Epileptic Seizure Detection in EEGs by Using Random Tree Forest, Naïve Bayes and KNN Classification

Lestari, Fauzia P.; Haekal, Mohammad; Edison, Rizki Edmi; Fauzy, Fikry Ravi; Khotimah, Siti Nurul; Haryanto, Freddy

doi:10.1088/1742-6596/1505/1/012055

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…So each feature in this class contributes with its functionality to the probability. It is used in large dataset [14].…”

Section: What Is Naive Bayesmentioning

confidence: 99%

“…"Given a Hypothesis H and evidence E, Bayes theorem states that the relationship between the probability of the hypothesis before getting the evidence P(H) and the probability of the hypothesis after getting the evidence P(H/E) is" [14]. P(H/E) = P(E/H).…”

Section: Bayes Theoremmentioning

confidence: 99%

“…From the above dataset the frequency table is designed for Outlook, Humidity, Windy [14] Table 3. Frequency Table…”

Section: Classification Stepsmentioning

confidence: 99%

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Comparative Analysis Among Decision Tree vs. Naive Bayes for Prediction of Weather Prognostication

Sindhu

Prakash

2021

Advances in Parallel Computing

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In the previous era, a computer is programmed for some specific task. An electronic device is programmed to do its function electronically. It was done with a target device, the programming environment and the system. We get the necessary intermediate code by running the program with the above said environment and committed into the target device. Thus the device performs the task it was intended to do. In case if we need to change the functionality of the device by the learning experience of the vendor and users, the vendor will upgrade the product. Nowadays in this machine learning era, the devices are programmed in such a way it can learn by its own experience and with the available data it collected it can even manipulate the algorithm by itself with the provided data set. Thus machine learning is ruling this era. We are going to discuss the machine learning algorithms here which was used to predict by itself with the data set collected. Therefore, machine learning is all about learning about computer algorithms that progress its potential through the experience. Thus, Machine learning is presently highly regarded analysis topic and applied to all told application in day to day life. In this paper we have a tendency to extract the knowledge of machine learning algorithms like decision tree, Naive Bayes and enforce the algorithms with sample dataset of weather prognostication.

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“…So each feature in this class contributes with its functionality to the probability. It is used in large dataset [14].…”

Section: What Is Naive Bayesmentioning

confidence: 99%

Section: Bayes Theoremmentioning

confidence: 99%

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Comparative Analysis Among Decision Tree vs. Naive Bayes for Prediction of Weather Prognostication

Sindhu

Prakash

2021

Advances in Parallel Computing

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“…Previously, many machine learning methods have been proposed for epileptic EEG signal recognition, such as the naive Bayes method (NB) [9], K-nearest neighbor (KNN) [10], support vector machine (SVM) [11], fuzzy system [12,13], and extreme learning machine (ELM) [14,15], and they have shown good effectiveness. In essence, epileptic EEG signal recognition is a typical imbalanced classification task [16,17].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Imbalanced Epileptic EEG Signals by a Graph‐Based Extreme Learning Machine

Zhou

Zhang

Jiang

2021

Wireless Communications and Mobile Computing

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Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

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“…Neste sistema aplica-se uma metodologia convencionalmente empregada para extração de características e análise de EEG descrita em trabalhos como [23,78,79]. Também de acordo com outros trabalhos da literatura [80][81][82][83][84][85], foram elegidos como métodos classificadores três técnicas amplamente usadas para tarefas de classificação em sinais EEG:…”

Section: Sistemaunclassified

Redes Neurais De Aprendizado Profundo Para a Identificação De Despertares Relacionados a Eventos Respiratórios Usando Sinais Eeg Polissonográficos

JARAMILLO¹

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Graças a Deus pela vida, fortaleza, experiencias e a sabedoria .Ao meu orientador o Dr. Marco Aurélio Pacheco pela oportunidade, confiança e apoio durante o desenvolvimento dessa pesquisa e ao Dr. Smith Arauco, por compartilhar seu conhecimento, tempo e pela ajuda oferecida.Aos meus Co-orientadores, a Dra. Elisabeth Costa Monteiro por sua ajuda, confiança e apoio incondicional, gratidão infinita pela dedicação e ajuda brindada, e ao Dr. Alvaro David Orjuela Cañón por ser meu amigo, tutor de graduação, um excelente professor e se tornar meu apoio. Ao Dr. Paulo Ivson pela ajuda, tempo, compreensão e apoio brindado.O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Código de Financiamento 001. À PUC-Rio, pela bolsa de isenção das anuidades e pelo acesso à uma infraestrutura de pesquisa e a um enriquecedor ambiente acadêmico.Ao meus pais pelo apoio, amor e paciência, gratidão por me ensinar que os obstáculos da vida podem ser a melhor escola e por me educar com muito amor e carinho. Ao meu irmão por ser meu amigo e minha motivação pra ser a melhor versão de mim cada dia. Ao meu namorado Juan David Caicedo Vásquez pelo amor, apoio, paciência, compreensão e por tornar minha vida simplesmente maravilhosa com seu sorriso e enorme coração, e a nossa gatinha Sombra, por nos dar luz e amor nos dias escuros.Às minhas amigas, Olga, Vicky e Leidy pelo apoio emocional e sua amizade ao longo destes anos. Também a meus amigos de mestrado Yessica, Esteban, Allan, Andrés e Priscila pelas lembranças, risadas e as brincadeiras compartilhadas, em especial a meu amigo Salvador Pacheco pelos ensinamentos, companhia e ajuda nos momentos mais difíceis, gratidão amigão!!.

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Epileptic Seizure Detection in EEGs by Using Random Tree Forest, Naïve Bayes and KNN Classification

Cited by 15 publications

References 14 publications

Comparative Analysis Among Decision Tree vs. Naive Bayes for Prediction of Weather Prognostication

Comparative Analysis Among Decision Tree vs. Naive Bayes for Prediction of Weather Prognostication

Recognition of Imbalanced Epileptic EEG Signals by a Graph‐Based Extreme Learning Machine

Redes Neurais De Aprendizado Profundo Para a Identificação De Despertares Relacionados a Eventos Respiratórios Usando Sinais Eeg Polissonográficos

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