Correlation Analysis and Modeling of EEG – EMG Signal for Startle-Induced Seizures

Pushpa, B R; Najumnissa, D.; Nayak, Dinesh

doi:10.3844/ojbsci.2018.17.23

Cited by 3 publications

(1 citation statement)

References 19 publications

(22 reference statements)

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“…The scalp EEG signal is determined based on the classification and the performance using machine learning algorithms (MLAs). 7,10 Nonlinear parameters such as interquartile range (IQR) are used to characterize the scalp EEG signal into normal and abnormal. 11 Feature extraction is performed based on IMF output.…”

Section: Introductionmentioning

confidence: 99%

Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

Balakrishnan

Hemalatha

Keshav

2018

International Journal of Epilepsy

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Background Epilepsy is a common neurological disorder characterized by seizures and can lead to life-threatening consequences. The electroencephalogram (EEG) is a diagnostic test used to analyze brain activity in various neurological conditions including epilepsy and interpreted by the clinician for appropriate diagnosis. However, the process of EEG analysis for diagnosis can be automated using machine learning algorithms (MLAs) to aid the clinician. The objective of the study was to test different algorithms that could be used for the detection of seizures. Materials and Methods Video EEG (vEEG) was collected from subjects diagnosed to have episodes of seizures. The epilepsy dataset thus obtained was subjected to empirical mode decomposition (EMD) and the signal was decomposed into intrinsic mode functions (IMFs). The first five levels of decomposition were considered for analysis as per the established protocol. Statistical features such as interquartile range (IQR), entropy, and mean absolute deviation (MAD) were extracted from these IMFs. Results In this study, different MLAs such as nearest neighbor (NN), naïve Bayes (NB), and support vector machines (SVMs) were used to distinguish between normal (interictal) and abnormal (ictal) states. The demonstrated accuracy rates were 97.32% for NN, 99.02% for NB, and 93.75% for SVM. Conclusion Based on this accuracy and sensitivity, it may be posited that the NB classifier provides significantly better results for the detection of abnormal signals indicating that MLA can detect the seizure with better accuracy.

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Section: Introductionmentioning

confidence: 99%

Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

Balakrishnan

Hemalatha

Keshav

2018

International Journal of Epilepsy

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Decoding the Correlation Among Leg Muscle and Brain Activations in Different Body Movements

Ramadoss

Rajagopal²,

Dawi

et al. 2022

Fractals

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We investigated the correlation among brain and leg muscle activations by analyzing Electroencephalogram (EEG) and Electromyogram (EMG) signals in different conditions. Twelve subjects performed four tasks, including (1) quarter turns, (2) U-turns, (3) bypass obstacles, and (4) repeating quarter turns and U-turns two times. Then, we quantified the alterations of the complexity of these signals by computing the fractal dimension and sample entropy. The results showed that EEG and EMG signals in the case of the first task are more complex than the second task, in which they are more complex than the third task. Furthermore, the brain and muscle signals show the least complexity in the case of the fourth task. Moreover, we found strong correlations in the variations of fractal dimension ([Formula: see text]) and sample entropy ([Formula: see text]) between EEG and EMG signals in various tasks. Therefore, brain and muscle activations are strongly correlated in different tasks. Similar analyses can be conducted in the case of other organs to decode their correlations.

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Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation

Soundirarajan

Kuča

Krejcar

et al. 2022

THC

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BACKGROUND: Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science. OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy. METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals. RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals. CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.

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Correlation Analysis and Modeling of EEG – EMG Signal for Startle-Induced Seizures

Cited by 3 publications

References 19 publications

Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

Decoding the Correlation Among Leg Muscle and Brain Activations in Different Body Movements

Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation

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