A Novel Geometrical Method for Discrimination of Normal, Interictal and Ictal EEG Signals

Akbari, Hesam; Esmaili, Somayeh Saraf

doi:10.18280/ts.370108

Cited by 13 publications

(12 citation statements)

References 32 publications

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“…Geometrical features have been extracted from 2D illustration of several bio-signals including hart rate variability (HRV) [35], ECG [36] and EEG [37,38] for detection of disorders. Geometrical features can quantify the variability and complexity of the 2D shape of bio-signals.…”

Section: Contributionmentioning

confidence: 99%

“…To quantify this element for SODP, we calculate the shortest distance from each point to the 45-degree line as a feature. If ( , ) is a point on SODP, we can calculate the summation of the shortest distance from each point relative to the 45degree line as [37]:…”

Section: Summation Of the Triangle Area Using Three Successive Pointsmentioning

confidence: 99%

“…To quantify the amplitude changes of EEG signals in the time domain, which can be defined as a covered distance for SODP of EEG signals on a coordinate plane, we calculate the summation of successive vector lengths (SSVL) as a feature. It is defined as following [37]:…”

Section: Summation Of the Centroid To Centroid Distance Of Successivementioning

confidence: 99%

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Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

Akbari¹,

Sadiq²,

Payan³

et al. 2021

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Late detection of depression is having detrimental consequences including suicide thus there is a serious need for an accurate computer-aided system for early diagnosis of depression. In this research, we suggested a novel strategy for the diagnosis of depression based on several geometric features derived from the Electroencephalography (EEG) signal shape of the second-order differential plot (SODP). First, various geometrical features of normal and depression EEG signals were derived from SODP including standard descriptors, a summation of the angles between consecutive vectors, a summation of distances to coordinate, a summation of the triangle area using three successive points, a summation of the shortest distance from each point relative to the 45-degree line, a summation of the centroids to centroid distance of successive triangles, central tendency measure and summation of successive vector lengths. Second, Binary Particle Swarm Optimization was utilized for the selection of suitable features. At last, the features were fed to support vector machine and k-nearest neighbor (KNN) classifiers for the identification of normal and depressed signals. The performance of the proposed framework was evaluated by the recorded bipolar EEG signals from 22 normal and 22 depressed subjects. The results provide an average classification accuracy of 98.79% with the KNN classifier using city-block distance in a ten-fold cross-validation strategy. The proposed system is accurate and can be used for the early diagnosis of depression. We showed that the proposed geometrical features are better than extracted features in the time, frequency, time-frequency domains as it helps in visual inspection and provide up to 17.56% improvement in classification accuracy in contrast to those features.

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Section: Contributionmentioning

confidence: 99%

Section: Summation Of the Triangle Area Using Three Successive Pointsmentioning

confidence: 99%

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Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

Akbari¹,

Sadiq²,

Payan³

et al. 2021

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“…Background. Epilepsy is a disease of the central nervous system in which the brain is abnormally active [1][2][3]. Epilepsy, in other words, is a neurological disorder that causes seizures, abnormal sensations, and sometimes loss of consciousness [4].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

Sadiq

Akbari

Rehman

et al. 2021

Journal of Healthcare Engineering

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For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

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“…The postsynaptic potentials that constitute the source of the EEG are collected in the cortex, spread through the structures surrounding the brain to the scalp with hair and recorded from the scalp via metal electrodes. The location of each electrode covered with a conductive material is determined by standard measurements performed on nasion, inion, right and left preauricular points and they are placed according to the international 10-20 system ( Figure 1) [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects

Akgun¹

2020

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Alcoholism is one of the major health problems in the world. The organ most affected by alcohol is the brain. It has been shown that alcohol causes neuronal loss in the brain and reduces brain blood flow and oxygen use. Electroencephalography is a method that measures the instantaneous electrical activity of the brain. It is known that valuable information can be obtained by observing the biological effects of alcohol through EEG. As their methods of signal processing and analysis have evolved, Electroencephalography signals have attracted the attention of researchers in this field. In this study, methods of the time-frequency analysis were applied to Electroencephalography signals obtained from normal and alcoholic subjects. For this purpose, the Cohen’s class distribution was examined. Ambiguity function analysis, which was in the structure of the distribution, was applied to the signals. Then, from the kernel structure inside the distribution, the Wigner-Ville distribution, which was very common, was reached and this distribution was examined. The inadequacy of the distribution resolution was seen and analysis of the new time-frequency distributions, which were obtained by making convolution with 4 types of kernel functions (nonseparable, separable, Doppler independent, lag independent), was performed. As a result, it was shown that the resolution of time-frequency distributions could be improved with proper kernel functions. Thus, at the end of these analyses, changes that alcohol caused in brain functions were revealed.

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A Novel Geometrical Method for Discrimination of Normal, Interictal and Ictal EEG Signals

Cited by 13 publications

References 32 publications

Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects

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