Nonlinear analysis of scalp EEGs from normal and brain tumour subjects
-
Salai Selvam V.
and
Shenbaga Devi S.
Abstract
Measurement of features from the chaos theory or as popularly known, the concept of nonlinear dynamics, as indicatives of several pathological conditions and cognition states using the electroencephalography (EEG) signal is very popular. In this paper, the analysis of scalp EEG signals of normal subjects and brain tumour patients using the nonlinear dynamic features has been presented. The nonlinear dynamic features that represent the dimensional and waveform complexities of the signal being analyzed have been considered. The statistical analysis of the selected nonlinear dynamic features has been presented. The results show that the nonlinear dynamic features significantly discriminate the brain tumour group from the normal group.
Acknowledgment
Authors would like to express their gratitude to the Institute of Neurology at the Madras Medical College, Chennai, India for providing the data for the research work.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Nonlinear analysis of scalp EEGs from normal and brain tumour subjects
- Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
- EEG signal classification based on SVM with improved squirrel search algorithm
- The effect of attentional focusing strategies on EMG-based classification
- Identification of dental pain sensation based on cardiorespiratory signals
- ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance
- A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches
- Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation
- Raspberry Pi implemented with MATLAB simulation and communication of physiological signal-based fast chaff point (RPSC) generation algorithm for WBAN systems
- Short Communication
- How yarn orientation limits fibrotic tissue ingrowth in a woven polyester heart valve scaffold: a case report
Articles in the same Issue
- Frontmatter
- Research Articles
- Nonlinear analysis of scalp EEGs from normal and brain tumour subjects
- Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
- EEG signal classification based on SVM with improved squirrel search algorithm
- The effect of attentional focusing strategies on EMG-based classification
- Identification of dental pain sensation based on cardiorespiratory signals
- ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance
- A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches
- Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation
- Raspberry Pi implemented with MATLAB simulation and communication of physiological signal-based fast chaff point (RPSC) generation algorithm for WBAN systems
- Short Communication
- How yarn orientation limits fibrotic tissue ingrowth in a woven polyester heart valve scaffold: a case report
