Diagnosing epilepsy using entropy measures and embedding parameters of EEG signals

Epilepsy is a neurological disorder that affects normal neural activity. These electrical activities can be recorded as signals containing information about the brain known as Electroencephalography (EEG) signals. Analysis of the EEG signals by individuals for epilepsy diagnosis is subjective and time-consuming. So, an automatic classification system with high detection accuracy is required to overcome possible errors. In this study, the discrete wavelet transform has been applied to EEG signals. Then, entropy measures and embedding parameters have been extracted. These features have been investigated individually to find the most discriminating ones. The significance level of each feature was evaluated by statistical analysis. Consequently, LDA and SVM algorithms have been employed to categorize the EEG signals. The results have indicated that the features of Embedding parameters, PermutationEntropy, FuzzyEntropy, SampleEntropy, NormEntropy, SureEntropy, LogEntropy, and ThresholdEntropy have the potential to discriminate epileptic patients from healthy subjects significantly. Also, SVM classifier has achieved the highest classification accuracy. In this study, we could find effective embedding-based and entropy-based features as appropriate single measures for identifying abnormal activities that can efficiently discriminate the EEG signals of epileptics from healthy individuals. According to the results, they can be used for automatic classification of epileptic EEG signals that are difficult to examine visually

Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode the observed functional activity at a given time point. These encodings are decoded using machine learning to compare both the algorithms and their assumptions, using the time series weights to predict whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. For classifying cognitive activity, the sparse coding algorithm of $L1$ Regularized Learning consistently outperformed 4 variations of ICA across different numbers of networks and noise levels (p$<$0.001). The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy. Within each algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p$<$0.001). The success of sparse coding algorithms may suggest that algorithms which enforce sparse coding, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA