Deep Cellular Recurrent Neural Architecture for Efficient Multidimensional Time-Series Data Processing

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the-art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper

Analysis of EEG signals using complex brain networks

The human brain is so complex that two mega projects, the Human Brain Project and the BRAIN Initiative project, are under way in the hope of answering important questions for peoples' health and wellbeing. Complex networks become powerful tools for studying brain function due to the fact that network topologies on real-world systems share small world properties. Examples of these networks are the Internet, biological networks, social networks, climate networks and complex brain networks. Complex brain networks in real time biomedical signal processing applications are limited because some graph algorithms (such as graph isomorphism), cannot be solved in polynomial time. In addition, they are hard to use in single-channel EEG applications, such as clinic applications in sleep scoring and depth of anaesthesia monitoring. The first contribution of this research is to present two novel algorithms and two graph models. A fast weighted horizontal visibility algorithm (FWHVA) overcoming the speed limitations for constructing a graph from a time series is presented. Experimental results show that the FWHVA can be 3.8 times faster than the Fast Fourier Transfer (FFT) algorithm when input signals exceed 4000 data points. A linear time graph isomorphism algorithm (HVGI) can determine the isomorphism of two horizontal visibility graphs (HVGs) in a linear time domain. This is an efficient way to measure the synchronized index between two time series. Difference visibility graphs (DVGs) inherit the advantages of horizontal visibility graphs. They are noise-robust, and they overcome a pitfall of visibility graphs (VG): that the degree distribution (DD) doesn't satisfy a pure power-law. Jump visibility graphs (JVGs) enhance brain graphs allowing the processing of non-stationary biomedical signals. This research shows that the DD of JVGs always satisfies a power-lower if the input signals are purely non-stationary. The second highlight of this work is the study of three clinical biomedical signals: alcoholic, epileptic and sleep EEGs. Based on a synchronization likelihood and maximal weighted matching method, this work finds that the processing repeated stimuli and unrepeated stimuli in the controlled drinkers is larger than that in the alcoholics. Seizure detections based on epileptic EEGs have also been investigated with three graph features: graph entropy of VGs, mean strength of HVGs, and mean degrees of JVGs. All of these features can achieve 100% accuracy in seizure identification and differentiation from healthy EEG signals. Sleep EEGs are evaluated based on VG and DVG methods. It is shown that the complex brain networks exhibit more small world structure during deep sleep. Based on DVG methods, the accuracy peaks at 88:9% in a 5-state sleep stage classification from 14; 943 segments from single-channel EEGs. This study also introduces two weighted complex network approaches to analyse the nonlinear EEG signals. A weighted horizontal visibility graph (WHVG) is proposed to enhance noise-robustness properties. Tested with two Chaos signals and an epileptic EEG database, the research shows that the mean strength of the WHVG is more stable and noise-robust than those features from FFT and entropy. Maximal weighted matching algorithms have been applied to evaluate the difference in complex brain networks of alcoholics and controlled drinkers. The last contribution of this dissertation is to develop an unsupervised classifier for biomedical signal pattern recognition. A Multi-Scale Means (MSK-Means) algorithm is proposed for solving the subject-dependent biomedical signals classification issue. Using JVG features from the epileptic EEG database, the MSK-Means algorithm is 4:7% higher in identifying seizures than those by the K-means algorithm and achieves 92:3% accuracy for localizing the epileptogenic zone. The findings suggest that the outcome of this thesis can improve the performance of complex brain networks for biomedical signal processing and nonlinear time series analysis