Automatization techniques for processing biomedical signals using machine learning methods

The Signal Processing Group (Department of Signal Theory and Communications, University Carlos III, Madrid, Spain) offers the expertise of its members in the automatic processing of biomedical signals. The main advantages in this technology are the decreased cost, the time saved and the increased reliability of the results. Technical cooperation for the research and development with internal and external funding is sought

Dividing Line between Quantum and Classical Trajectories: Bohmian Time Constant

This work proposes an answer to a challenge posed by Bell on the lack of clarity in regards to the line between the quantum and classical regimes in a measurement problem. To this end, a generalized logarithmic nonlinear Schr\"odinger equation is proposed to describe the time evolution of a quantum dissipative system under continuous measurement. Within the Bohmian mechanics framework, a solution to this equation reveals a novel result: it displays a time constant which should represent the dividing line between the quantum and classical trajectories. It is shown that continuous measurements and damping not only disturb the particle but compel the system to converge in time to a Newtonian regime. While the width of the wave packet may reach a stationary regime, its quantum trajectories converge exponentially in time to classical trajectories. In particular, it is shown that damping tends to suppress further quantum effects on a time scale shorter than the relaxation time of the system. If the initial wave packet width is taken to be equal to 2.8 10^{-15} m (the approximate size of an electron), the Bohmian time constant is found to have an upper limit, i. e., ${\tau_{B\max}} = {10^{- 26}}s$

Bohmian Trajectories of Airy Packets

The discovery of Berry and Balazs in 1979 that the free-particle Schr\"odinger equation allows a non-dispersive and accelerating Airy-packet solution has taken the folklore of quantum mechanics by surprise. Over the years, this intriguing class of wave packets has sparked enormous theoretical and experimental activities in related areas of optics and atom physics. Within the Bohmian mechanics framework, we present new features of Airy wave packet solutions to Schr\"odinger equation with time-dependent quadratic potentials. In particular, we provide some insights to the problem by calculating the corresponding Bohmian trajectories. It is shown that by using general space-time transformations, these trajectories can display a unique variety of cases depending upon the initial position of the individual particle in the Airy wave packet. Further, we report here a myriad of nontrivial Bohmian trajectories associated to the Airy wave packet. These new features are worth introducing to the subject's theoretical folklore in light of the fact that the evolution of a quantum mechanical Airy wave packet governed by the Schr\"odinger equation is analogous to the propagation of a finite energy Airy beam satisfying the paraxial equation. Numerous experimental configurations of optics and atom physics have shown that the dynamics of Airy beams depends significantly on initial parameters and configurations of the experimental set-up.Comment: 8 page

Técnicas de automatización para el procesado de señales biomédicas basadas en métodos de aprendizaje máquina

El Grupo de Tratamiento de Señal (Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, España), ofrece su experiencia en el procesado automático de señales biomédicas. Las principales ventajas de esta tecnología son la reducción de costes, el ahorro de tiempo de proceso y la mayor fiabilidad de los resultados. Se busca cooperación técnica para el desarrollo con financiación interna y externa

Sistemas de monitorización inteligentes basados en redes de sensores con aplicaciones militares, medioambientales, en domótica, seguridad y seguimiento

El Grupo de Tratamiento de Señal (Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, España), ofrece su experiencia en el desarrollo de sistemas de monitorización basados en redes de sensores. Las principales ventajas de esta tecnología son la reducción de costes, el ahorro de tiempo de proceso y la mayor fiabilidad de los resultados. Se busca cooperación técnica para el desarrollo con financiación interna y externa

Multi-source change-point detection over local observation models

In this work, we address the problem of change-point detection (CPD) on high-dimensional, multi-source, and heterogeneous sequential data with missing values. We present a new CPD methodology based on local latent variable models and adaptive factorizations that enhances the fusion of multi-source observations with different statistical data-type and face the problem of high dimensionality. Our motivation comes from behavioral change detection in healthcare measured by smartphone monitored data and Electronic Health Records. Due to the high dimension of the observations and the differences in the relevance of each source information, other works fail in obtaining reliable estimates of the change-points location. This leads to methods that are not sensitive enough when dealing with interspersed changes of different intensity within the same sequence or partial missing components. Through the definition of local observation models (LOMs), we transfer the local CP information to homogeneous latent spaces and propose several factorizations that weight the contribution of each source to the global CPD. With the presented methods we demonstrate a reduction in both the detection delay and the number of not-detected CPs, together with robustness against the presence of missing values on a synthetic dataset. We illustrate its application on real-world data from a smartphone-based monitored study and add explainability on the degree of each source contributing to the detection.This work has been partly supported by Spanish government (AEI/MCI) under grants RTI2018-099655-B-100, PID2021-123182OB-I00, PID2021-125159NB-I00, and TED2021-131823B-I00, by Comunidad de Madrid under grant IND2018/TIC-9649, IND2022/TIC- 23550, by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161, and by Comunidad de Madrid and FEDER through IntCARE-CM

Diseño, Implementación y Verificación de un Sensor de Temperatura CMOS de Bajo Coste y Alta Funcionalidad

En este proyecto, se presenta un sensor de temperatura integrado CMOS basado en la medida de una variable secundaria, cuyo valor es dependiente de la temperatura, como es el tiempo de subida que presenta una señal eléctrica en sus flancos de subida. Con el objetivo de reducir coste y potencia consumida, el sensor integrado de temperatura propuesto genera un pulso con un ancho proporcional a la temperatura medida. Este sensor para realizar la medida elimina la necesidad de tener una señal que sirva de referencia. El área ocupada por este modelo de sensor es de 1.8967mm2, siendo éste fabricado en tecnología CMOS de 0.35µm de 4 capas de metal. Gracias a la excelente linealidad que presenta la salida digital del sensor, el error de medida alcanzado es como máximo de ±0.520ºC. La resolución efectiva mostrada en el caso peor es de 0.7ºC, y el consumo de potencia se encuentra por debajo de los 263µW, con una velocidad de realización de medidas que puede llegar a alcanzar las 1.5x10^6 medidas por segundo

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R) and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy in Madrid, Spain, The foundation de l’avenir, and the Fondation de France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network (TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015– 69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P. Moreno-Muñoz has been supported by FPI grant BES-2016-07762