scholarly journals Conditional entropy of ordinal patterns

2014 ◽  
Vol 269 ◽  
pp. 94-102 ◽  
Author(s):  
Anton M. Unakafov ◽  
Karsten Keller
Keyword(s):  
Conditional Entropy ◽  
Ordinal Patterns

scholarly journals A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1092
Author(s):  
Xian Liu ◽  
Zhuang Fu
Keyword(s):  
Conditional Entropy ◽  
Neural Mass Model ◽  
High Temporal Resolution ◽  
Eeg Signals ◽  
Mass Model ◽  
Neural Mass ◽  
Brain Rhythm ◽  
Brain States ◽  
Ordinal Patterns ◽  
Electroencephalogram Eeg

Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.

scholarly journals Change-Point Detection using the Conditional Entropy of Ordinal Patterns

2018 ◽  
Author(s):  
Karsten Keller ◽  
Anton M. Unakafov
Keyword(s):  
Time Series ◽  
Change Point ◽  
Numerical Experiments ◽  
A Priori ◽  
Conditional Entropy ◽  
Change Point Detection ◽  
A Priori Information ◽  
Ordinal Patterns ◽  
Priori Information ◽  
Point Detection

This paper is devoted to change-point detection using only the ordinal structure of a time series. A statistic based on the conditional entropy of ordinal patterns characterizing the local up and down in a time series is introduced and investigated. The statistic requires only minimal a priori information on given data and shows good performance in numerical experiments.

scholarly journals Change-Point Detection Using the Conditional Entropy of Ordinal Patterns

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 709 ◽  
Author(s):  
Anton Unakafov ◽  
Karsten Keller
Keyword(s):  
Time Series ◽  
Change Point ◽  
Numerical Experiments ◽  
A Priori ◽  
Conditional Entropy ◽  
Change Point Detection ◽  
A Priori Information ◽  
Ordinal Patterns ◽  
Priori Information ◽  
Point Detection

This paper is devoted to change-point detection using only the ordinal structure of a time series. A statistic based on the conditional entropy of ordinal patterns characterizing the local up and down in a time series is introduced and investigated. The statistic requires only minimal a priori information on given data and shows good performance in numerical experiments. By the nature of ordinal patterns, the proposed method does not detect pure level changes but changes in the intrinsic pattern structure of a time series and so it could be interesting in combination with other methods.

scholarly journals Detection of time reversibility in time series by ordinal patterns analysis

2018 ◽  
Vol 28 (12) ◽  
pp. 123111 ◽  
Author(s):  
J. H. Martínez ◽  
J. L. Herrera-Diestra ◽  
M. Chavez
Keyword(s):  
Time Series ◽  
Time Reversibility ◽  
Ordinal Patterns

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

scholarly journals Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 378
Author(s):  
Taeyong Kwon ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Road Networks ◽  
Monitoring Network ◽  
Conditional Entropy ◽  
Radar Data ◽  
Disaster Risk ◽  
Water Levels ◽  
The Road ◽  
Dam Operation ◽  
A Site ◽  
High Degree

Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network.

scholarly journals Computing conditional entropies for quantum correlations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peter Brown ◽  
Hamza Fawzi ◽  
Omar Fawzi
Keyword(s):  
Quantum Key Distribution ◽  
Detection Efficiency ◽  
Conditional Entropy ◽  
Key Distribution ◽  
Quantum Correlations ◽  
Upper Bounds ◽  
Cryptographic Protocols ◽  
Quantum States ◽  
Security Proofs

AbstractThe rates of quantum cryptographic protocols are usually expressed in terms of a conditional entropy minimized over a certain set of quantum states. In particular, in the device-independent setting, the minimization is over all the quantum states jointly held by the adversary and the parties that are consistent with the statistics that are seen by the parties. Here, we introduce a method to approximate such entropic quantities. Applied to the setting of device-independent randomness generation and quantum key distribution, we obtain improvements on protocol rates in various settings. In particular, we find new upper bounds on the minimal global detection efficiency required to perform device-independent quantum key distribution without additional preprocessing. Furthermore, we show that our construction can be readily combined with the entropy accumulation theorem in order to establish full finite-key security proofs for these protocols.

scholarly journals Unraveling the decay of the number of unobserved ordinal patterns in noisy chaotic dynamics

2019 ◽  
Vol 100 (4) ◽  
Author(s):  
Felipe Olivares ◽  
Luciano Zunino ◽  
Miguel C. Soriano ◽  
Darío G. Pérez
Keyword(s):  
Chaotic Dynamics ◽  
Ordinal Patterns
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