Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the ''gating function''. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.

EEG signal classification using wavelet feature extraction and a mixture of expert model

In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.

EEG signal classification using PCA, ICA, LDA and support vector machines

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders

This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease. The algorithm can be divided into four steps. The first step consists in image enhancement, shade correction and image normalization of the green channel. The second step aims at detecting candidates, i.e. all patterns possibly corresponding to MA, which is achieved by diameter closing and an automatic threshold scheme. Then, features are extracted, which are used in the last step to automatically classify candidates into real MA and other objects; the classification relies on kernel density estimation with variable bandwidth. A database of 21 annotated images has been used to train the algorithm. The algorithm was compared to manually obtained gradings of 94 images; sensitivity was 88.5% at an average number of 2.13 false positives per image.

Automatic detection of microaneurysms in color fundus images for screening of diabetic retinopathy.

Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction

Neural classification of lung sounds using wavelet coefficients.

Parkinsons disease (PD) is one of the most common degenerative disease of the nervous system. In this paper, a method is proposed to distinguish PD patients from healthy people using wavelet transform based feature extraction of gait characteristics. This approach could help the physicians for early diagnosis of the disease and to start the treatment at the beginning stage of the disease. Wavelet Transform (WT) of preprocessed gait data was performed and statistical features were extracted from these coefficients. Artificial Neural Network (ANN) based classifier has been used in this paper and performance of the model with various efficient Back Propagation (BP) algorithms were evaluated. A comparative study on classification using different classifiers such as Support Vector Machine (SVM), Naive Bayes classifier has also been done. Experimental results have demonstrated very good performance on classifying PD patients.

Parkinsons disease classification using wavelet transform based feature extraction of gait data

Microaneurysms (MAs) are the earliest pre-eminent indicators of diabetic retinopathy (DR) and are hard to distinguish for ophthalmologists on standard fundus images. This study proposes a method based on discrete orthonormal Stockwell transform and statistical features for discriminating between normal and diseased retinal images. Feature extraction by the two different approaches is consolidated and a total of 24 features are extracted for classifier models. Training and testing of the proposed method have been accomplished using 1140 retinal colour photographs. A comparative study using eight best-known classifiers is showcased for detection of MAs and the performance of the classifiers is evaluated using retinal images by performing ten-fold cross-validation procedure. Simulation results demonstrate the efficiency and adequacy of the proposed method which mainly characterise the textural features. The proposed method is compared with existing algorithms and the results show that the algorithm detects DR with high veracity. With the high accuracy and positive prediction, the proposed system assures promising results in early diagnosis of DR.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5672

Automated detection of microaneurysms using Stockwell transform and statistical features

Ensemble of multi-stage deep convolutional neural networks for automated grading of diabetic retinopathy using image patches

Movement disorders occurring due to improper functioning of neurons are termed as neurodegenerative diseases. Among the neurodegenerative diseases, amyotropic lateral sclerosis (ALS), huntingtons disease (HD) and Parkinson's disease (PD) are important. As these diseases are progressive, early diagnosis has significance in managing the diseases. Gait signal is movement signal which can be obtained from force sensitive resistors placed under the foot. Left stride, right stride and left swing signals are selected, frequency spectra and power spectral density of signals are obtained. After dividing the obtained spectra into ten parts, statistical features of mean, variance, skewness and kurtosis are computed. Ability of artificial neural network, support vector machine and naive Bayes classifier are tested for classifying the diseases using the selected statistical features.

C. Sathish Kumar

Papers

Neural classification of lung sounds using wavelet coefficients.

Parkinsons disease classification using wavelet transform based feature extraction of gait data

Automated detection of microaneurysms using Stockwell transform and statistical features

Ensemble of multi-stage deep convolutional neural networks for automated grading of diabetic retinopathy using image patches

Frequency analysis of gait signals for detection of neurodegenerative diseases