Nonlinear dynamics and modeling of heart and brain signals

Ph.DDOCTOR OF PHILOSOPH

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable- Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.7

Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining

Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply

Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs

Measurements, Models, Systems and Design

531 s.

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces