A novel approach for detection of dyslexia using convolutional neural network with EOG signals

Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational development; hence, early detection is highly important. Electrooculogram (EOG) signals are one of the most frequently used physiological signals in human-computer interfaces applications. EOG is a method based on the examination of the electrical potential of eye movements. The advantages of EOG-based systems are non-invasive, affordable, easy to record, and can be processed in real time. In this paper, a novel 1D CNN approach using EOG signals is proposed for the diagnosis of dyslexia. The proposed approach aims to diagnose dyslexia using EOG signals that are recorded simultaneously during reading texts, which are prepared in different typefaces and fonts. EOG signals were recorded from both horizontal and vertical channels, thus comparing the success of vertical and horizontal EOG signals in detecting dyslexia. The proposed approach provided an effective classification without requiring any hand-crafted feature extraction techniques. The proposed method achieved classifier accuracy of 98.70% and 80.94% for horizontal and vertical channel EOG signals, respectively. The results show that the EOG signals-based approach gives successful results for the diagnosis of dyslexia

A New Approach to Detection of Parkinson’s Disease Using Variational Mode Decomposition Method and Deep Neural Networks

In this study, a new approach is proposed to detection of Parkinson's disease by using Electroencephalography (EEG) signals and three different subband signals generated using Variational Mode Decomposition (VMD) method. In the proposed method, EEG signals and subband signals are applied separately to the generated 1D CNN model and the classification results are compared. The classification results showed that the VMD-sub band signals obtained from EEG signals were successful in diagnosing Parkinson's. The highest classifier accuracy was obtained from second VMD subband data by 98.10%

Artificial Neural Network Based Diagnostic System For Melanoma Skin Cancer

Nowadays, skin cancer is a life-threatening disease that causes human death. The human body contains melanocytic cells and these cells are found in the skin. Abnormal growth of melanocytic cells causes skin cancer. This disease can be diagnosed by an expert dermatologist as a result of the interpretation of dermoscopy images by ABCD rule. Because the diagnosis is made by physicians, there are some problems such as misdiagnosis due to human errors. Therefore, in order to solve these problems, computer-assisted diagnosis is necessary to diagnose skin cancer to assist the doctor. The aim of this study was to determine melanoma skin cancer using image processing techniques. In the study, different Artificial Neural Network (ANN) models were applied and their classifier performances were obtained as Multilayer Perceptron (MLP) 99.8 %, Patern Recognition Neetwork 98.3 %, Support Vector Machines 96.7 % and K nearest neighborhood (KNN) 95 %

Classification of healthy and pathological voices using artificial neural networks Saǧlikli ve patolojik seslerin yapay sinir aǧlari kullanarak siniflandirilmasi

© 2019 IEEE.Speech is the basis of communication between people. In daily life, function loss occurs in mechanisms that create voice due to reasons such as occupation, environment, age and gender. In this study, 57 healthy and 150 pathological voice data (from Hyperkinetic Dysphonia, Hypokinetic Dysphonia, Reflux Laryngitis) was classified using proposed fetaures and Artificial Neural Networks (ANN). The data obtained from voice data is given as input to ANN model. Depending on these input data, the output information of the artificial neural network is determined as patient or healthy. In order to classify the patient and healthy group, two hidden layers and an output layer were used as the artificial neural network model with the least error. As a result of the study, the accuracy of classification between patients and healthy groups was 90.47 %

Assessment of dyslexic children with EOG signals: Determining retrieving words/re-reading and skipping lines using convolutional neural networks

This study aims to determine and classify the back to eye movement (retrieving words/re-reading) and skipping lines while reading from electrooculography (EOG) signals. For this aim, EOG signals were recorded during the reading of a text from healthy and from dyslexic children. In this study, a method to assist in the diagnosis and follow-up of dyslexia is proposed by determining skipping lines and back to eye movement (retrieving words/re-reading) while reading. Using the proposed method, skipping lines while reading and back to eye movement (retrieving words/re-reading movements) were determined from EOG signals and spectrogram images of these movement signals are obtained using the Short Time Fourier Transform (STFT) method. These spectrogram images were classified using the 2 Dimensional Convolutional Neural Network (2D-CNN) classifier. The 2D-CNN model has classified the skipping lines signals while reading and back to eye movement (retrieving words/re-reading) signals with 99% success. The findings show that the method proposed in the diagnosis and follow-up of dyslexia can give positive results using these EOG signals

New Method to Diagnosis of Dyslexia Using 1D-CNN

© 2020 IEEE.Dyslexia is a learning disability that can be characterized by reading difficulties. The EOG signals are widely used in biomedical applications such as Human Computer Interaction (HCI), and the use of the EOG signal in the diagnosis of neurodegenerative diseases is increasing. In this paper, we proposed a novel approach for diagnosis with dyslexia using one dimensional convolutional neural network (ID CNN) based on EOG signals. In the first stage of the study, EOG signals were during healthy and dyslexic children read four different texts. In the second stage, the EOG signals were filtered and segmented into frames. At the last stage, the EOG signals were classified the using ID CNN. According to obtained results, 73. 6128±2.8155 % classification accuracy was performed in classifying the healthy and dyslexic group

Detection of amyotrophic lateral sclerosis disease by variational mode decomposition and convolution neural network methods from event-related potential signals

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a result of visual stimuli from ALS patients and healthy controls. As a new method, variational mode decomposition (VMD) is applied to the produced ERP signals and the signals are decomposed into subbands. In addition, empirical mode decomposition (EMD), one of the popular decomposition methods in the literature, was also analyzed, and ERP signals were divided into subbands and compared with the VMD method. Subband signals were classified in two stages with the one-dimensional convolutional neural network (1D CNN) model, which is one of the DL techniques proposed in the study. Accuracy, sensitivity, specificity, and F1-Score measurements were obtained using 5-and 10-fold cross-validation to evaluate classifier performance. In the first stage of classification, only VMD and EMD subband signals were used and 92.95% classification accuracy was obtained by the VMD method. In the second stage, VMD, EMD subband signals, and original ERP signals were all classified together with the VMD+ERP model achieving the maximum classification accuracy rate of 90.42%. It is thought that the results of the study will contribute to the diagnosis of similar neurological disorders such as ALS, attention studies based on visual stimuli, and the development of brain-computer interface (BCI) systems using the method applied to the proposed ERP signals

Detection of Epilepsy Using Wavelet Coherence and Convolutional Neural Networks

According to the World Health Organization, epilepsy is a disease that affects approximately 50 million people worldwide. Due to the unexpected onset of epileptic seizures, it can lead to bodily injury and death. For this reason, it is very important to predict epilepsy. In this study, it was aimed to detect epilepsy by using Electroencephalogram (EEG) signals recorded from Bonn Epilepsy Laboratory. Wavelet Coherence Analysis and Convolutional Neural Networks were used for this aim. Classification results show that accuracy of different clusters in the data set using the proposed method were obtained as 96% for N-S clusters, 96.5% for F-S clusters, 99% for Z-S clusters and 100% for O-S clusters.The results show that the proposed method is promising in estimating epilepsy from EEG signals