Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Dynamic Behaviour ◽  
Joint Probability ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Channel System ◽  
Markov Network ◽  
Eeg Data ◽  
Multi Level ◽  
Joint Probability Functions ◽  
Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

scholarly journals QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

Fractals ◽  
2009 ◽  
Vol 17 (04) ◽  
pp. 473-483
Author(s):  
BEHZAD AHMADI ◽  
BAHAREH ZAGHARI ◽  
RASSOUL AMIRFATTAHI ◽  
MOJTABA MANSOURI
Keyword(s):  
Correlation Dimension ◽  
Single Channel ◽  
Drug Effects ◽  
Eeg Signal ◽  
Depth Of Anesthesia ◽  
Anesthetic Drugs ◽  
Eeg Data ◽  
Prediction Probability ◽  
Analysis Application ◽  
Electroencephalogram Eeg

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

scholarly journals Stochastic Modeling of Inter & Intra Stage Dependent Cancer Growth under Chemotherapy through Trivariate Poisson Processes

2014 ◽  
Vol 10 ◽  
pp. 31-44
Author(s):  
P. Tirupathi Rao ◽  
B.N. Naveen Kumar ◽  
J. Jayabharathiraj
Keyword(s):  
Mutant Cell ◽  
Three Dimensional ◽  
Joint Probability ◽  
Numerical Data ◽  
Malignant Cell ◽  
Process Approach ◽  
Cancer Growth ◽  
Cell Mutant ◽  
Statistical Measures ◽  
Joint Probability Functions

This study has proposed a Stochastic Model for cancer growth under chemotherapy with the assumptions of the growth, transition and loss parameters of different stages are inter and intra dependent. A trivariate Poisson process approach has been adopted for modeling the three stage cancer growth by considering the stages of cells in the tumor namely normal cell, mutant cell and malignant cell in the presence and absence of chemotherapy during time 't'. Stochastic differential equations were obtained and the three dimensional joint probability functions along with related statistical measures were derived. Model behavior was analysed through numerical data.

scholarly journals Publisher Correction: CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Hasun Yu ◽  
Jinmyung Jung ◽  
Seyeol Yoon ◽  
Mijin Kwon ◽  
Sunghwa Bae ◽  
...  
Keyword(s):  
Drug Effects ◽  
Multi Level

scholarly journals Pharmacological evidence for the anticonvulsant activity of Tylophora indica in experimental animal models

2017 ◽  
Vol 6 (4) ◽  
pp. 750 ◽  
Author(s):  
Hafis TK ◽  
Shyamjith Manikkoth ◽  
Melinda Sequeira ◽  
Roopa P. Nayak
Keyword(s):  
Hind Limb ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Ethanolic Extract ◽  
Therapeutic Index ◽  
Albino Rats ◽  
Anticonvulsant Effect ◽  
Tylophora Indica ◽  
High Therapeutic Index ◽  
Wistar Albino Rats

Background: Epilepsy is a common neurological disorder. The antiepileptic drugs currently used are unable to manage seizures effectively and are associated with numerous adverse drug effects. Hence, there is a necessity of a newer anticonvulsant drug with high therapeutic index profile. The objective of this pre-clinical research was to investigate the role of Tylophora indica on Maximal electric shock [MES] and Pentylene tetrazole [PTZ] provoked convulsions in Wistar albino rats.Methods: 36 Wistar albino rats were used for this study, after obtaining ethical clearance. The ethanolic extract of the leaves of Tylophora indica [TIEE] (100 mg/kg, p.o) was used to screen the anticonvulsant effect on MES and PTZ provoked convulsions in Wistar albino rats. In MES seizures, inhibition of the tonic hind limb extension and in PTZ seizures, extent of convulsions was noted.Results: TIEE (100 mg/kg, p.o) significantly (p<0.001) blocked the hind limb extension due to MES. The same dose also significantly (p<0.001) lessened the extent of convulsions induced by PTZ.Conclusions: The data suggests that the ethanolic extract of Tylophora indica leaves produce its anticonvulsant effect via different mechanisms since it prevented the hind limb extension induced by MES and decreased the duration of convulsions produced by PTZ.

scholarly journals CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Hasun Yu ◽  
Jinmyung Jung ◽  
Seyeol Yoon ◽  
Mijin Kwon ◽  
Sunghwa Bae ◽  
...  
Keyword(s):  
Human Body ◽  
Drug Targets ◽  
Drug Effects ◽  
Context Information ◽  
Expression Data ◽  
Multiple Organs ◽  
Disease Associations ◽  
Multi Level ◽  
Context Specific ◽  
Insight Into

Abstract In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.

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