ACCURATE DETECTION OF SEIZURE USING NONLINEAR PARAMETERS EXTRACTED FROM EEG SIGNALS

Electroencephalography (EEG) is the graphical recording of electrical activity along the scalp. The EEG signal monitors brain activity noninvasively with a high accuracy of milliseconds and provides valuable discernment about the brain’s state. It is also sensitive in detecting spikes in epilepsy. Computer-aided diagnosis (CAD) tools allow epilepsy to be diagnosed by evading invasive methods. This paper presents a novel CAD system for epilepsy using other linear features together with Hjorth’s nonlinear features such as mobility, complexity, activity and Kolmogorov complexity. The proposed method uses MATLAB software to extract the nonlinear features from the EEG data. The optimal features are selected using the statistical analysis, ANOVA (analysis of variance) test for classification. Once selected, they are fed into the decision tree (DT) for the classification of the different epileptic classes. The proposed method affirms that four nonlinear features, Kolmogorov complexity, singular value decomposition, mobility and permutation entropy are sufficient to provide the highest accuracy of 93%, sensitivity of 97%, specificity of 88% and positive predictive value (PPV) of 94%, with the DT classifier. The mean value is the highest in the ictal stage for the Kolmogorov complexity proving it to have the best variation. It also has the highest [Formula: see text]-value of 300.439 portraying it to be the best parameter that is favourable for the clinical diagnosis of epilepsy, when used together with the DT classifier, for a duration of 23.6[Formula: see text]s of EEG data.

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Classification Algorithms for EEG-Based Brain-Computer Interface

Advances in Medical Technologies and Clinical Practice - Advanced Classification Techniques for Healthcare Analysis ◽

10.4018/978-1-5225-7796-6.ch003 ◽

2019 ◽

pp. 52-73

Author(s):

Sravanth Kumar Ramakuri ◽

Chinmay Chakraboirty ◽

Anudeep Peddi ◽

Bharat Gupta

Keyword(s):

Brain Activity ◽

Biomedical Signal Processing ◽

Computer Interface ◽

Eeg Signals ◽

Large Sets ◽

Eeg Data ◽

Important Measurement ◽

The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

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Classification of Brain Activity Patterns Using Machine Learning Based on EEG Data

2020 1st International Conference Problems of Informatics, Electronics, and Radio Engineering (PIERE) ◽

10.1109/piere51041.2020.9314660 ◽

2020 ◽

Author(s):

Marina S. Murtazina ◽

Tatiana V. Avdeenko

Keyword(s):

Machine Learning ◽

Brain Activity ◽

Activity Patterns ◽

Eeg Data

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Ordinal Pattern Based Complexity Analysis for EEG Activity Evoked by Manual Acupuncture in Healthy Subjects

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127414500187 ◽

2014 ◽

Vol 24 (02) ◽

pp. 1450018 ◽

Cited By ~ 5

Author(s):

Guosheng Yi ◽

Jiang Wang ◽

Kai-Ming Tsang ◽

Wai-Lok Chan ◽

Xile Wei ◽

...

Keyword(s):

Healthy Subjects ◽

Pain Treatment ◽

Complexity Analysis ◽

Brain Activity ◽

Permutation Entropy ◽

Manual Acupuncture ◽

Nonlinear Complexity ◽

Eeg Data ◽

Neurophysiological Mechanisms ◽

Ordinal Patterns

Manual acupuncture (MA) is widely used in Traditional Chinese Medicine clinic for pain treatment and controlling stress. To investigate how MA modulates brain activities, electroencephalograph (EEG) signals are recorded with 20 channels by MA at ST36 of right leg in 11 healthy subjects during rest. Two novel nonlinear measures based on ordinal patterns of EEG series, i.e. permutation entropy (PE) and order index (OI), are adopted to investigate the nonlinear complexity characteristic in EEG data at different acupuncture states. It is observed that the recorded EEG series during and after MA have higher PE values and lower OI values compared to before MA. The results show that MA at ST36 can increase EEG complexity, which is especially obvious during MA. Our findings suggest that the PE and OI measures are promising methods to reveal EEG dynamical changes associated with MA stimulus, which could provide a potential for further exploring the interactions between acupuncture and brain activity. Moreover, these preliminary conclusions highlight the beneficial modulations of brain activity by MA, which could contribute to understanding the acupuncture effects on brain, as well as the neurophysiological mechanisms underlying MA.

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Classification of Brain Activity Patterns from EEG Data Using WEKA

10.1109/itnt52450.2021.9649270 ◽

2021 ◽

Author(s):

Marina Murtazina ◽

Tatiana Avdeenko

Keyword(s):

Brain Activity ◽

Activity Patterns ◽

Eeg Data

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The right thalamus may play an important role in anesthesia-awakening regulation in frogs

PeerJ ◽

10.7717/peerj.4516 ◽

2018 ◽

Vol 6 ◽

pp. e4516 ◽

Cited By ~ 3

Author(s):

Yanzhu Fan ◽

Xizi Yue ◽

Fei Xue ◽

Steven E. Brauth ◽

Yezhong Tang ◽

...

Keyword(s):

General Anesthesia ◽

South African ◽

Brain Activity ◽

Approximate Entropy ◽

Permutation Entropy ◽

Amphibian Species ◽

The Awakening ◽

Eeg Data ◽

The Right

BackgroundPrevious studies have shown that the mammalian thalamus is a key structure for anesthesia-induced unconsciousness and anesthesia-awakening regulation. However, both the dynamic characteristics and probable lateralization of thalamic functioning during anesthesia-awakening regulation are not fully understood, and little is known of the evolutionary basis of the role of the thalamus in anesthesia-awakening regulation.MethodsAn amphibian species, the South African clawed frog (Xenopus laevis) was used in the present study. The frogs were immersed in triciane methanesulfonate (MS-222) for general anesthesia. Electroencephalogram (EEG) signals were recorded continuously from both sides of the telencephalon, diencephalon (thalamus) and mesencephalon during the pre-anesthesia stage, administration stage, recovery stage and post-anesthesia stage. EEG data was analyzed including calculation of approximate entropy (ApEn) and permutation entropy (PE).ResultsBoth ApEn and PE values differed significantly between anesthesia stages, with the highest values occurring during the awakening period and the lowest values during the anesthesia period. There was a significant correlation between the stage durations and ApEn or PE values during anesthesia-awakening cycle primarily for the right diencephalon (right thalamus). ApEn and PE values for females were significantly higher than those for males.DiscussionApEn and PE measurements are suitable for estimating depth of anesthesia and complexity of amphibian brain activity. The right thalamus appears physiologically positioned to play an important role in anesthesia-awakening regulation in frogs indicating an early evolutionary origin of the role of the thalamus in arousal and consciousness in land vertebrates. Sex differences exist in the neural regulation of general anesthesia in frogs.

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

2018 ◽

pp. 429-441

Author(s):

M. Jeyanthi ◽

C. Velayutham

Keyword(s):

Nearest Neighbor ◽

Technology Development ◽

Vital Role ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Data Mining Technique ◽

Data Set ◽

Eeg Data ◽

Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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A novel automated autism spectrum disorder detection system

Complex & Intelligent Systems ◽

10.1007/s40747-021-00408-8 ◽

2021 ◽

Author(s):

Shu Lih Oh ◽

V. Jahmunah ◽

N. Arunkumar ◽

Enas W. Abdulhay ◽

Raj Gururajan ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Developmental Disorder ◽

Detection System ◽

Brain Activity ◽

Limited Range ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Nonlinear Features ◽

Genetic And Environmental Factors ◽

Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

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