Cognitive task discrimination using approximate entropy (ApEn) on EEG signals

Flores, Christian; Noel, Julien; Fernández, José Raúl Machado

doi:10.1109/brc.2013.6487521

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…Gu et al ( 2003 ) also applied ApEn to data across multiple stages of sleep, and during epileptic seizure onset, reporting decreases during sleep and during seizure onset, but did not use any statistical testing. Attempts to tie ApEn changes to behavioral changes during wakefulness have found conflicting results: ApEn analysis of subjects driving while sleep deprived found no significant changes in ApEn preceding driving errors (Papadelis et al, 2007a ), though Flores Vega et al ( 2013 ) recently showed that ApEn could be used to differentiate between some of the various mental tasks tested. Papadelis et al ( 2007b ) found no significant changes in ApEn as a function of hypoxia, but ApEn derived metrics did show significant changes.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram approximate entropy influenced by both age and sleep

Lee

Fattinger

Mouthon

et al. 2013

Front. Neuroinform.

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The use of information-based measures to assess changes in conscious state is an increasingly popular topic. Though recent results have seemed to justify the merits of such methods, little has been done to investigate the applicability of such measures to children. For our work, we used the approximate entropy (ApEn), a measure previously shown to correlate with changes in conscious state when applied to the electroencephalogram (EEG), and sought to confirm whether previously reported trends in adult ApEn values across wake and sleep were present in children. Besides validating the prior findings that ApEn decreases from wake to sleep (including wake, rapid eye movement (REM) sleep, and non-REM sleep) in adults, we found that previously reported ApEn decreases across vigilance states in adults were also present in children (ApEn trends for both age groups: wake > REM sleep > non-REM sleep). When comparing ApEn values between age groups, adults had significantly larger ApEn values than children during wakefulness. After the application of an 8 Hz high-pass filter to the EEG signal, ApEn values were recalculated. The number of electrodes with significant vigilance state effects dropped from all 109 electrodes with the original 1 Hz filter to 1 electrode with the 8 Hz filter. The number of electrodes with significant age effects dropped from 10 to 4. Our results support the notion that ApEn can reliably distinguish between vigilance states, with low-frequency sleep-related oscillations implicated as the driver of changes between vigilance states. We suggest that the observed differences between adult and child ApEn values during wake may reflect differences in connectivity between age groups, a factor which may be important in the use of EEG to measure consciousness.

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Section: Introductionmentioning

confidence: 99%

Electroencephalogram approximate entropy influenced by both age and sleep

Lee

Fattinger

Mouthon

et al. 2013

Front. Neuroinform.

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“…ApEn is also employed for analyzing short data sets [19], computational applications [20], and brain signals [21][22][23][24].…”

Section: Approximate Entropymentioning

confidence: 99%

Entropy-Based Methods for Motor Fault Detection: A Review

Aguayo-Tapia,

Avalos-Almazan,

Rangel-Magdaleno

2024

Entropy

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In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

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“…Approximate entropy: Approximate entropy (ApEn) is defined as the "logarithmic likelihood that runs of patterns of data that are close to each other will remain close on next incremental comparisons".ApEn is a statistical metric that quantifies the complexity or irregularity of signals both deterministic and stochastic (Flores Vega et al, 2013). It reflects the rate of new pattern generation and is thus related to the concept of entropy.…”

Section: Feature Extraction For Linear and Nonlinear Analysismentioning

confidence: 99%

A Case Study Analysis of EEG Signals under Conditions of Cognition

Kalpana¹,

Chitra²,

Sagari³

2015

AJMS

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Since Electro Encephalo Graphic (EEG) signal is considered chaotic, Nonlinear Dynamics and Deterministic Chaos theory may supply effective descriptors of the dynamics and underlying chaos in the brain. The EEG signals are highly subjective and the information about the various states may appear at random in the time scale. Therefore, EEG signal parameters, extracted and analyzed using computers, are highly useful. This study was undertaken to evaluate the linear and nonlinear parameters such as Approximate Entropy (ApEn), Correlation Dimension (D 2 ), Pearson Autocorrelation, Bi-correlation, Hurst exponent and phase space plots from the EEG signals under different cognitive states.

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Cognitive task discrimination using approximate entropy (ApEn) on EEG signals

Cited by 14 publications

References 10 publications

Electroencephalogram approximate entropy influenced by both age and sleep

Electroencephalogram approximate entropy influenced by both age and sleep

Entropy-Based Methods for Motor Fault Detection: A Review

A Case Study Analysis of EEG Signals under Conditions of Cognition

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