Seizure Detection with Local Binary Pattern and CNN Classifier

Ramakrishnan, S.; Murugavel, A. S. Muthanantha; Sathiyamurthi, P.; Ramprasath, J.

doi:10.1088/1742-6596/1767/1/012029

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…/ The author(s) of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission. EEG nöronlar tarafından üretilen beynin elektriksel aktivesinin ayrıntılarını içeren bir sinyaldir [11]. Bu sinyaller zaman serisi verileridir ve zaman alanından öznitelikler çıkarılarak EEG modeli analiz edilir [12].…”

Section: Etik Standartların Beyanı (Declaration Of Ethical Standards)unclassified

Epileptik Nöbet Tespiti İçin Destek Regresyon Temelli Yeni Bir Sınıflandırma Yaklaşımı

KINACI

Bal

Kınacı³

2024

Politeknik Dergisi

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Sınıflandırma problemi araştırmacılar tarafından uzun zamandır incelenen bir konu olmasına rağmen güncelliğini hala korumaktadır. Özellikle görüntü işleme ve hastalık tanısının belirlenmesi problemleri güncel uygulama alanlarından bazılardır. Bu çalışma destek vektör regresyon ve matematiksel programlamaya dayalı yeni bir veri sınıflandırma yöntemi sunmaktadır. Önerilen yöntem iki aşamalı hibrit bir yapıdan oluşmaktadır. İlk aşamada, destek vektör regresyon denklemi ile her bir birim için sınıflandırma skoru elde edilirken ikinci aşamada ise birimlerin sınıflandırma skorları kullanılarak bir matematiksel model yardımıyla sınıflandırma kuralı oluşturulur ve birimlerin sınıflandırılması sağlanır. Önerilen yöntem geleneksel yöntemlere alternatif bir yenilik sunmaktadır. Geleneksel matematiksel programlamaya dayalı yöntemler sınıfları doğrusal bir fonksiyon ile ayırır. Bu durum ise matematiksel programlamaya dayalı algoritmalarının kullanımını kısıtlar. Önerilen yöntem, doğrusal veya doğrusal ayrılamayan veri yapılarının tamamında kullanılabilir olmasının yanı sıra ikiden fazla grup sayısının olduğu problem türlerine de kolaylıkla dönüştürülebilmektedir. Model önce simülasyon ile irdelenmiş sonrasında Elektroensefalograf (EEG) sinyallerinin sınıflandırılması probleminde uygulanmış ve sınıflandırma performansı mevcut yöntemlerle karşılaştırılmıştır. Elde edilen sonuçlar tablolarda verilmiş ve önerilen modelin mevcut algoritmalara alternatif olabileceğini gösterilmiştir.

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Section: Etik Standartların Beyanı (Declaration Of Ethical Standards)unclassified

Epileptik Nöbet Tespiti İçin Destek Regresyon Temelli Yeni Bir Sınıflandırma Yaklaşımı

KINACI

Bal

Kınacı³

2024

Politeknik Dergisi

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“…The proposed model outperformed other methods with a sensitivity of 96.84% and an absolute specificity of 99.66% on the CHB-MIT dataset. In this work [28], the author presented two innovative strategies for extracting features from EEG data and classifying them using Convolutional Neural Networks (CNNs). The results of the studies showed that the suggested technique outperforms the others in terms of sensitivity, specificity, classification accuracy, and runtime.…”

Section: Literature Surveymentioning

confidence: 99%

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

Cherukuvada¹,

Kayalvizhi²

2023

Computers, Materials &Amp; Continua

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The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of ES analysis use traditional deep learning (DL), and machine learning (ML) approaches. This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection (OAOFS-DBNECD) technique using EEG signals. The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs. The suggested OAOFS-DBNECD technique transforms the EEG signals into .csv format at the initial stage. Next, the OAOFS technique selects an optimal subset of features using the preprocessed data. For seizure classification, the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer (AEO) with a deep belief network (DBN) model. An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm. The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies. In addition, the result of the suggested approach has been evaluated using the CHB-MIT database, and the findings demonstrate accuracy of 97.81%. These findings confirmed the best seizure categorization accuracy on the EEG data considered.

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“…A CNN is a type of feed-forward neural network that shows robust performances in many image classification and pattern recognition tasks [43,44]. The main distinction between a CNN and a normal neural network is that a CNN benefits from the properties of the local connective, shared weights, hierarchical features, and pooling [45].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

et al. 2021

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This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

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Seizure Detection with Local Binary Pattern and CNN Classifier

Cited by 7 publications

References 12 publications

Epileptik Nöbet Tespiti İçin Destek Regresyon Temelli Yeni Bir Sınıflandırma Yaklaşımı

Epileptik Nöbet Tespiti İçin Destek Regresyon Temelli Yeni Bir Sınıflandırma Yaklaşımı

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

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