Epileptic EEG activity detection for children using entropy-based biomarkers

Kbah, Sadeem Nabeel Saleem; Al-Qazzaz, Noor Kamal; Jaafer, Sumai Hamad; Sabir, Mohannad K.

doi:10.1016/j.neuri.2022.100101

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…It makes this method computationally efficient and sensitive to short-term patterns. This helps diagnose neurological diseases by detecting EEG anomalies since short-term changes are significant [29,30]. Permutation entropy with time series amplitude variations is called AAPE.…”

Section: Of 16mentioning

confidence: 99%

“…Permutation entropy with time series amplitude variations is called AAPE. AAPE may catch more signal dynamics features for heterogeneous signals by studying ordinal patterns and amplitudes [30,31].…”

Section: Of 16mentioning

confidence: 99%

“…The decision tree is a recursively generated tree that is based on the supervised classification technique of breaking down a big problem into multiple smaller subproblems. Each leaf node in a decision tree is assigned a class label, and the internal nodes, as well as the parent node, use attribute testing criteria to partition records based on their features [30,47]. The boost in the accuracy of classification was not drastically affected by the larger number of trees.…”

Section: Machine Learning Classification Modelmentioning

confidence: 99%

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Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches

Al-Qazzaz,

Alrahhal,

Jaafer

et al. 2023

Preprint

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One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

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Section: Of 16mentioning

confidence: 99%

Section: Of 16mentioning

confidence: 99%

Section: Machine Learning Classification Modelmentioning

confidence: 99%

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Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches

Al-Qazzaz,

Alrahhal,

Jaafer

et al. 2023

Preprint

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“…Electroencephalography (EEG) is highly nonlinear and entropy measures have long been used in clinical practice to reveal the nonlinear nature, for example, in classifying walking limitations [ 1 ], analyzing complexity and variability of trunk accelerations in patients with Parkinson’s Disease [ 2 , 3 ], differentiating balance patterns in diabetic patients with and without neuropathy [ 4 ], assessing anesthetic drug effects on the brain [ 5 ], identifying fetal distress [ 6 ], autism spectrum disorder in children [ 7 ], tinnitus [ 8 ], attention deficit hyperactivity disorder [ 9 ], epilepsy [ 10 ], Alzheimer’s disease [ 11 ], schizotypy [ 12 ], mind wandering [ 13 ] and psychogenic non-epileptic seizures [ 14 ]. Examples of entropy measures are permutation entropy [ 15 ], approximate entropy [ 16 ], neural network entropy [ 17 ], dispersion entropy [ 18 ], sample entropy [ 19 , 20 ] and their variants [ 21 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

Zhang,

Zhai,

et al. 2024

PLoS ONE

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In the analysis of electroencephalography (EEG), entropy can be used to quantify the rate of generation of new information. Entropy has long been known to suffer from variance that arises from its calculation. From a sensor’s perspective, calculation of entropy from a period of EEG recording can be treated as physical measurement, which suffers from measurement noise. We showed the feasibility of using Kalman filtering to reduce the variance of entropy for simulated signals as well as real-world EEG recordings. In addition, we also manifested that Kalman filtering was less time-consuming than moving average, and had better performance than moving average and exponentially weighted moving average. In conclusion, we have treated entropy as a physical measure and successfully applied the conventional Kalman filtering with fixed hyperparameters. Kalman filtering is expected to be used to reduce measurement noise when continuous entropy estimation (for example anaesthesia monitoring) is essential with high accuracy and low time-consumption.

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“…The cognitive activities accessed through electroencephalogram (EEG) signals provide an intelligent solution to assist specially-abled individuals to accomplish body movement just by mere imagination without any external support is motor imagery (MI) classification [ 1 ]. Non-invasive nature and wide exposure of EEG signals for various neurological activities makes it most viable for modelling BCI systems [ 2 , 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

Khare

Gaikwad

Bokde

2022

Sensors

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Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.

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Epileptic EEG activity detection for children using entropy-based biomarkers

Cited by 11 publications

References 29 publications

Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches

Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

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