EEG signal classification for epilepsy diagnosis via optimum path forest – A systematic assessment

Nunes, Thiago; Coelho, André L. V.; Lima, Clodoaldo A. M.; Papa, João Paulo; Albuquerque, Victor Hugo C. de

doi:10.1016/j.neucom.2014.01.020

Cited by 88 publications

(48 citation statements)

References 37 publications

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“…Suzuki [99] Massive-training neural network (MTANN) Iwashita et al [100], Luz et al [101], Nunes et al [102] and Papa et al [103] Optimum path forest (OPF) …”

Section: Authorsmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

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200

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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“…Suzuki [99] Massive-training neural network (MTANN) Iwashita et al [100], Luz et al [101], Nunes et al [102] and Papa et al [103] Optimum path forest (OPF) …”

Section: Authorsmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

Self Cite

200

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“…Amin et al [14] compared the classification accuracy rate of SVM, MLP, k-NN, and Naïve Bayes (NB) classifiers for epilepsy detection. Nunes et al [15] used the optimum path forest classifier for seizure identification. Moreover, artificial bee colony [16] and particle swarm optimization [17] algorithms were also used to optimize neural networks for EEG data classification.…”

Section: Feature Classification Methodsmentioning

confidence: 99%

A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

Manjula¹,

Anandaraju²

2018

IJET

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Brain Computer Interfacing (BCI) is a methodology which imparts a path for communication from external world using brain signals through computer. BCI identifies the specific patterns in a person's changing brain activity to initiate control which relates to the person's intention. The BCI system paraphrases these signal patterns into meaningful control command. In evolving BCI system, numerous signal processing algorithms are proposed. Non-invasive Electroencephalogram (EEG) signals or mind waves are used to extract the distinguished features and further they are classified choosing an appropriate classifier. A study on different feature extraction & Classification algorithms is used in EEG-based BCI exploration and to identify their distinct properties. This paper proposes different methodologies of feature extraction and feature Classification. It also addresses the methods and technology adapted in every phase of the EEG signal processing. This comparative survey also helps in selecting suitable algorithm for the development and accomplishment of further classification of signals.

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“…More detailed descriptions about the supervised OPF algorithm and some of its recent applications can be found, for example, in [36]-classification of ultrasonic signals, [47]-land cover classification, [48,49]-electroencephalogram (EEG) and electrocardiogram (ECG) signal identification and recognition, [50]-characterization of graphite particles in metallographic images, [51,52]-learning-time constrained applications, [53]-segmentation and classification of human intestinal parasites, and in [54]-intrusion detection in computer networks.…”

Section: Machine Learningmentioning

confidence: 99%

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Silva

Marinho

Filho

et al. 2016

Metals

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Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 • C, the undesirable tertiary sigma phase is formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, the material toughness being compromised when the amount of this phase is not less than 4%. This work aimed to develop a solution for the detection of this phase in duplex stainless steels through the computational classification of induced magnetic field signals. The proposed solution is based on an Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes, Artificial Neural Network and Support Vector Machine based classifiers. The induced magnetic field was produced by the interaction between an applied external field and the microstructure. Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts of sigma phases (up to 18% in content). The obtained classification results were compared against the ones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracture surface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved a classification accuracy superior to 95% and was revealed to be robust to signal noise, being therefore a valid testing tool to be used in this domain.

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EEG signal classification for epilepsy diagnosis via optimum path forest – A systematic assessment

Cited by 88 publications

References 37 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

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