Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are preferred for identifying and controlling nonlinear DS with multiple equilibrium points. Identification of such DS has been treated largely as a supervised learning problem. Instead, we focus on an unsupervised learning scenario where we know neither the number nor the type of dynamics. We propose a Graph-based spectral clustering method that takes advantage of a velocity-augmented kernel to connect data points belonging to the same dynamics, while preserving the natural temporal evolution. We study the eigenvectors and eigenvalues of the Graph Laplacian and show that they form a set of orthogonal embedding spaces, one for each sub-dynamics. We prove that there always exist a set of 2-dimensional embedding spaces in which the sub-dynamics are linear and n-dimensional embedding spaces where they are quasi-linear. We compare the clustering performance of our algorithm to Kernel K-Means, Spectral Clustering and Gaussian Mixtures and show that, even when these algorithms are provided with the correct number of sub-dynamics, they fail to cluster them correctly. We learn a diffeomorphism from the Laplacian embedding space to the original space and show that the Laplacian embedding leads to good reconstruction accuracy and a faster training time through an exponential decaying loss compared to the state-of-the-art diffeomorphism-based approaches.Comment: Paper Accepted at Journal of Machine Learning Research 23 (2022

The fluctuation behavior of heart and respiratory system signals as a quantitative tool for studying long-term environmental exposures and chronic diseases

Background: Several studies over the last decades have suggested that a wide range of disease states, as well as the aging process itself, are marked by progressive impairment of the involved physiological processes to adapt, resulting in a loss of complexity in the dynamics of physiological functions. Therefore, measuring complexity from physiological system signals holds enormous promise for providing a new understanding of the mechanisms underlying physiological systems and how they change with diseases and aging. Furthermore, since physiological systems are continuously exposed to environmental factors, measuring how physiological complexity changes during exposure to environmental elements might also provide new insights into their effects. Indeed, this approach may be able to unveil subtle but important changes in the regulatory mechanisms of physiological systems not detectable by traditional analysis methods. Objectives: The overall objective of this PhD thesis was to quantify the complexity of the dynamics of heart and respiratory system signals, in order to investigate how this complexity changes with long-term environmental exposures and chronic diseases, using data from large epidemiological and clinical studies, in order to control for most potential confounders of the fluctuation behavior of systems signals (e.g., demographic, environmental, clinical, and lifestyle factors). We specifically aimed (1) at assessing the influence, first, of long-term smoking cessation, and second, of long-term exposure to traffic-related particulate matter of less than 10 micrometers in diameter (TPM10), on the regulation of the autonomic cardiovascular system and heart rate dynamics in an aging general population, using data from the SAPALDIA cohort study; (2) to assess whether the subgrouping of patients with recurrent obstructive airway diseases, including mild-to-moderate asthma, severe asthma, and COPD, according to their pattern of lung function fluctuation, allows for the identification of phenotypes with specific treatable traits, using data from the BIOAIR study. Methods: In the SAPALDIA cohort, a population-based Swiss cohort, 1608 participants ≥ 50 years of age underwent ambulatory 24-hr electrocardiogram monitoring and reported on lifestyle and medical history. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyses of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with (1) smoking status and time elapsed since smoking cessation; (2) long-term exposure to TPM10. The models were adjusted for known confounding factors. In the BIOAIR study, we conducted a time series clustering analysis based on the fluctuation of twice-daily FEV1 measurements recorded over a one year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or COPD from the longitudinal Pan-European BIOAIR study. Results: In the SAPALDIA cohort, our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover. Regarding long-term exposure to TPM10, we did not observe an overall association with heart rate variability/heart rate dynamics in the entire study population. However, significant changes in the heart rate dynamics were found in subjects without cardiovascular morbidity and significant changes, both in the heart rate dynamics and in the heart rate variability, were found in non-obese subjects without cardiovascular morbidity. Furthermore, subjects with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM10. In the BIOAIR study, we identified five phenotypes, of those three distinct phenotypes of severe asthma, in which the progressive functional alteration of the lung corresponded to a gradually increasing clinical severity and translated into specific risks of exacerbation and treatment response features. Conclusions: This thesis hopes to demonstrate the importance of multidimensional approaches to gain understanding in the complex functioning of the human physiological system and of disease processes. Characterization of the complexity in the fluctuation behavior of system signals holds enormous promise for providing new understandings of the regulatory mechanisms of physiological systems and how they change with diseases. However, it is important to combine this kind of approach with classical epidemiological approaches in order to disentangle the various contributions of the intrinsic physiological dynamics, aging, diseases and comorbidities, lifestyle, and environment. In the SAPALDIA cohort study, we were able to disentangle the influence of specific environmental exposures, such as particulate matter air pollution and smoking exposure, on the heart rate variability and heart rate dynamics, and thus to unveil long-term alterations in former heavy smokers, as well as adverse effects of low level, but long-term, exposure to TPM10 in healthy subjects and in subjects with homozygous GSTM1 gene deletion. In the BIOAIR study, we provide evidence that airway dynamics contain substantial information, which enables the identification of clinically meaningful phenotypes, in which the functional alteration of the lung translates into specific treatable traits

Deep learning techniques for biomedical data processing

The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a significant increase in both theoretical and applicative studies. On the one hand, the versatility and the ability to tackle complex tasks have led to the rapid and widespread diffusion of DL technologies. On the other hand, the dizzying increase in the availability of biomedical data has made classical analyses, carried out by human experts, progressively more unlikely. Contextually, the need for efficient and reliable automatic tools to support clinicians, at least in the most demanding tasks, has become increasingly pressing. In this survey, we will introduce a broad overview of DL models and their applications to biomedical data processing, specifically to medical image analysis, sequence processing (RNA and proteins) and graph modeling of molecular data interactions. First, the fundamental key concepts of DL architectures will be introduced, with particular reference to neural networks for structured data, convolutional neural networks, generative adversarial models, and siamese architectures. Subsequently, their applicability for the analysis of different types of biomedical data will be shown, in areas ranging from diagnostics to the understanding of the characteristics underlying the process of transcription and translation of our genetic code, up to the discovery of new drugs. Finally, the prospects and future expectations of DL applications to biomedical data will be discussed

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

Background: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded at https://bitbucket.org/deepak_panday/ceps/src/pipeline_v2/. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing

Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions