Nonlinear dynamics and modeling of heart and brain signals

Ph.DDOCTOR OF PHILOSOPH

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 Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification

Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes

Development of electroencephalogram (EEG) signals classification techniques

Electroencephalography (EEG) is one of the most important signals recorded from humans. It can assist scientists and experts to understand the most complex part of the human body, the brain. Thus, analysing EEG signals is the most preponderant process to the problem of extracting significant information from brain dynamics. It plays a prominent role in brain studies. The EEG data are very important for diagnosing a variety of brain disorders, such as epilepsy, sleep problems, and also assisting disability patients to interact with their environment through brain computer interface (BCI). However, the EEG signals contain a huge amount of information about the brain’s activities. But the analysis and classification of these kinds of signals is still restricted. In addition, the manual examination of these signals for diagnosing related diseases is time consuming and sometimes does not work accurately. Several studies have attempted to develop different analysis and classification techniques to categorise the EEG recordings. The analysis of EEG recordings can lead to a better understanding of the cognitive process. It is used to extract the important features and reduce the dimensions of EEG data. In the classification process, machine learning algorithms are used to detect the particular class of EEG signal based on its extracted features. The performance of these algorithms, in which the class membership of the input signal is determined, can then be used to infer what event in the real-world process occurred to produce the input signal. The classification procedure has the potential to assist experts to diagnose the related brain disorders. To evaluate and diagnose neurological disorders properly, it is necessary to develop new automatic classification techniques. These techniques will help to classify different EEG signals and determine whether a person is in a good health or not. This project aims to develop new techniques to enhance the analysis and classification of different categories of EEG data. A simple random sampling (SRS) and sequential feature selection (SFS) method was developed and named the SRS_SFS method. In this method, firstly, a SRS technique was used to extract statistical features from the original EEG data in time domain. The extracted features were used as the input to a SFS algorithm for key features selection. A least square support vector machine (LS_SVM) method was then applied for EEG signals classification to evaluate the performance of the proposed approach. Secondly, a novel approach that combines optimum allocation (OA) and spectral density estimation methods was proposed to analyse EEG signals and classify an epileptic seizure. In this study, the OA technique was introduced in two levels to determine representative sample points from the EEG recordings. To reduce the dimensions of sample points and extract representative features from each OA sample segment, two power spectral density estimation methods, periodogram and autoregressive, were used. At the end, three popular machine learning methods (support vector machine (SVM), quadratic discriminant analysis, and k-nearest neighbor (k-NN)) were employed to evaluate the performance of the suggested algorithm. Additionally, a Tunable Q-factor wavelet transform (TQWT) based algorithm was developed for epileptic EEG feature extraction. The extracted features were forwarded to the bagging tree, k-NN, and SVM as classifiers to evaluate the performance of the proposed feature extraction technique. The proposed TQWT method was tested on two different EEG databases. Finally, a new classification system was presented for epileptic seizures detection in EEGs blending frequency domain with information gain (InfoGain) technique. Fast Fourier transform (FFT) or discrete wavelet transform (DWT) were applied individually to analyse EEG recording signals into frequency bands for feature extraction. To select the most important feature, the infoGain technique was employed. A LS_SVM classifier was used to evaluate the performance of this system. The research indicates that the proposed techniques are very practical and effective for classifying epileptic EEG disorders and can assist to present the most important clinical information about patients with brain disorders

A Multi-Tier Distributed fog-based Architecture for Early Prediction of Epileptic Seizures

Epilepsy is the fourth most common neurological problem. With 50 million people living with epilepsy worldwide, about one in 26 people will continue experiencing recurring seizures during their lifetime. Epileptic seizures are characterized by uncontrollable movements and can cause loss of awareness. Despite the optimal use of antiepileptic medications, seizures are still difficult to control due to their sudden and unpredictable nature. Such seizures can put the lives of patients and others at risk. For example, seizure attacks while patients are driving could affect their ability to control a vehicle and could result in injuries to the patients as well as others. Notifying patients before the onset of seizures can enable them to avoid risks and minimize accidents, thus, save their lives. Early and accurate prediction of seizures can play a significant role in improving patients’ quality of life and helping doctors to administer medications through providing a historical overview of patient's condition over time. The individual variability and the dynamic disparity in differentiating between the pre-ictal phase (a period before the onset of the seizure) and other seizures phases make the early prediction of seizures a challenging task. Although several research projects have focused on developing a reliable seizure prediction model, numerous challenges still exist and need to be addressed. Most of the existing approaches are not suitable for real-time settings, which requires bio-signals collection and analysis in real-time. Various methods were developed based on the analysis of EEG signals without considering the notification latency and computational cost to support monitoring of multiple patients. Limited approaches were designed based on the analysis of ECG signals. ECG signals can be collected using consumer wearable devices and are suitable for light-weight real-time analysis. Moreover, existing prediction methods were developed based on the analysis of seizure state and ignored the investigation of pre-ictal state. The analysis of the pre-ictal state is essential in the prediction of seizures at an early stage. Therefore, there is a crucial need to design a novel computing model for early prediction of epileptic seizures. This model would greatly assist in improving the patients' quality of lives. This work proposes a multi-tier architecture for early prediction of seizures based on the analysis of two vital signs, namely, Electrocardiography (ECG) and Electroencephalogram (EEG) signals. The proposed architecture comprises of three tiers: (1) sensing at the first tier, (2) lightweight analysis based on ECG signals at the second tier, and (3) deep analysis based on EEG signals at the third tier. The proposed architecture is developed to leverage the potential of fog computing technology at the second tier for a real-time signal analytics and ubiquitous response. The proposed architecture can enable the early prediction of epileptic seizures, reduce the notification latency, and minimize the energy consumption on real-time data transmissions. Moreover, the proposed architecture is designed to allow for both lightweight and extensive analytics, thus make accurate and reliable decisions. The proposed lightweight model is formulated using the analysis of ECG signals to detect the pre-ictal state. The lightweight model utilizes the Least Squares Support Vector Machines (LS-SVM) classifier, while the proposed extensive analytics model analyzes EEG signals and utilizes Deep Belief Network (DBN) to provide an accurate classification of the patient’s state. The performance of the proposed architecture is evaluated in terms of latency minimization and energy consumption in comparison with the cloud. Moreover, the performance of the proposed prediction models is evaluated using three datasets. Various performance metrics were used to investigate the prediction model performance, including: accuracy, sensitivity, specificity, and F1-Measure. The results illustrate the merits of the proposed architecture and show significant improvement in the early prediction of seizures in terms of accuracy, sensitivity, and specificity

DYNAMIC SELF-ORGANISED NEURAL NETWORK INSPIRED BY THE IMMUNE ALGORITHM FOR FINANCIAL TIME SERIES PREDICTION AND MEDICAL DATA CLASSIFICATION

Artificial neural networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables of data are related to the time dimension and are highly correlated. The main aim of this research work is to investigate and develop efficient dynamic neural networks in order to deal with data analysis issues. This research work proposes a novel dynamic self-organised multilayer neural network based on the immune algorithm for financial time series prediction and biomedical signal classification, combining the properties of both recurrent and self-organised neural networks. The first case study that has been addressed in this thesis is prediction of financial time series. The financial time series signal is in the form of historical prices of different companies. The future prediction of price in financial time series enables businesses to make profits by predicting or simply guessing these prices based on some historical data. However, the financial time series signal exhibits a highly random behaviour, which is non-stationary and nonlinear in nature. Therefore, the prediction of this type of time series is very challenging. In this thesis, a number of experiments have been simulated to evaluate the ability of the designed recurrent neural network to forecast the future value of financial time series. The resulting forecast made by the proposed network shows substantial profits on financial historical signals when compared to the self-organised hidden layer inspired by immune algorithm and multilayer perceptron neural networks. These results suggest that the proposed dynamic neural networks has a better ability to capture the chaotic movement in financial signals. The second case that has been addressed in this thesis is for predicting preterm birth and diagnosing preterm labour. One of the most challenging tasks currently facing the healthcare community is the identification of preterm labour, which has important significances for both healthcare and the economy. Premature birth occurs when the baby is born before completion of the 37-week gestation period. Incomplete understanding of the physiology of the uterus and parturition means that premature labour prediction is a difficult task. The early prediction of preterm births could help to improve prevention, through appropriate medical and lifestyle interventions. One promising method is the use of Electrohysterography. This method records the uterine electrical activity during pregnancy. In this thesis, the proposed dynamic neural network has been used for classifying between term and preterm labour using uterine signals. The results indicated that the proposed network generated improved classification accuracy in comparison to the benchmarked neural network architectures

Deep learning in classifying depth of anesthesia (DoA)

© Springer Nature Switzerland AG 2019. This present study is what we think is one of the first studies to apply Deep Learning to learn depth of anesthesia (DoA) levels based solely on the raw EEG signal from a single channel (electrode) originated from many subjects under full anesthesia. The application of Deep Neural Networks to detect levels of Anesthesia from Electroencephalogram (EEG) is relatively new field and has not been addressed extensively in current researches as done with other fields. The peculiarities of the study emerges from not using any type of pre-processing at all which is usually done to the EEG signal in order to filter it or have it in better shape, but rather accept the signal in its raw nature. This could make the study a peculiar, especially with using new development tool that seldom has been used in deep learning which is the DeepLEarning4J (DL4J), the java programming environment platform made easy and tailored for deep neural network learning purposes. Results up to 97% in detecting two levels of Anesthesia have been reported successfully

Developing new techniques to analyse and classify EEG signals

A massive amount of biomedical time series data such as Electroencephalograph (EEG), electrocardiography (ECG), Electromyography (EMG) signals are recorded daily to monitor human performance and diagnose different brain diseases. Effectively and accurately analysing these biomedical records is considered a challenge for researchers. Developing new techniques to analyse and classify these signals can help manage, inspect and diagnose these signals. In this thesis novel methods are proposed for EEG signals classification and analysis based on complex networks, a statistical model and spectral graph wavelet transform. Different complex networks attributes were employed and studied in this thesis to investigate the main relationship between behaviours of EEG signals and changes in networks attributes. Three types of EEG signals were investigated and analysed; sleep stages, epileptic and anaesthesia. The obtained results demonstrated the effectiveness of the proposed methods for analysing these three EEG signals types. The methods developed were applied to score sleep stages EEG signals, and to analyse epileptic, as well as anaesthesia EEG signals. The outcomes of the project will help support experts in the relevant medical fields and decrease the cost of diagnosing brain diseases