Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Jaafer, Sumai Hamad

doi:10.22153/kej.2021.11.001

Cited by 5 publications

(4 citation statements)

References 24 publications

(28 reference statements)

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“…Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) are temporal domain-based features in which the Hurst exponent assesses long-range dependence or time series self-similarity. A high Hurst exponent indicates correlation and predictability, while a low exponent indicates unpredictability [26,27]. TsEn is extended to Shannon entropy enabling the incorporation of non-extensive statistics.…”

Section: Of 16mentioning

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%

“…DL can automatically learn a comprehensive collection of features from training data, providing a more versatile feature space for modeling than ML's hand-crafted features [26]. Despite these developments, numerous issues remain unanswered and existing approaches fail to detect epileptic seizures [32].…”

Section: Of 16mentioning

confidence: 99%

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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“…First, the collected signals are amplified many times before being transmitted to the computer via the cyton board, bio-sensing daisy board, and USB dongle. Then the notch filter is applied, which is used to remove a specific frequency [16], so the filter was applied for 50 Hz because that hertz is the type of activity in the AC current source of the electrical equipment. As well, a band-pass filter (1-100) Hz is applied to remove the other unwanted frequencies.…”

Section: Pre-processingmentioning

confidence: 99%

BCI-Based Smart Room Control using EEG Signals

Amanuel¹,

Alazzawi²

2022

alkej

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In this paper, we implement and examine a Simulink model with electroencephalography (EEG) to control many actuators based on brain waves. This will be in great demand since it will be useful for certain individuals who are unable to access some control units that need direct contact with humans. In the beginning, ten volunteers of a wide range of (20-66) participated in this study, and the statistical measurements were first calculated for all eight channels. Then the number of channels was reduced by half according to the activation of brain regions within the utilized protocol and the processing time also decreased. Consequently, four of the participants (three males and one female) were chosen to examine the Simulink model during different actions. The model contained: input signals, data selection according to the activation regions in the brain, features extraction, classification according to the frequency ranges of each action, and an interface with an embedded system to control the actuators.

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“…ASD is considered as a one of numerous behavioral diseases caused by abnormal brain connections, which can occur locally, regionally, or both [4]. Numerous studies carried out on the anatomical brain connections in autism have found that autistic brains grow at a faster rate than control brains [5][6][7]. The rate of research that focuses on understanding the neurobiology of this complicated neurodevelopmental condition has increased significantly [8].…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Autism Spectrum Disorder detection among Adults

Al-Qazzaz

Jaffer

Abdulazez

et al. 2022

NJES

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Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-based technique based on k-neighbors (IBK). These classifiers have been performed with Waikato Environment for Knowledge Analysis (WEKA) tools to distinguish autistic adults from healthy, normal subjects. The results showed that, with 99.71%, SMO classification accuracy was 99.71, which exceeded the accuracy of other classifiers. The proposed architecture allows for early detection of ASD, distinguishing between ASD and healthy control subjects. This study could help doctors and clinicians by giving them a better idea of what the future holds for people with autism spectrum disorder (ASD) and by improving therapy programs, allowing people with ASD to live a long and happy life.

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Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Cited by 5 publications

References 24 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

BCI-Based Smart Room Control using EEG Signals

Data Mining for Autism Spectrum Disorder detection among Adults

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