMLP classification accuracy of 98.97% proved the effctiveness of the applied approach. Our results were compared to recent researches results on the same database, proving to be superior or at least an interesting alternative for seizures detection within EEG signals.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Cognitive Analytics for Rapid Stress Relief in Humans Using EEG Based Analysis of Tratak Sadhana (Meditation)

2020 ◽  
Vol 10 (4) ◽  
pp. 1-20
Author(s):  
Swati Kamthekar ◽  
Prachi Deshpande ◽  
Brijesh Iyer
Keyword(s):  
Big Data ◽  
Fractal Dimensions ◽  
Approximate Entropy ◽  
Stress Relief ◽  
Eeg Signals ◽  
Nonlinear Parameters ◽  
Wavelet Entropy ◽  
Rest State ◽  
Eyes Closed ◽  
The Brain

The article reports the effect of Tratak Sadhana (meditation) on humans using electroencephalograph (EEG) signals. EEG represents the brain activities in the form of electrical signals. Due to non-stationary nature of the EEG signals, nonlinear parameters like approximate entropy, wavelet entropy and Higuchi' fractal dimensions are used to assess the variations in EEG rest as well as during Tratak Sadhana, i.e. at a rest state with eyes closed and during Tratak meditation. EEG signals are captured using EPOC Emotive EEG sensor. The sensor has 14 electrodes covering human scalp. Results shows that new practitioners can also achieve a rapid meditative state as compared to other meditation techniques. Further, the Big Data perspective of the present study is discussed. The present study shows that Tratak Sadhana meditation is an effective tool for rapid stress relief in humans.

scholarly journals Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Yongshao Ren ◽  
Duan Li ◽  
Logan Voss ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Energy Operator ◽  
Burst Suppression ◽  
Multiple Comparison ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Nonlinear Energy ◽  
Selection Of

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

SYNCHRONIZATION MEASURES OF THE SCALP ELECTROENCEPHALOGRAM CAN DISCRIMINATE HEALTHY FROM ALZHEIMER'S SUBJECTS

2007 ◽  
Vol 17 (02) ◽  
pp. 61-69 ◽  
Author(s):  
MARK A. KRAMER ◽  
FEN-LEI CHANG ◽  
MAURICE E. COHEN ◽  
DONNA HUDSON ◽  
ANDREW J. SZERI
Keyword(s):  
Cognitive Impairment ◽  
Healthy Subjects ◽  
Phase Synchronization ◽  
Eyes Closed ◽  
Eeg Data ◽  
Two Measures ◽  
Left And Right ◽  
Scalp Electroencephalogram ◽  
Occipital Lobes ◽  
Electroencephalogram Eeg

Three synchronization measures are applied to scalp electroencephalogram (EEG) data collected from 20 patients diagnosed to have either: (1) no dementia, (2) mild cognitive impairment (MCI), or (3) Alzheimer's disease (AD). We apply the three synchronization measures — the phase synchronization, and two measures of nonlinear interdependency — to the data collected from awake patients resting with eyes closed. We show that the synchronization in potential between electrodes near the left and right occipital lobes provides a statistically significant discriminant between the healthy and AD subjects, and the MCI and AD subjects. None of the three measures appears able to distinguish between the healthy and MCI subjects, although MCI subjects show synchronization values intermediate between healthy subjects (with high synchronization values) and AD subjects (with low synchronization values) on average.

scholarly journals Special Issue on Brain Machine/Computer Interface and its Application

2020 ◽  
Vol 32 (4) ◽  
pp. 723-723
Author(s):  
Shoichiro Fujisawa ◽  
Minoru Fukumi ◽  
Jianting Cao ◽  
Yasue Mitsukura ◽  
Shin-ichi Ito
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Eeg Analysis ◽  
Special Issue ◽  
Eeg Signals ◽  
Auditory P300 ◽  
Environment Control ◽  
Electroencephalogram Eeg ◽  
Human Support

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games. This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model. We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

scholarly journals Person authentication based on eye-closed and visual stimulation using EEG signals

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hui Yen Yap ◽  
Yun-Huoy Choo ◽  
Zeratul Izzah Mohd Yusoh ◽  
Wee How Khoh
Keyword(s):  
User Satisfaction ◽  
Visual Stimulation ◽  
Support Vector ◽  
Biometric System ◽  
Eeg Signals ◽  
Pairwise Correlation ◽  
Eyes Closed ◽  
Recording Process ◽  
Electroencephalogram Eeg ◽  
Accuracy Performance

AbstractThe study of Electroencephalogram (EEG)-based biometric has gained the attention of researchers due to the neurons’ unique electrical activity representation of an individual. However, the practical application of EEG-based biometrics is not currently widespread and there are some challenges to its implementation. Nowadays, the evaluation of a biometric system is user driven. Usability is one of the concerning issues that determine the success of the system. The basic elements of the usability of a biometric system are effectiveness, efficiency and user satisfaction. Apart from the mandatory consideration of the biometric system’s performance, users also need an easy-to-use and easy-to-learn authentication system. Thus, to satisfy these user requirements, this paper proposes a reasonable acquisition period and employs a consumer-grade EEG device to authenticate an individual to identify the performances of two acquisition protocols: eyes-closed (EC) and visual stimulation. A self-collected database of eight subjects was utilized in the analysis. The recording process was divided into two sessions, which were the morning and afternoon sessions. In each session, the subject was requested to perform two different tasks: EC and visual stimulation. The pairwise correlation of the preprocessed EEG signals of each electrode channel was determined and a feature vector was formed. Support vector machine (SVM) was then used for classification purposes. In the performance analysis, promising results were obtained, where EC protocol achieved an accuracy performance of 83.70–96.42% while visual stimulation protocol attained an accuracy performance of 87.64–99.06%. These results have demonstrated the feasibility and reliability of our acquisition protocols with consumer-grade EEG devices.

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

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.

Detection of crossover points in detrended fluctuation analysis: An application to EEG signals of patients with epilepsy

2020 ◽  
Author(s):  
Victor Barreto Mesquita ◽  
Florêncio Mendes Oliveira Filho ◽  
Paulo Canas Rodrigues
Keyword(s):  
Long Range ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Research Direction ◽  
R Package ◽  
Supplementary Information ◽  
Fluctuation Analysis ◽  
Eeg Signals ◽  
Amplitude Function ◽  
Epileptic Patients

Abstract Motivation The quantification of long-range correlation of electroencephalogram (EEG) signals is an important research direction for its relevance in helping understanding the brain activity. Epileptic seizures have been studied in the past years where different non-linear statistical approaches have been employed to understand the relationship between the EEG signal and the epilepitc discharge. One of the most widely used method for to analyse long memory processes is the detrended fuctuation analysis (DFA). However, no objective and pragmatic methods have been developed to detect crossover points and reference channels in DFA. Results In this paper, we propose: (i) two automatic approaches that successfully detect crossover points in DFA related methods on the log-log plot; and (ii) a criteria to choose the reference channel for the log-amplitude function. Moreover, the DFA is applied to EEG signals of 10 epileptic patients collected from the CHB-MIT database, being the log-amplitude function used to compare the different brain hemispheres by making use of the methodology proposed in the paper. The existence of long-range power-law correlations is demonstrated and indicates that the EEG signals of epileptic patients present three well defined regions with the first region showing a 1/f 1/f noise (pink noise) for seven subjects and a random walk behaviour for three subjects. The second and third regions show anti-persistence behaviour. Moreover, the results of the log-amplitude function were divided in two groups: (i) the first, including seven subjects, where the increase in the scales results in an increase in the fluctuation in the the frontal channels; and (ii) the second, included three subjects, where the fluctuation for large scales are greater for the parietal channels. Availability The functions used in this paper are available in the R package DFA (Mesquita et al., 2020). Supplementary information Supplementary information are available at Bioinformatics online.

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