Glial Modulation of Electrical Rhythms in a Neuroglial Network Model of Epilepsy

Grigorovsky, Vasily; Breton, Vanessa L.; Bardakjian, Berj L.

doi:10.1109/tbme.2020.3022332

Cited by 8 publications

(19 citation statements)

References 57 publications

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“…In light of this assessment and figure 18, it very well may be seen that the proposed model is 3.8% exact than TTFC [4], 4.95% exact than NNM [5], and 2.9% exact than LBP TH [9] for various EEG signal sorts. In view of this assessment and figure 19, it tends to be seen that the proposed model has 5.3% more review than TTFC [4], 3.9% more review than NNM [5], and 4.5% more review than LBP TH [9] for various EEG signal sorts.…”

Section: Results Analysis and Comparisonmentioning

confidence: 87%

“…This presentation was accomplished because of mix of MFCC and iVector highlights, and utilization of Multiple Neural Networks for profoundly proficient characterization process. Also, accuracy execution of these models is classified in table2 as follows, In light of this assessment and figure 17, it was seen that the proposed model has 5.2% more exact than TTFC [4], 4.3% more precision than NNM [5], and 6.75% more exactness than LBP TH [9] for various kinds of EEG signals.…”

Section: Results Analysis and Comparisonmentioning

confidence: 98%

“…For this assessment, all out 5000 unique sections were separated from this dataset, and were partitioned into 60:40 proportion for preparing and testing individually. Results were assessed regarding exactness, accuracy, review and deferral, and were contrasted and TTFC [4], NNM [5], and LBP TH [9] for approval purposes. The outcomes for exactness can be seen from table 1 as follows,…”

Section: Results Analysis and Comparisonmentioning

confidence: 99%

“…Because of which the proposed model is fit for organization in high-exactness clinical conditions. It was seen that the proposed model had more than 5% more precision than TTFC [4], more than 4% more exactness than NNM [5], and more than 6% more precision than LBP TH [9] for various sorts of EEG signals. Comparable execution was noticed for accuracy and review, which makes the model profoundly deployable for a wide assortment of EEG grouping situations.…”

Section: Conclusion and Future Scopementioning

confidence: 97%

“…These models have incredible precision, yet need terms of exactness execution due to their application-unequivocal portrayal characteristics. Developments to this model are discussed in [5,6], wherein Neuroglial Network Model (NNM), and low-force trotted ultrasound energy (LIFUS) are used for multidomain EEG groupings. These models have extraordinary exactness, yet can't be scaled for a long while on account of high computational multifaceted design.…”

Section: Literature Reviewmentioning

confidence: 99%

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Design of an improved soft computing model for EEG classification using MFCC and quad classifier

Kala¹,

Mishra²,

Dubey

2022

ijhs

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Electroencephalography (EEG) signals are a combination of complex pattern sequences,that are periodic in nature. These pattern sequences include a gamma waves that indicates deep thinking behaviour, a beta wave sequence that indicates busy and active mind status, an alpha wave segment which indicates reflective and restful behaviour, a theta wave which is an indicative of drowsiness, and a delta wave which indicates sleeping & dreaming conditions. Features like frequency changes, amplitude changes, pattern changes, etc. are used to identify chronic, ischemic and other diseases related to the brain. In order to classify these wave patterns into brain diseases like epilepsy, a series of high complexity signal processing operations are needed to be executed in tandem. These operations include signal pre-processing, feature extraction, feature selection, classification into epileptic & non-epileptic seizure and post-processing.A large variety of algorithms are developed by researchers for each of these operations. Performance of these algorithms varies largely w.r.t. the number of leads used for EEG capture, filtering efficiency, feature extraction & selection efficiency, and classifier efficiency.Thus, it becomes ambiguous for researchers and system designers to select the best possible algorithm set for their application. the proposed AMVAFEx model uses Multiple Neural Networks for definite EEG grouping.

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