Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable- Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.7

Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.Epilepsi merupakan penyakit yang menyerang otak dan mengakibatkan seseorang mengalami kejang karena adanya gangguan system saraf pusat (neurologis) sehingga menyebabkan hilang kesadaran. Perekaman aktifitas listrik otak maka digunakan uji sinyal EEG, karena dengan uji EEG dapat digunakan untuk mendiagnosa penyakit otak dan mental seperti epilepsi. Tujuan yang hendak dicapai pada penelitian ini adalah agar dapat mengidentifikasi seseorang mengidap epilepsi atau tidak menggunakan metode Fast Fourier Transform (FFT) dan Adaptive Neuro Fuzzy Inference System (ANFIS) serta hasil tingkat akurasi, sensitivitas, dan presisi dari metode tersebut. Metode FFT digunakan untuk mentransformasikan sinyal EEG yang semula berbasis waktu menjadi sinyal EEG berbasis frekuensi dan dilanjutkan dengan proses ekstraksi fitur dari setiap sinyal hasil pemfilteran dengan menggunakan median, mean dan standart deviasi pada masing-masing sinyal EEG. Hasil dari ektraksi fitur digunakan sebagai input pada proses klasifikasi sinyal EEG menggunakan metode ANFIS. Hasil sistem klasifikasi dengan dua kelas (Normal-Epilepsi) menghasilkan akurasi, sensitivitas, dan presisi sebesar 100% dan sistem klasifikasi sinyal EEG menggunakan ANFIS dengan pembagian tiga kelas (Normal-Not Seizure Epilepsy-Epilepsy) menghasilkan akurasi sebesar 89.33% sensitivitas 89.37% dan presisi sebesar 89.33%

Medical image classification and symptoms detection using neuro fuzzy

The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great potential in this field. Hence, in this project the neuro fuzzy system or ANFIS was applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the tumors

Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier.

The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of Brain-Computer Interface (BCI). We propose here to use an Adaptive Neuron-Fuzzy Inference System (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. The continuous wavelet transform will be used to extract highly representative features from selected scales. The performance of ANFIS will be compared with the well-known support vector machine classifier