Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry

In the first half of the 20th century, a first pulse oximeter was available to measure blood flow changes in the peripheral vascular net. However, it was not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor many physiological parameters in clinical environments. Over the last decade, its use has extended to the area of biometrics, with different methods that allow the extraction of characteristic features of each individual from the PPG signal morphology, highly varying with time and the physical states of the subject. In this paper, we present a novel PPG-based biometric authentication system based on convolutional neural networks. Contrary to previous approaches, our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns image in the (p, q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal are strongly dependent on the vascular bed's biostructure, which is unique to each individual, and highly stable over time and other psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the convoluted nature of the blood network. Our biometric authentication system reaches very low Equal Error Rates (ERRs) with a single attempt, making it possible, by the very nature of the envisaged solution, to implement it in miniature components easily integrated into wearable biometric systems.Comment: 18 pages, 6 figures, 4 table

Markers of endothelial damage in patients with chronic kidney disease on hemodialysis

Patients with Stage 5 chronic kidney disease who are on hemodialysis (HD) remain in a chronic inflammatory state, characterized by the accumulation of uremic toxins that induce endothelial damage and cardiovascular disease (CVD). Our aim was to examine microvesicles (MVs), monocyte subpopulations, and angiopoietins (Ang) to identify prognostic markers in HD patients with or without diabetes mellitus (DM). A total of 160 prevalent HD patients from 10 centers across Spain were obtained from the Biobank of the Nephrology Renal Network (Madrid, Spain): 80 patients with DM and 80 patients without DM who were matched for clinical and demographic criteria. MVs from plasma and several monocyte subpopulations (CD142+/CD16+, CD14+/CD162+) were analyzed by flow cytometry, and the plasma concentrations of Ang1 and Ang2 were quantified by ELISA. Data on CVD were gathered over the 5.5 yr after these samples were obtained. MV level, monocyte subpopulations (CD14+/CD162+ and CD142+/CD16+), and Ang2-to-Ang1 ratios increased in HD patients with DM compared with non-DM patients. Moreover, MV level above the median (264 MVs/µl) was associated independently with greater mortality. MVs, monocyte subpopulations, and Ang2-to-Ang1 ratio can be used as predictors for CVD. In addition, MV level has a potential predictive value in the prevention of CVD in HD patients. These parameters undergo more extensive changes in patients with DM.Support for this work was provided by Plan Nacional de IDi Proyectos de Investigación en Salud of Instituto de Salud Carlos III (ISCIII)–Subdirección General de Evaluación, Fondos de desarrollo regional (FEDER; PI11/01536, PI12/01489, PI14/00806, PI15/01785); Junta de Andalucía grants (P010-CTS-6337, P11-CTS-7352); and Fundación Nefrológica. P. Buendía, A. Carmona, and C. Luna-Ruiz are fellows from Consejería de Innovacion, Ciencia y Empresa, Junta de Andalucía

PhDAY 2020 -FOO (Facultad de Óptica y Optometría)

Por cuarto año consecutivo los doctorandos de la Facultad de Óptica y Optometría de la Universidad Complutense de Madrid cuentan con un congreso propio organizado por y para ellos, el 4º PhDAY- FOO. Se trata de un congreso gratuito abierto en la que estos jóvenes científicos podrán presentar sus investigaciones al resto de sus compañeros predoctorales y a toda la comunidad universitaria que quiera disfrutar de este evento. Apunta en tu agenda: el 15 de octubre de 2020. En esta ocasión será un Congreso On-line para evitar que la incertidumbre asociada a la pandemia Covid-19 pudiera condicionar su celebración

The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI): Incidence and First-Episode Case–Control Programme

Funder: FP7 Ideas: European Research Council; doi: http://dx.doi.org/10.13039/100011199; Grant(s): HEALTH-F2-2010-241909Abstract: Purpose: The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) study contains an unparalleled wealth of comprehensive data that allows for testing hypotheses about (1) variations in incidence within and between countries, including by urbanicity and minority ethnic groups; and (2) the role of multiple environmental and genetic risk factors, and their interactions, in the development of psychotic disorders. Methods: Between 2010 and 2015, we identified 2774 incident cases of psychotic disorders during 12.9 million person-years at risk, across 17 sites in 6 countries (UK, The Netherlands, France, Spain, Italy, and Brazil). Of the 2774 incident cases, 1130 cases were assessed in detail and form the case sample for case–control analyses. Across all sites, 1497 controls were recruited and assessed. We collected data on an extensive range of exposures and outcomes, including demographic, clinical (e.g. premorbid adjustment), social (e.g. childhood and adult adversity, cannabis use, migration, discrimination), cognitive (e.g. IQ, facial affect processing, attributional biases), and biological (DNA via blood sample/cheek swab). We describe the methodology of the study and some descriptive results, including representativeness of the cohort. Conclusions: This resource constitutes the largest and most extensive incidence and case–control study of psychosis ever conducted

The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI): Incidence and First-Episode Case–Control Programme

Funder: FP7 Ideas: European Research Council; doi: http://dx.doi.org/10.13039/100011199; Grant(s): HEALTH-F2-2010-241909Abstract: Purpose: The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) study contains an unparalleled wealth of comprehensive data that allows for testing hypotheses about (1) variations in incidence within and between countries, including by urbanicity and minority ethnic groups; and (2) the role of multiple environmental and genetic risk factors, and their interactions, in the development of psychotic disorders. Methods: Between 2010 and 2015, we identified 2774 incident cases of psychotic disorders during 12.9 million person-years at risk, across 17 sites in 6 countries (UK, The Netherlands, France, Spain, Italy, and Brazil). Of the 2774 incident cases, 1130 cases were assessed in detail and form the case sample for case–control analyses. Across all sites, 1497 controls were recruited and assessed. We collected data on an extensive range of exposures and outcomes, including demographic, clinical (e.g. premorbid adjustment), social (e.g. childhood and adult adversity, cannabis use, migration, discrimination), cognitive (e.g. IQ, facial affect processing, attributional biases), and biological (DNA via blood sample/cheek swab). We describe the methodology of the study and some descriptive results, including representativeness of the cohort. Conclusions: This resource constitutes the largest and most extensive incidence and case–control study of psychosis ever conducted

The continuity of effect of schizophrenia polygenic risk score and patterns of cannabis use on transdiagnostic symptom dimensions at first-episode psychosis: findings from the EU-GEI study

Abstract: Diagnostic categories do not completely reflect the heterogeneous expression of psychosis. Using data from the EU-GEI study, we evaluated the impact of schizophrenia polygenic risk score (SZ-PRS) and patterns of cannabis use on the transdiagnostic expression of psychosis. We analysed first-episode psychosis patients (FEP) and controls, generating transdiagnostic dimensions of psychotic symptoms and experiences using item response bi-factor modelling. Linear regression was used to test the associations between these dimensions and SZ-PRS, as well as the combined effect of SZ-PRS and cannabis use on the dimensions of positive psychotic symptoms and experiences. We found associations between SZ-PRS and (1) both negative (B = 0.18; 95%CI 0.03–0.33) and positive (B = 0.19; 95%CI 0.03–0.35) symptom dimensions in 617 FEP patients, regardless of their categorical diagnosis; and (2) all the psychotic experience dimensions in 979 controls. We did not observe associations between SZ-PRS and the general and affective dimensions in FEP. Daily and current cannabis use were associated with the positive dimensions in FEP (B = 0.31; 95%CI 0.11–0.52) and in controls (B = 0.26; 95%CI 0.06–0.46), over and above SZ-PRS. We provide evidence that genetic liability to schizophrenia and cannabis use map onto transdiagnostic symptom dimensions, supporting the validity and utility of the dimensional representation of psychosis. In our sample, genetic liability to schizophrenia correlated with more severe psychosis presentation, and cannabis use conferred risk to positive symptomatology beyond the genetic risk. Our findings support the hypothesis that psychotic experiences in the general population have similar genetic substrates as clinical disorders

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

The 0–1 test distinguishes between regular and chaotic dynamics for a deterministic system using a time series as a starting point without appealing to any state space reconstruction method. A modification of the 0–1 test allows for the determination of a more comprehensive range of signal dynamic behaviors, particularly in the field of biological signals. We report the results of applying the test and study with more details the PhotoPlethysmoGraphic (PPG) signal behavior from different healthy young subjects, although its use is extensible to other biological signals. While mainly used for heart rate and blood oxygen saturation monitoring, the PPG signal contains extensive physiological dynamics information. We show that the PPG signal, on a healthy young individual, is predominantly quasi-periodic on small timescales (short span of time concerning the dominant frequency). However, on large timescales, PPG signals yield an aperiodic behavior that can be firmly chaotic or a prior transition via an SNA (Strange Nonchaotic Attractor). The results are based on the behavior of well-known time series that are random, chaotic, aperiodic, periodic, and quasi-periodic

Análisis dimensional conforme a la dinámica y complejidad de las señales biológicas

The characterization of the dynamic behavior of biological signals has played, for several decades now, an essential role in early diagnostics of pathologies, both physical and emotional. This work outlines a methodology which, based on a scalar time series obtained experimentally, something quite usual in the clinical field, allows the reconstruction of the space of states which describe the functional response of a physiological process. This methodology relies on very consolidated mathematical foundations, the theory of nonlinear dynamic systems. Once the space where the dynamics of the physical process develops has been reconstructed, we are in a situation where we can characterize its spatial or structural complexity, from a geometric and temporal viewpoint, as it evolves in time. Among outstanding complexity metrics, some stand out, such as entropy, fractal dimension and Lyapunov exponents which, with their many deductive variants, allow us to evaluate the degree of regularity which repetitive patterns present in the time series experiment, and to estimate the structural richness which describes its dynamics. In the context of this research, we undertake the study of a specific biological signal, the PPG (PhotoPlethysmoGraphic) signal, attending to the outlined methodology and according to the most appropriate nonlinear analysis tools. The characterization of its functional response, in contrast to other fundamental reference signals whose behavior is known a priori, allows us to identify its dynamic complexity and, therefore, estimate how the index of coupling between the subsystems which make up the functionality of the underlying physiological process evolves. In this analytical frame it would be feasible to establish a protocol to compare PPG signals from individuals subjected to a stressful situation, with respect to their counterparts in the basal state, so that we can figure out what stress results in, from an operative perspective, according to the functional response of the physiological process which triggers it, and how the organism reestablishes the optimal working conditions or, in a more adverse scenario, how it dynamically expresses a possible irreversible degradation of the system. ----------RESUMEN---------- La caracterización del comportamiento dinámico de las señales biológicas ha desempeñado, desde hace varias décadas, un papel esencial en el diagnóstico temprano de patologías tanto físicas como emocionales. Este trabajo delinea una metodología que, sobre la base de una sucesión temporal escalar adquirida experimentalmente, algo muy habitual en el ámbito clínico, propicia la reconstrucción del espacio de estados que describen la respuesta funcional de un proceso fisiológico. Esta metodología descansa sobre una fundamentación matemática bastante consolidada, la teoría de los sistemas dinámicos no lineales. Una vez reconstruido el espacio en el que se desenvuelve la dinámica del proceso físico, se está en condiciones de poder caracterizar su complejidad espacial o estructural, desde un punto de vista geométrico, y temporal, a medida que aquella evoluciona con el tiempo. Entre las medidas de la complejidad sobresalen parámetros como la entropía, la dimensión fractal y los exponentes de Lyapunov, que, en sus múltiples variantes deductivas, facilitan la evaluación del grado de regularidad que experimentan los patrones repetitivos presentes en la sucesión temporal y la estimación de la riqueza estructural que describe su dinámica. En el contexto de esta investigación se acomete el estudio de una señal biológica en particular, la señal PPG, del inglés photoplethysmographic, atendiendo a la metodología planteada y de acuerdo con las herramientas de análisis no lineal más oportunas. La caracterización de su respuesta funcional, en contraste con otras señales de referencia básicas cuyo comportamiento es conocido a priori, permite identificar su complejidad dinámica y, por tanto, estimar cómo se desarrolla el índice de acoplamiento entre los subsistemas que conforman la funcionalidad del proceso fisiológico subyacente. Con este marco analítico resultaría factible establecer un protocolo de actuación con el que comparar señales PPG de sujetos sometidos a una situación de estrés, respecto a sus contrapartes en estado basal, de manera que pueda descifrarse en qué se traduce el estrés desde una perspectiva operativa, a tenor de la respuesta funcional del proceso fisiológico que la desencadena, y cómo el organismo restablece las condiciones óptimas de funcionamiento o, en un escenario más adverso, cómo exterioriza dinámicamente una posible degradación irreversible del sistema

Phase Space Reconstruction from a Biological Time Series: A Photoplethysmographic Signal Case Study

In the analysis of biological time series, the state space is comprised of a framework for the study of systems with presumably deterministic and stationary properties. However, a physiological experiment typically captures an observable that characterizes the temporal response of the physiological system under study; the dynamic variables that make up the state of the system at any time are not available. Only from the acquired observations should state vectors be reconstructed to emulate the different states of the underlying system. This is what is known as the reconstruction of the state space, called the phase space in real-world signals, in many cases satisfactorily resolved using the method of delays. Each state vector consists of m components, extracted from successive observations delayed a time τ . The morphology of the geometric structure described by the state vectors, as well as their properties depends on the chosen parameters τ and m. The real dynamics of the system under study is subject to the correct determination of the parameters τ and m. Only in this way can be deduced features have true physical meaning, revealing aspects that reliably identify the dynamic complexity of the physiological system. The biological signal presented in this work, as a case study, is the photoplethysmographic (PPG) signal. We find that m is five for all the subjects analyzed and that τ depends on the time interval in which it is evaluated. The Hénon map and the Lorenz flow are used to facilitate a more intuitive understanding of the applied techniques