Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Esmaeilpour, Mansour; Mohammadi, Ali Reis Ali

doi:10.9781/ijimai.2016.422

Cited by 14 publications

(9 citation statements)

References 12 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, we maybe use these novel operators to solve new MCDM problems in practice, such as industrial site selection [46], intrusion detection [47], EEG signals analysis [48], consensus model [49], temporary disaster debris Management [50], etc. The limitation of proposed operators is that if we consider interrelationships among more than three criteria, more parameters will be given, which may bewilder experts, such that they perhaps cannot decide how to choose the values of parameters.…”

Section: Discussionmentioning

confidence: 99%

Multicriteria Decision Making Based on Generalized Maclaurin Symmetric Means with Multi-Hesitant Fuzzy Linguistic Information

Лю

Gao

2018

Symmetry

View full text Add to dashboard Cite

Abstract:In multicriteria decision making (MCDM), multi-hesitant fuzzy linguistic term sets (MHFLTSs) can eliminate the limitations of hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic sets (HFLSs), and emphasize the importance of a repeated linguistic term (LT). Meanwhile, there is usually an interrelation between criteria. The Maclaurin symmetric mean (MSM) operator can capture the interrelationships among multi-input arguments. The purpose of this paper is to integrate MHFLTSs with MSM operators and to solve MCDM problems. Firstly, we develop the generalized MSM operator for MHFLTSs (MHFLGMSM), the generalized geometric MSM operator for MHFLTSs (MHFLGGMSM), the weighted generalized MSM operator for MHFLTSs (WMHFLGMSM) and the weighted generalized geometric MSM operator for MHFLTSs (WMHFLGGMSM), respectively. Then, we discuss their properties and some special cases. Further, we present a novel method to deal with MCDM problems with the MHFLTSs based on the proposed MSM operators. Finally, an illustrative example about how to select the best third-party logistics service provider is supplied to demonstrate the practicality and reliability of the proposed approaches in comparison with some existing approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Multicriteria Decision Making Based on Generalized Maclaurin Symmetric Means with Multi-Hesitant Fuzzy Linguistic Information

Лю

Gao

2018

Symmetry

View full text Add to dashboard Cite

show abstract

“…Recorded in electroencephalograms, these tests register the alternations that the brain produces due to its electrical activity over time. It has been widely used in the study and treatment of different clinical problems and diseases, such as diagnosis of epilepsy [15], depth of anaesthesia [44], or sleep disorders [11].…”

Section: Electroencephalogramsmentioning

confidence: 99%

Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring

Fernandez-Blanco¹,

Fernández-Lozano²,

Rivero³

2021

Preprint

View full text Add to dashboard Cite

Over the years, several approaches have tried to tackle the problem of performing an automatic scoring of the sleeping stages. Although any polysomnography usually collects over a dozen of different signals, this particular problem has been mainly tackled by using only the Electroencephalograms presented in those records. On the other hand, the other recorded signals have been mainly ignored by most works. This paper explores and compares the convenience of using additional signals apart from the electroencephalograms. More specifically, this work uses the SHHS-1 dataset with 5,804 patient which contains an electromyogram recorded at the same time as two electroencephalograms.To compare the results, first, it has been evaluated the same architecture with different input signals and all their possible combinations. These tests show how, using more than one signal especially if they are from different sources, improves the results of the classification. Additionally, the best models obtained for each combination of one or more signals have been used in ensemble models and, its performance has been compared showing the convenience of using these multi-signal models to improve the classification. The best overall model, an ensemble of Depth-wise Separational Convolutional Neural Networks, has achieved an accuracy of 86.06% with a Cohen's Kappa of 0.80 and a F 1 of 0.77. Up to date, those are the best results on the complete dataset and it shows a significant improvement in the precision and recall for the most uncommon class in the dataset.

show abstract

“…Bipolar fuzzy sets [7]-a generalization of Zadeh's idea of the fuzzy set [8] which itself expands the classical set-are sets whose elements have positive and negative membership degrees. Hybrid models of fuzzy sets have been applied in many different sciences [9][10][11][12]. The first definition of fuzzy ideals in BCK/BCI-algebras was by Xi [13] in 1991.…”

Section: Introductionmentioning

confidence: 99%

m-Polar ( α , β ) -Fuzzy Ideals in BCK/BCI-Algebras

Al-Masarwah

Ahmad

2019

Symmetry

View full text Add to dashboard Cite

Multi-polar vagueness in data plays a prominent role in several areas of the sciences. In recent years, the thought of m-polar fuzzy sets has captured the attention of numerous analysts, and research in this area has escalated in the past four years. Hybrid models of fuzzy sets have already been applied to many algebraic structures, such as B C K / B C I -algebras, lie algebras, groups, and symmetric groups. A symmetry of the algebraic structure, mathematically an automorphism, is a mapping of the algebraic structure onto itself that preserves the structure. This paper focuses on combining the concepts of m-polar fuzzy sets and m-polar fuzzy points to introduce a new notion called m-polar ( α , β ) -fuzzy ideals in B C K / B C I -algebras. The defined notion is a generalization of fuzzy ideals, bipolar fuzzy ideals, ( α , β ) -fuzzy ideals, and bipolar ( α , β ) -fuzzy ideals in B C K / B C I -algebras. We describe the characterization of m-polar ( ∈ , ∈ ∨ q ) -fuzzy ideals in B C K / B C I -algebras by level cut subsets. Moreover, we define m-polar ( ∈ , ∈ ∨ q ) -fuzzy commutative ideals and explore some pertinent properties.

show abstract

Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Cited by 14 publications

References 12 publications

Multicriteria Decision Making Based on Generalized Maclaurin Symmetric Means with Multi-Hesitant Fuzzy Linguistic Information

Multicriteria Decision Making Based on Generalized Maclaurin Symmetric Means with Multi-Hesitant Fuzzy Linguistic Information

Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring

m-Polar ( α , β ) -Fuzzy Ideals in BCK/BCI-Algebras

Contact Info

Product

Resources

About