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Heart rate variability (HRV) is an indicator of autonomic activity. As such, it is said to be able to detect signs of disease and can be used to estimate disease status. Recently, the usefulness of the QT interval has been discussed. The reason for this is that QT interval variability is said to be a risk marker for arrhythmia and cardiovascular death. However, this is only a possibility, and whether it actually functions as a risk marker has not yet been established. All S, Q, R, T and P peaks from electrocardiograms (ECGs) were obtained with several Matlab algorithms and compared, and then RR and QT intervals were calculated. On the other hand, it is generally known that there are clear differences in QT interval and RR interval between men and women and with age. Therefore, this project codified an algorithm for HRV analysis implemented in Matlab to see if there are similar differences in QT, RR interval indicators between men and women and by age. For this purpose, several indicators were implemented in Matlab and applied to the QT and RR intervals based on linear and nonlinear algorithms.

This project, promoted by Coresys Health, aims to enhance the capabilities of medical monitors to monitor the inflammatory response, through the analysis of heart rate variability (HRV) de- rived from the electrocardiogram (ECG) and electroencephalogram (EEG) signals. This would be a big step forward for patient care allowing to monitor the state of the patients through non- invasive methods and even allow for early detection of complications from surgery and sepsis. The objective of the project was to create a database and to identify by signal processing pa- rameters within the HRV and EEG signals that can effectively reflect the inflammatory reflex. A step within this objective included a study of the signal-filtering and spectral performance comparison between the Fast Fourier Transform (FFT) and the Lomb-Scargle periodogram of the HRV signal. The EEG and ECG signals from 28 adult patients undergoing surgery under general anesthesia for routine invasive procedures were used for this study. This database was created during this master’s thesis at "Clinica Interhospital" (Guayaquil, Ecuador). Linear and non-linear from both the time-domain frequency-domain analysis were applied to the EEG and HRV signals, in order to obtain parameters to differentiate between pre-surgical and post-surgical stimulus segments during the operation. Specifically, 5-minute segments were selected before and after any surgical stimuli (patients were under sedation). Linear time-domain and linear frequency- domain standard parameters were taken into account. Furthermore, non-linear time-domain and frequency-domain parameters were obtained through entropy-based algorithms. Statis- tical analyses included a pair-up Wilcoxon test, correlation analysis, linear logistic regression analysis, and linear and non-linear classification methods to evaluate the discriminatory capa- bilities of these parameters. For the single parameter analysis, the findings revealed significant differences (p-value, Aquest projecte, promogut per Coresys Health, té com a objectiu millorar les capacitats dels monitors mèdics per monitorar la resposta inflamatòria, mitjançant l’anàlisi de la variabili- tat de la freqüència cardíaca (HRV) derivada de l’electrocardiograma (ECG) i les senyals de l’electroencefalograma (EEG). Això suposaria un gran pas endavant per a la atenció als pacients, permetent monitorar l’estat dels pacients mitjançant mètodes no invasius i fins i tot permetent la detecció precoç de complicacions de cirurgies i sepsis. L’objectiu del projecte era crear una base de dades i identificar, mitjançant el processament de senyals, els paràmetres dins de les senyals de HRV i EEG que poden reflectir eficaçment el reflex inflamatori. Un pas dins d’aquest objectiu va incloure un estudi de la comparació del rendiment de filtratge de senyals entre la Transformada Ràpida de Fourier (FFT) i el periodograma de Lomb-Scargle de la senyal de HRV. Les senyals d’EEG i ECG de 28 pacients adults sotmesos a cirurgia sota anestèsia general per procediments invasius rutinaris es van utilitzar per a aquest estudi. Aquesta base de dades es va crear durant aquesta tesi de màster a la "Clinica Interhospital" (Guayaquil, Equador). Es van aplicar anàlisis tant lineals com no lineals tant en el domini temporal com en el domini de freqüència a les senyals d’EEG i HRV, per obtenir paràmetres per diferenciar entre segments de pre-estímul i post-estímul quirúrgic durant l’operació. Es van tenir en compte paràmetres estàndard tant en el domini temporal com en el domini de freqüència de HRV. A més, es van obtenir paràmetres no lineals en el domini temporal i de freqüència mitjançant algoritmes basats en entropia. Les anàlisis estadístiques van incloure una prova Wilcoxon de parells, anàlisi de correlació, anàlisi de regressió logística lineal i mètodes de classificació lineals i no lineals per avaluar les capacitats discriminatòries d’aquests paràmetres. Per a l’anàlisi de paràmetres individuals, el, Este proyecto, promovido por Coresys Health, tiene como objetivo mejorar las capacidades de los monitores médicos para seguir la respuesta inflamatoria, mediante el análisis de la variabil- idad de la frecuencia cardíaca (HRV) derivada del electrocardiograma (ECG) y las señales del electroencefalograma (EEG). Esto supondría un gran avance en la atención al paciente, permi- tiendo monitorear el estado de los pacientes a través de métodos no invasivos e incluso permitir la detección temprana de complicaciones quirúrgicas y sepsis. El objetivo del proyecto era crear una base de datos e identificar, mediante el procesamiento de señales, los parámetros dentro de las señales de HRV y EEG que pueden reflejar eficazmente el reflejo inflamatorio. Un paso dentro de este objetivo incluyó un estudio de la comparación del rendimiento de filtrado de señales entre la Transformada Rápida de Fourier (FFT) y el pe- riodograma de Lomb-Scargle de la señal de HRV. Se utilizaron las señales de EEG y ECG de 28 pacientes adultos sometidos a cirugía bajo aneste- sia general para procedimientos invasivos rutinarios en este estudio. Esta base de datos se creó durante esta tesis de máster en la "Clínica Interhospitalaria" (Guayaquil, Ecuador). Se aplicaron análisis tanto lineales como no lineales tanto en el dominio temporal como en el dominio de fre- cuencia a las señales de EEG y HRV, con el fin de obtener parámetros para diferenciar entre segmentos preestímulo y postestímulo quirúrgico durante la operación. Se tuvieron en cuenta parámetros estándar tanto en el dominio temporal como en el dominio de frecuencia de HRV. Además, se obtuvieron parámetros no lineales en el dominio temporal y de frecuencia a través de algoritmos basados en la entropía. Los análisis estadísticos incluyeron una prueba Wilcoxon emparejada, análisis de correlación, análisis de regresión logística lineal y métodos de clasifi- cación lineales y no lineales para evaluar las capacidades discriminatorias de estos parámetro

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing, Peer Reviewed, Postprint (published version)

Avui en dia és possible implantar una pròtesi de vàlvula aòrtica a un pacient sense necessitat de realitzar una cirurgia a cor obert. Les noves tècniques de cateterisme cardíac, permeten millorar la qualitat de vida de pacients que no són aptes per superar una cirurgia d’aquestes característiques per motius de la seva condició física, és a dir, per la seva edat avançada i/o per patir patologies amb risc associat a la intervenció. Aquest projecte es centra en la intervenció TAVI (implant d’una vàlvula aòrtica transcatèter), aquesta intervenció es duu a terme quan el pacient pateix una estenosi aòrtica severa, que vol dir una calcificació de la vàlvula i que requereix reemplaçar-la. En aquesta tècnica es permet substituir la vàlvula malmesa per una pròtesi realitzant únicament una petita incisió a la artèria femoral del pacient. Tot i així, aquesta tècnica requereix una precisió tècnica molt elevada, tant per part del dispositiu mèdic utilitzat com de la manipulació del mateix. Per aquest motiu, és necessari desenvolupar dispositius que siguin molt fiables i fàcils de manipular per tal de reduir el temps d’intervenció i l’aparició de complicacions que puguin posar en risc la vida del pacient. L’objectiu d’aquest projecte és dissenyar i fabricar la vàlvula hemostàtica d’un dels dispositius que s’utilitza durant la intervenció. Aquesta vàlvula hemostàtica és molt important per la intervenció ja que forma part del dispositiu que s’encarrega d’obrir la via de pas per dur a terme la intervenció, que és l’artèria femoral. Aquesta artèria és una de les que més grans i de les que més cabal sanguini transporta en tot l’organisme, per tant, és necessari que hi hagi un bon segellat hemostàtic per evitar una pèrdua excessiva de sang i posar en risc la vida del pacient. Per altra banda, aquesta vàlvula també serveix com a pas d’entrada al cos del pacient de tota la resta de dispositius que s’utilitzen en la intervenció, és adir, que tot dispositiu que ha d’entrar al cos, ho fa a t

El gráfico de Poincaré es un método no lineal que permite analizar la variabilidad de una señal, dibujando dicha señal contra ella misma retardada en el tiempo. Aunque este método ha sido aplicado al análisis del electroencefalograma (EEG) para la medición de niveles de anestesia general, su aplicación en la medición de niveles de anestesia ligera aún no se ha abordado con profundidad. En este trabajo, se consideran varios tiempos de retardo en el gráfico de Poincaré para caracterizar el nivel de anestesia ligera en 110 pacientes sometidos a endoscopia digestiva. Los índices de Poincaré son combinados con parámetros espectrales de señales que contienen información del EEG y del EMG (electromiografía) para modelar los niveles de anestesia ligera, teniendo como referencia la escala de sedación Ramsay. La mejor predicción, utilizando índices del gráfico de Poincaré, del nivel de sedación y analgesia (Pk = 0,711) se obtiene usando un retardo de 8 ms en el rango de frecuencia de 30-110 Hz y de 6 ms en el rango de frecuencia de 0,5 - 30 Hz, respectivamente. La combinación de diferentes parámetros permitió obtener valores de Pk = 0,799., Peer Reviewed, Postprint (published version)

Mantenir el nivell d’anestèsia i nocicepció adequat durant una operació és essencial per el minimitzar riscos durant una cirurgia i al finalitzar-la. L’anestèsia general és la combinació de efecte hipnòtic, per induir l’estat d’inconsciència en el pacient, i analgèsia per prevenir nocicepció. L’analgèsia històricament ha estat administrada basada en senyals clínics durant el procediment mèdic. Malgrat això, recentment s’han començat a utilitzar monitors que proporcionen un valor numèric indicar la profunditat d’anestèsia del pacient. En moltes ocasions aquest índex no dona prou informació sobre l’estat del pacient. És per aquesta raó que en aquest treball s’implementa la funció de l’espectrograma en el monitor d’anestèsia Conox. Aquest document descriu el funcionament de la funció que transforma les dades rebudes en imatges per mostrar-les en pantalla i l’adaptació del codi de la versió prèvia per funcionar en dos modes diferents, mode EEG i mode espectrograma. L’espectrograma ha estat dissenyat de manera que pot ser mostrat amb 3 escales de temps (30 minuts, 2 hores i 6 hores). La interfície ha estat modificada afegint un botó per alternar entre el mode EEG i el mode espectrograma. S’han desactivat els botons que afecten al EEG quan el mode espectrograma està actiu. Per validar l’aplicació s’ha comparat el resultat obtingut per pantalla amb el simulat per Matlab. S’ha utilitzat una base de dades amb pacients en diferents estats clínics i una base de dades sintètica. La base de dades sintètica ha estat creada amb sumatoris de senyals sinusoïdals de diferents freqüències i amplitud, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

Nowadays, one of the main reasons of people’s death in developed countries is due to cardiac causes. The analysis of cardiac electrical series by signal processing using different linear and non-linear techniques could give predictors or markers of sudden cardiac death risk stratification in cardiac pathologies. One of the most analyzed time series is the heart rate variability (HRV). HRV refers to the variations of both instantaneous heart rate and RR intervals (i.e. beat-to-beat intervals) controlled by the Autonomic Nervous System (ANS). In this research project, a set of subjects with different cardiac pathologies has been analyzed using conventional time-domain and frequency-domain linear techniques as well as a nonlinear time-domain technique. The phase rectified signal averaging (PRSA), a non-linear signal processing technique, was applied to assess the risk of suffering sudden cardiac death. Also, subjects classified into different groups of risk (normal or reduced left ventricular ejection fraction, NYHAII or NYHAIII classification and presence or absence of ischemic pathology) were analyzed. Different measures were defined from PRSA, acceleration and deceleration based capacities of the HRV series. The phase-rectified signal averaging, or PRSA, has been demonstrated to be a method that shows clear advantages compared to the conventional analysis methods in the time and frequency domains. While these conventional methods have not been capable of providing results that showed statistically significant differences between the groups of low and high risk of sudden cardiac death at night period HRV recordings and also during 24h HRV recordings, the PRSA method not only proved differences between these groups but also allowed to characterize subjects in other groups.

Maintaining a good level of anesthesia and nociception along a surgery is essential for the both the intraoperative and the postoperative outcomes. General anaesthesia (GA) is the combination of hypnotic effect, in order to achieve unconsciousness of the patient, and analgesia to pervent nociception. Analgesia has been historically administered based on clinical signs during the procedure. However, in the recent years several monitors have appeared on the market in order to try to achieve a metric that is not arbitrary to aid the physicians and medical doctors. Since these monitors are relatively new and there is not yet a golden standard for nociception measurement, there is still plenty of research to carry out. Hence, the aim of this work is to develop mathematical regression models with the purpose of predicting the level of nociception during general anaesthesia. As which are the physiological parameters that carry more relevant information is still uncertain, different combinations of electroencephalography (EEG) and heart rate variability (HRV) parameters have been tried as inputs for these prediction models. The reference target for the models is a combination of the concentration of anaesthetic drug (remifentanil), the consciousness of the patient (qCON index), and the response to tetanic stimuli. The data used for this study was gathered during 475 gynecological ambulatory procedures in Hospital Clinic of Barcelona. The data was split in two different sets of patients, patients for training the models and patients for testing the models in a 1:2 ratio. As the data gathering took five years, in order to prevent the bias in time due to change of the data collector or by the learning process of the collection of data, we grouped the patients chronologically; every three patients the first was re-grouped unto training set and the two remaing unto test set. The selection of the features was carried out through three different methods, RReliefF algorithm, random

Aortic Stenosis (AS) is a form of valvular heart disease that it is in increasing rate nowadays. No effective drug treatment for AS is available. In this way, the analysis of AS with a noninvasive technic such as the analysis of the heart rate variability (HRV) can be a high advance for early cardiac disease detection. In this project, it was analyzed 24-hours Heart Rate Variability (HRV) of patients with Aortic Stenosis which have underwent aortic valvular replacement, suffering some of them sudden cardiac death during the follow up. The HRV has been analyzed by applying a methodology base on symbolic dynamics, using a Non-equidistant (NEQ) algorithm and an equidistant (EQ) algorithm. From this methodology, several variables were proposed and validated for their prediction ability, in order to differentiate groups of patients with different cardiac risk of suffering sudden cardiac death. In this way, the RR cardiac time series analysis has permitted to statistically describe patients that died for cardiac reasons during the valvular surgery (Operdeath), those that died five years after the surgery (Death-five-years) and all patients that died during the follow-up of the five years (Death-alive-total). Furthermore, RR cardiac series analysis has permitted to statistically describe patients with different levels, Left Ventricular Ejection Fraction (LVEF) and New York Heart Association (NYHA).

Anticoagulant drugs are essential treatments of cardiovascular diseases like myocardial infarction and stroke, but their effective use requires achieving a careful balance between suppressing pathological clotting (thrombosis) and compromising natural coagulation capabilities (hemostasis). Striking such a balance is difficult given the dynamic network of cellular and molecular reactions that take place in flowing blood. Embracing a systems pharmacology approach, computational and experimental models have been used to map the dynamic response of a novel class of Factor XI inhibitors, and explore their potential as anticoagulant medications with a wide risk/benefit margin. Systems pharmacology, computational model, blood coagulation., Outgoing

System identification is a methodology for building mathematical models of dynamic systems using measurements of the system’s input and output signals. The process of system identification requires work described below. First, measure the input and output signals from your system in time or frequency domain. Next, select a model structure. Apply an estimation method to estimate value for the adjustable parameters in the candidate model structure. And then, evaluate the estimated model to see if the model is adequate for your application needs.In particular, this process is called validation process. Black-box models are the functional relationships between system inputs and system outputs. In a dynamic system, the values of the output signals depend on both the instantaneous values of its input signals and also on the past behavior of the output system. In this project, we identify the system from the HRV (Heart Rate Variability) obtained of several ECG (Electrocardiogram) . We used MATLAB for analyzing the data. ECG is the physiological signal and it is the recording of electrical activity of the myocardium of the heart during each cardiac cycle. ECG wave forms consist of each waves of P , QRS , T , U. We use the ECG data from PhysioNet. This web site provides a lot of physiological signal data as open source. We attempt to obtain RR intervals (RRI) from these data. We have written MATLAB scripts in order to codify the models and also we have used the identification MATLAB app. Setting the input and output in matlab app and detrend, select polynomial model and try several models. We set as input random noise and set as output the RRI. After we obtained the models, we can compare its errors and how the model fits the signal., Incoming

Cardiovascular diseases are the leading cause of death worldwide. In this way, it is of high relevance to stratify high and low risk of developing sudden cardiac death (SCD) in those patients that present a cardiac pathology, and to predict this fatal development. The extraction of quantitative markers from the electrocardiogram (ECG) time-series data is nowadays a difficult but promising task. The signal processing methodologies can be applied for this purpose. In this way, the main objective of this project was to characterise differences between cardiac pathologies, discriminating between high and low risk of sudden cardiac death by using different Poincaré plot measures (non-linear dynamics analysis). Finally, it was also pursued to find an individual predictor from each of the Poincaré plot measures. Patient position, tilt versus supine, was analysed from RR beat series, showing a higher complexity in supine position (SD1, SD2, Ratio, Area). Tilt position did not present significant differences between low risk and high risk of suffering sudden cardiac death. Supine position, presented significant differences from SD2 parameter with a higher complexity in sudden cardiac death group. Finally, analysing RR and QT heart-beat series behaviour obtained from 24h-ECG Holter recordings by SD1, SD2, Ratio and Area parameters, it can be stated that SCD patients presented less complexity behaviour of their cardiac series recordings. Ischemic patient group (compared with non-ischemic), showed different responses during day and night, with a more complex behaviour during day period of the cardiac series. Furthermore, all RR series presented more predictability than QT series. Lastly, this analysis showed that QT beat series presented a decreasing slope from day to night, mainly in SD1, SD2 and Area, maybe due to the intrinsic information from the beginning of the depolarisation to the end of QT-wave repolarisation.

To analyse the responses of the autonomic nervous system (ANS) to different cardiac activity in different athlete’s performances and at different altitudes can provide knowledge about the mechanism of the neural control and regulation linked to sport. In this way, the heart rate variability (HRV), which can be measured as the time between consecutive heart beats, is an accurate and non-invasive measure of the ANS. The effect of ANS on the cardiac activity is done through two branches, sympathetic stimulation and parasympathetic or vagal stimulation path. To probe how the cardiac system adjusts its rhythm depending on the subject physical condition and on the altitude effect, heart rate variability (HRV) was studied in time-domain (TD) and frequency-domain (FD) based processing techniques. Furthermore, in order to describe certain dynamics processes of the cardiac system, some non-linear time-domain signal processing methods have been applied. A total of 29 subjects (13 high trained and 16 low trained athletes) were tested doing incremental exercise in two different altitudes, Girona (sea level) and Masella (mountain altitude, 1800m approximately). HRV was assessed by linear TD indices defined in [Tas96], those estimated using auto correlation function (ACF) and the spectral measures resulting from power spectral density (PSD) analysis, as well as, non-linear TD indices derived from recurrence plots (RP), auto mutual information function (AMIF) and entropy information. The results suggest that there are significant differences of the HRV performances when it is studied at sea level and at mountain altitude, and also differences at ANS responses. Some statistical differences were observed concerning to high and low trained athletes. However, when athletes with better conditions (high VO2max) were compared to those with worse conditions, it could be seen that HRV at sea level presents more complexity behaviour (less regularity and less complexity behaviour (more regula

Pla de Doctorat Industrial de la Generalitat de Catalunya. Aplicat embargament des de la data de defensa fins l'1 de maig de 2021, Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthesia is known to affect cerebral hemodynamics , but CBF is only monitored in critical patients due to the lack of a continuous and affordable bedside monitor for this purpose. This Thesis propases a potential solution through bioelectrical impedance, also known as rheoencephalography (REG), that could fill the existing gap for a low-cost and effective CBF monitor. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF changes during anesthesia. Firstly, clinical and technical information related to other CBF monitoring methods and signal processing algorithms suitable for REG signals are provided. Subsequently, clinical data collected under the scope of this project and used to develop and validate the techniques applied to REG signals are described. As a first step in the analysis of REG signals, different filter options for noise removal are presented, in the linear and nonlinear domain. The nonlinear filler applied to the signal attractor showed a better accuracy, mainly in noisy environments, but its computational burden compromises its use in real time monitoring of physiological signals. The analysis of REG starts with the use of geometric features extracted from the time domain, which is the standard processin strategy for this type of data. Geometric features are tested for their ability to detect apneas in young healthy volunteers and to distinguish between different anesthetic depths. Their performance is poor in apnea detection, but they are capable oftracking cerebral hemodynamic changes during anesthesia. Two new approaches are proposed, one based on the nonlin, El flujo sanguíneo cerebral (FSC) refleja el suministro de sangre arterial al cerebro. Como no se pueden guardar nutrientes, oxigeno ni agua en la cavidad craneal por restricciones de espacio y presión, una perfusión continua es fundamental para la supervivencia. La anestesia afecta la hemodinámica cerebral, pero el FSC sólo se monitoriza en pacientes críticos porque no existen dispositives de cabecera, con monitorización continua y a precios asequibles. Esta Tesis propone una posible solución a través de la impedancia bioeléctrica o rheoencefalografia (REG), que puede ocupar el vacío existente en la monitorización del FSC efectiva y de bajo coste. La hipótesis subyacente es que REG tiene información de FSC que podría recuperarse aplicando técnicas avanzadas de procesado, permitiendo detectar las alteraciones de FSC en anestesia. En primer lugar, se presenta una revisión de la literatura, con información clínica y técnica relevante de métodos de monitorización de FSC y algoritmos de procesado de señal aplicables a señales REG. A continuación se describen los datos clínicos recogidos en el ámbito de este proyecto, usados para desarrollar y validar las técnicas aplicadas a señales REG. Como primer paso en el análisis de REG, se presentan distintas opciones de filtros de eliminación de ruido, en el dominio lineal y no lineal. El filtro no lineal aplicado al atractor de la señal ofrece una mayor precisión, especialmente en ambientes muy ruidosos, però su carga computacional compromete su uso para monitorización en tiempo real de señales fisiológicas. El análisis de las señales REG empieza con el estudio de características geométricas extraídas del dominio temporal, la técnica de procesado más común para este tipo de datos. Se evalúa la capacidad de dichas características para detectar apneas en voluntarios jóvenes sanos y para distinguir entre distintas profundidades anestésicas. Su rendimiento es pobre en la detección de apneas, pero son capaces de detectar cambios hem, Postprint (published version)

Aortic stenosis (AS) is a form of valvular heart disease that is bound to be seen more often by healthcare professionals in the coming years. This is due to the increasing life expectancy. Aortic stenosis incapacitates the aortic valve to open completely. This condition means that the valve gets narrower, causing a decrease in blood flow, which may cause the death of the patient. The origin of this pathology may be congenital or acquired, which usually affects older people. No effective drug treatment for AS is available. Symptomatic patients with significant AS should be referred for surgery. The analysis of AS with a non-invasive way such as the analysis of the heart rate variability (HRV) can be a high advance for early cardiac disease detection. In the present project, it has been done a study on HRV in patients affected of aortic stenosis. HRV signals were obtained from 24h-Holter ECG recordings. The HRV has been analyzed by applying a non-linear time-domain method called Multiscale Entropy (MSE) that extends entropies to multiple signal resolutions to provide assessment of the complexity of a time series. Furthermore, linear time-domain and frequency-domain analysis has been perform. Several comparison test have been applied between results of those patients who suffered sudden cardiac death from those who survive the valvular surgery in a follow-up of 5 years.

Objective: Interest in emotion recognition has increased in recent years as a useful tool for diagnosing psycho-neural illnesses. In this study, the auto-mutual and the cross-mutual information function, AMIF and CMIF respectively, are used for human emotion recognition. Approach: The AMIF technique was applied to heart rate variability (HRV) signals to study complex interdependencies, and the CMIF technique was considered to quantify the complex coupling between HRV and respiratory signals. Both algorithms were adapted to short-term RR time series. Traditional band pass filtering was applied to the RR series at low frequency (LF) and high frequency (HF) bands, and a respiration-based filter bandwidth was also investigated (). Both the AMIF and the CMIF algorithms were calculated with regard to different time scales as specific complexity measures. The ability of the parameters derived from the AMIF and the CMIF to discriminate emotions was evaluated on a database of video-induced emotion elicitation. Five elicited states i.e. relax (neutral), joy (positive valence), as well as fear, sadness and anger (negative valences) were considered. Main results: The results revealed that the AMIF applied to the RR time series filtered in the band was able to discriminate between the following: relax and joy and fear, joy and each negative valence conditions, and finally fear and sadness and anger, all with a statistical significance level p¿-value 0.05, sensitivity, specificity and accuracy higher than 70% and area under the receiver operating characteristic curve index AUC 0.70. Furthermore, the parameters derived from the AMIF and the CMIF allowed the low signal complexity presented during fear to be characterized in front of any of the studied elicited states. Significance: Based on these results, human emotion manifested in the HRV and respiratory signal responses could be characterized by means of the information-content complexity, Peer Reviewed, Postprint (author's final draft)

The refined multiscale entropy (RMSE) approach is commonly applied to assess complexity as a function of the time scale. RMSE is normally based on the computation of sample entropy (SampEn) estimating complexity as conditional entropy. However, SampEn is dependent on the length and standard deviation of the data. Recently, fuzzy entropy (FuzEn) has been proposed, including several refinements, as an alternative to counteract these limitations. In this work, FuzEn, translated FuzEn (TFuzEn), translated-reflected FuzEn (TRFuzEn), inherent FuzEn (IFuzEn), and inherent translated FuzEn (ITFuzEn) were exploited as entropy-based measures in the computation of RMSE and their performance was compared to that of SampEn. FuzEn metrics were applied to synthetic time series of different lengths to evaluate the consistency of the different approaches. In addition, electroencephalograms of patients under sedation-analgesia procedure were analyzed based on the patient’s response after the application of painful stimulation, such as nail bed compression or endoscopy tube insertion. Significant differences in FuzEn metrics were observed over simulations and real data as a function of the data length and the pain responses. Findings indicated that FuzEn, when exploited in RMSE applications, showed similar behavior to SampEn in long series, but its consistency was better than that of SampEn in short series both over simulations and real data. Conversely, its variants should be utilized with more caution, especially whether processes exhibit an important deterministic component and/or in nociception prediction at long scales, Peer Reviewed, Postprint (published version)

Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis, Peer Reviewed, Postprint (published version)

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Developing a tool which identifies emotions based on their effect on cardiac activity may have a potential impact on clinical practice, since it may help in the diagnosing of psycho-neural illnesses. In this study, a method based on the analysis of heart rate variability (HRV) guided by respiration is proposed. The method was based on redefining the high frequency (HF) band, not only to be centered at the respiratory frequency, but also to have a bandwidth dependent on the respiratory spectrum. The method was first tested using simulated HRV signals, yielding the minimum estimation errors as compared to classical and respiratory frequency centered at HF band based definitions, independently of the values of the sympathovagal ratio. Then, the proposed method was applied to discriminate emotions in a database of video-induced elicitation. Five emotional states, relax, joy, fear, sadness and anger, were considered. The maximum correlation between HRV and respiration spectra discriminated joy vs. relax, joy vs. each negative valence emotion, and fear vs. sadness with p-value = 0.05 and AUC = 0.70. Based on these results, human emotion characterization may be improved by adding respiratory information to HRV analysis., Peer Reviewed, Postprint (author's final draft)

The autonomic nervous system (ANS) is the control mechanism of heart rate variability (HRV). Assessing HRV can lead to prominent information about this control system, taking into account the two control sympathetic and vagal branches. HRV can be evaluated by analyzing RR time series (consecutive heart beats) from linear methods in time and frequency domain in order to extract HRV indexes. Moreover, it is known that heart rate dynamics is not strictly linear, this is the reason that analyzing HRV dynamics by non-linear techniques can provide additional and essential information to the ANS. This project focuses on the quantification of several linear and non-linear metrics from athletes’ RR recordings obtained during a trail and rest conditions, concretely, the days before and after this effort under supine and orthostatic positions.

The excessive daytime sleepiness (EDS) is one of the symptoms of several sleep-related disorders. Several investigations sustain that there are more affected people than the clinically diagnosed, while many studies have been carried out to assess daytime sleepiness, automatic EDS detection remains an open problem. The protocols for sleepiness detection are complicated, long and annoying for the patients. Thereby, the electroencephalogram (EEG) signal has been introduced to provide additional, valuable and non-intrusive information about the state of the patient. In this project, two methodologies based on linear frequency domain analysis (Power Spectral Density (PSD)) and nonlinear time domain analysis (Multiscale entropy (MSE)) has been developed and applied to the pre-processed EEG signals (once eliminated the EEG artifacts with two Butterworth filters (band-pass (0.3-70Hz) and Notch) and applied the independent component analysis (ICA) ), for sleepiness characterization. In this way, different indices have been defined in order to best describe the different EEG behavior between EDS and without daytime sleepiness (WDS) patients. It has been found that both MSE and PSD techniques contribute to highlight the EEG characteristics of both groups of patients (EDS and WDS), and therefore, these methodologies can help to find EEG indices which define this sleep disease and thus, it can be detected more easily.

Objective: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers. Methods:A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (t). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection. Results:Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R. Conclusions Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changes, Peer Reviewed, Postprint (published version)

La somnolencia diurna excesiva es un o de los síntomas provenientes de varios trastornos relacionados con el sueño. Diferentes investigaciones afirman que hay más personas afectadas de somnolencia diurna que las diagnosticadas clínicamente , por este motivo se realizan estudios clínicos para detectarla. Los protocolos p ara la identificación de somnolencia son complica dos, largos y molestos para los pacientes . Esto conlleva a la necesidad de realizar estudios basados en el procesado de la señal electrocardiográfica (EEG) para la detección automática de esta enfermedad. El análisis de la señal EEG proporciona información valios a y de forma no invasiva sobre el esta do del paciente. Para caracterizar la somnolencia, en el presente estudio se han desarrol lado dos metodologías basadas en el análisis lineal de la señal EEG en el dominio de la frecuencia (densidad de potencia espectral (PSD)) y en el análisis no lineal de la señal en el dominio del tiempo (entropía multi escala (MSE)). Para ello se ha analizado la señal EEG de 19 electrodos superficiales . Diferentes índices han sido definidos con el fin de describir el comportamiento del E EG entre pacientes con somnolencia diurna (CSD) y pacientes sanos ( sin somnolencia diurna (S S D )). Se ha encontrado que tanto las técnicas MSE como PSD contribuyen a destacar l a s características del EEG de l o s dos grupos de pacientes (CSD y SSD ) y por lo tanto, facilitan encontrar índices que caracterizan la enfermedad y así detectar la más fácilmente ., Postprint (author's final draft)

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P¿=¿0.780) and the extended modified beta distribution (P¿=¿0.765) provided similar results, higher than the rest of analyzed kernels., Peer Reviewed, Postprint (published version)

Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way., Peer Reviewed, Postprint (author's final draft)

Aplicat embargament des de la data de defensa fins els maig 2020., Cardiac output (CO) defines the blood flow arriving from the heart to the different organs in the body and it is thus a primary determinant of global 02 transport. Cardiac output has traditionally been measured using invasive methods, whose risk sometimes exceeds the advantages of a cardiac output monitoring. In this context, the minimization of risk in new noninvasive technologies for CO monitoring could translate into major advantages for clinicians, hospitals and patients: ease of usage and availability, reduced recovery time, and improved patient outcome. Impedance Cardiography (ICG) is a promising noninvasive technology for cardiac output monitoring but available information on the ICG signals is more scare than other physiological signals such as the electrocardiogram (ECG). The present Doctoral Thesis contributes to the development of signal treatment techniques for the ICG in order to create an innovative hemodynamic monitor. First, an extensive literature review is provided regarding the basics of the clinical background in which cardiac output monitoring is used and concerning the state of the art of cardiac output monitors on the market. This Doctoral Thesis has produced a considerable amount of clinical data which is also explained in detail. These clinical data are also useful to complement the theoretical explanation of patient indices such as heart rate variability, blood flow and blood pressure. In addition, a new method to create synthetic biomedical signals with known time-frequency characteristics is introduced. One of the first analysis in this Doctoral Thesis studies the time difference between peak points of the heart beats in the ECG and the ICG: the RC segment. This RC segment is a measure of the time delay between electrical and mechanical activity of the heart. The relationship of the RC segment with blood pressure and heart interval is analyzed. The concordance of beat durations of both the electrocardiogram and the impedance cardiogram is, El gasto cardíaco (GC) define el flujo de sangre que llega desde el corazón a los distintos órganos del cuerpo y es, por tanto, un determinante primario del transporte global de oxígeno. Se ha medido tradicionalmente usando métodos invasivos cuyos riesgos excedían en ocasiones las ventajas de su monitorización. En este contexto, la minimización del riesgo de la monitorización del gasto cardíaco en nuevas tecnologías no invasivas podría traducirse en mayores ventajas para médicos, hospitales y pacientes: facilidad de uso, disponibilidad del equipamiento y menor tiempo de recuperación y mejores resultados en el paciente. La impedancio-cardiografía o cardiografía de impedancia (ICG} es una prometedora tecnología no invasiva para la monitorización del gasto cardíaco. Sin embargo, la información disponible sobre las señales de ICG es más escasa que otras señales fisiológicas como el electrocardiograma (ECG). La presente Tesis Doctoral contribuye al desarrollo de técnicas de tratamiento de señal de ICG para así crear un monitor hemodinámico innovador. En primer lugar, se proporciona una extensa revisión bibliográfica sobre los aspectos básicos del contexto clínico en el que se utiliza la monitorización del gasto cardíaco así como sobre el estado del arte de los monitores de gasto cardíaco que existen en el mercado. Esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos que también se explican en detalle. Dichos datos clínicos también son útiles para complementar las explicaciones teóricas de los índices de paciente de variabilidad cardíaca y el flujo y la presión sanguíneos. Además, se presenta un nuevo método de creación de señales sintéticas biomédicas con características de tiempo-frecuencia conocidas. Uno de los primeros análisis de esta Tesis Doctoral estudia la diferencia temporal entre los picos de los latidos cardíacos del ECG y del ICG: el segmento RC. Este segmento RC es una medida del retardo temporal entre la actividad eléctrica y mecánic, Postprint (published version)

The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports, Peer Reviewed, Postprint (author's final draft)

En la actualidad, la monitorización del dolor es un tema muy investigado. La preocupación del paciente en todas las áreas de la salud siempre es la misma, ¿me dolerá? El dolor es una sensación completamente subjetiva y versátil y justamente es por eso que no está completamente resuelto. De modo que en l a present e investigación se ha llevado a cabo el registro de diversas señales fisiológicas de 26 voluntarios sanos del sexo masculino para poder realizar un estudio de cuál de estas variables o ptim izaría la monitorización del dolor en las personas. Los parámetros r egistrados son el ritmo cardíac o , la variabilidad de la frecuencia cardíac a , la impedancia de la piel y la señal electroencefalográfica . Se concluye qu e tanto el protocolo establecido como el estímulo doloroso elegido han permitido observar variaciones en los parámetros analizados., Postprint (author's final draft)

Postprint (author's final draft)

© 2017 by the authors. The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports., Peer Reviewed, Postprint (author's final draft)

Analysis of the cardiac output during Valsalva maneuver and passive leg raising in order to perform a clinical study. To do the measures the qCO (Quantium Medical, Mataró, Spain) has been used, a medical device that measures the cardiac output non-invasively by bioimpedance. The test to be performed is defined using a protocol that determines the duration of the measures and how to perform it. The registers were performed to healthy volunteers. A statistical study has been done to find differences between the different states of the test and differences between characteristic groups of volunteers; sex, sport or smokers.

En la actualidad, la monitorización del dolor es un tema muy investigado. La preocupación del paciente en todas las áreas de la salud siempre es la misma, ¿me dolerá? El dolor es una sensación completamente subjetiva y versátil y justamente es por eso que no está completamente resuelto. De modo que en el presente proyecto se ha llevado a cabo el registro de diversas señales fisiológicas muy diversas entre ellas de 26 voluntarios sanos del sexo masculino para poder realizar una investigación y estudio de cuál de estas variables sería más óptima para la monitorización del dolor en las personas. Los parámetros registrados son el ritmo o frecuencia cardíaca (FC), la variabilidad de la frecuencia cardíaca (VFC), la conductancia o impedancia de la piel (CP) y la señal electroencefalográfica (EEG).