Automated diagnosis of epilepsy from EEG signals using ensemble learning approach

Abdulhay, Enas; Elamaran, V.; Chandrasekar, M.; Balaji, V. S.; Narasimhan, K.

doi:10.1016/j.patrec.2017.05.021

Cited by 39 publications

(12 citation statements)

References 23 publications

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“…The lower accuracy—in those articles—is mainly due to the utilization of linear methods or of techniques sensitive to amplitude modulation. Linear methods cannot be sufficiently adapted to the nonlinear nature of EEG (Abdulhay et al, 2017; Abdulhay, Alafeef, Alzghoul, et al, 2020; Abdulhay, Elamaran, Chandrasekar, et al, 2020); linear processing assumes the strict superposition principle that fully skips the important requirement of data driven analysis or exploration based on inherent physiological processes not on pre‐assumed models. Furthermore, although a number of approaches were directed toward nonlinearity, accuracy level was influenced by the incapability of complete dissociation between the modulations of energy—in the time and frequency domains—with the purpose of extracting spectral information without the effect of amplitude modulation (Abdulhay, Alafeef, Abdelhay, & Al‐Bashir, 2020).…”

Section: Resultsmentioning

confidence: 99%

Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

et al. 2021

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Autism spectrum disorder (ASD) is a neurological and developmental disorder that commences usually in the early years of age. It impacts the social interaction, communication and learning. It is believed that it is mainly caused by an abnormal connectivity between brain zones. The presented work applies an EEG‐based nonlinear method for the classification of ASD and neuro‐typical groups. The suggested procedure does not require pre‐assumptions and is completely data‐driven. Also, the main advantage is the accurate tracking of the pace of EEG activity without the effect of amplitude modulation on the spectral information. In addition, the tracking is conducted point‐by‐point to avoid shortcomings of inexact global features. First, for every (ASD or neuro‐typical) volunteer, the recorded EEG channels (64 channels) are decomposed by empirical mode decomposition (EMD) in order to get the underlying components (intrinsic mode functions—IMFs). Second, the direct quadrature (DQ) method is used to normalize the IMFs, and to dissociate between amplitude and frequency contents of the resulted components, as well as to help extract then the point‐by‐point spectral information from the analytic normalized IMFs by Hilbert transform. Third, the correlation coefficients between the instantaneous frequency vectors of the counterpart components will be computed over all channels (i.e., between components number ‘i’ of channels ‘x’ and ‘y’, 1 < i < number of components, 1 < x < 64, 1 < y < 64). Fourth, correlation coefficients array is constructed. Fifth, the dimension of the feature array is reduced without loss of significant information. Sixth, classification of reduced features is achieved via neural network. Finally, the statistical assessment of the classification outcome is conducted. The proposed method yields a test accuracy of 94.1%–100%.

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Section: Resultsmentioning

confidence: 99%

Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

et al. 2021

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“…Abdulhay et al (2020) [36] entropy, nonlinear features, higher order spectra KNN, SVM, BN, SCANN A-D-E 98.5…”

Section: Authorsmentioning

confidence: 99%

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Ren

et al. 2023

Chinese Phys. B

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Electroencephalogram (EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the Horizontal Visibility Graph (HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the Weighted Horizontal Visibility Graph (WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations, they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis (MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals. Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

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“…For an effective medical diagnosis, we proposed the idea of using an improved LSTM model to implement medical consultation text classification. The commonly used classification methods include Naïve Bayes [ 1 ], Support Vector Machine (SVM) [ 2 ], and Decision Trees [ 3 ]. These classic machine learning classification algorithms have achieved significant results in text classification tasks.…”

Section: Introductionmentioning

confidence: 99%

An Improved Double Channel Long Short-Term Memory Model for Medical Text Classification

Liang

Chen

et al. 2021

Journal of Healthcare Engineering

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There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.

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Automated diagnosis of epilepsy from EEG signals using ensemble learning approach

Cited by 39 publications

References 23 publications

Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

An Improved Double Channel Long Short-Term Memory Model for Medical Text Classification

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