AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Glory, H. Anila; Vigneswaran, C.; Jagtap, Sujeet S.; Shruthi, R.; Hariharan, G.; Sriram, V. S. Shankar

doi:10.1007/s00521-020-05384-7

Cited by 31 publications

(7 citation statements)

References 82 publications

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“…With the aid of various parameters and datasets, the performance and outcomes of the suggested FSE‐GTRN‐LBO technique are validated in this part. Three distinct benchmarking datasets, including those from Bonn, CHB‐MIT, and Freiburg, were examined 3,36–38 . The parameters used for assess the results of the proposed mechanism are calculated as shown in below:

Accuracy = \frac{T_{normalP} + T_{normalN}}{T_{normalP} + T_{normalN} + F_{normalp} + F_{normalN}} \times 100 %

Precision = \frac{T_{P}}{T_{normalP} + F_{normalP}} \times 100 %

F 1 - score = \frac{2 \times Pre \times Sen}{Pre + Sen} \times 100 %

Recall = \frac{T_{P}}{T_{normalP} + F_{normalN}} \times 100 %

Sensitivity = \frac{T_{P}}{T_{normalP} + F_{normalN}} \times 100 %

Specificity = \frac{T_{N}}{{normalT}_{normalN} + {normalF}_{normalP}} \times 100 %

where,

T_{normalP}

– true positives,

T_{normalN}

– true negatives,

F_{no...}

…”

Section: Resultsmentioning

confidence: 99%

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Golla,

Maloji

2023

Numer Methods Biomed Eng

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Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time‐consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non‐linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time‐frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE‐GTRN‐LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE‐based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure‐affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE‐GTRN‐LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state‐of‐the‐art techniques.

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Accuracy = \frac{T_{normalP} + T_{normalN}}{T_{normalP} + T_{normalN} + F_{normalp} + F_{normalN}} \times 100 %

Precision = \frac{T_{P}}{T_{normalP} + F_{normalP}} \times 100 %

F 1 - score = \frac{2 \times Pre \times Sen}{Pre + Sen} \times 100 %

Recall = \frac{T_{P}}{T_{normalP} + F_{normalN}} \times 100 %

Sensitivity = \frac{T_{P}}{T_{normalP} + F_{normalN}} \times 100 %

Specificity = \frac{T_{N}}{{normalT}_{normalN} + {normalF}_{normalP}} \times 100 %

where,

T_{normalP}

– true positives,

T_{normalN}

– true negatives,

F_{no...}

…”

Section: Resultsmentioning

confidence: 99%

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

Golla,

Maloji

2023

Numer Methods Biomed Eng

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“…This technique is developed based on the standard Deep Neural Network (DNN), Spatio-Temporal Neural Network (i.e. CNN and gated recurrent unit), 13,41,42 and back propagation algorithm, which offers a large set of functions for analyzing the patterns of seizure-affected signals. The proposed TAENN is able to effectively deal with the temporal correlation that exists within the input time series because of the directional circulation mechanism.…”

Section: Classificationmentioning

confidence: 99%

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Kantipudi,

Kumar,

Aluvalu

et al. 2024

Sci Rep

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Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.

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“…It is one of the first research articles to study inter‐hospital generalized performance. Glory et al (2021) proposed Adaptive Haar Wavelet (AHW) based Binary Grasshopper Optimization Algorithm (BGOA) with DNN to classify EEG data. The authors also compared this method to DNN without using AHW‐BGOA on Bern Barcelona, Bonn University, and CHB MIT data.…”

Section: Software Developmentsmentioning

confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Cited by 31 publications

References 82 publications

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

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