Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Mindfulness-based meditation, interoceptive awareness and heart rate variability

La tesi di dottorato dal titolo “Mindfulness based meditation, Interoceptive awareness and Heart rate variability (tutor: prof. Antonino Raffone; cotutor: prof.ssa Maria Casagrande) affronta la relazione tra l'esperienza del corpo che si svolge sia esplicitamente, limitata alla sua dimensione più sottile e cioè la consapevolezza interocettiva, che implicitamente nel corso dell'esperienza emotiva, e la variabilità della frequenza cardiaca intesa come indicatore di adattamento all'ambiente e di regolazione emotiva, eventualmente modulate dalla meditazione di consapevolezza declinata in diversi contesti. Dopo un primo capitolo introduttivo in cui descrivo per sommi capi l’evoluzione nel tempo degli studi sulla meditazione, nei primi capitoli vengono definiti ed approfonditi gli elementi oggetto di studio. Il secondo capitolo è perciò dedicato alla meditazione mindfulness, alle sue origini buddhiste ed alle sue misure, le sue diverse declinazioni sia nei percorsi strutturati quali MBSR e MBCT che nei più recenti approcci terapeutici di psicoterapia cognitiva di terza generazione. Il terzo capitolo è dedicato alla consapevolezza interocettiva ed alle sue misure. Il quarto capitolo alla variabilità cardiaca, inclusa la descrizione dei parametri che rendono possibile il suo studio, con un’attenzione particolare al valore di indice di regolazione emotiva mediata dal vago ed alla descrizione delle sue misure. Quindi i successivi quattro capitoli sono dedicati alla presentazione dei diversi studi fatti con due gruppi in due diversi contesti, e cioè nel corso di un percorso MBSR e di un ritiro di Insight, riportando il processo delle analisi, i risultati e le conclusioni. Infine nell’ultimo capitolo ho inserito una discussione sull’insieme del lavoro fatto, incluse le problematicità riscontrate e le possibilità di approfondimento in studi successivi

Study of Heart Rate Variability in Bipolar Disorder: Linear and Non-Linear Parameters during Sleep.

The aim of the study is to define physiological parameters and vital signs that may be related to the mood and mental status in patients affected by bipolar disorder. In particular we explored the autonomic nervous system through the analysis of the heart rate variability. Many different parameters, in the time and in the frequency domain, linear and non-linear were evaluated during the sleep in a group of normal subject and in one patient in four different conditions. The recording of the signals was performed through a wearable sensorized T-shirt. Heart rate variability (HRV) signal and movement analysis allowed also obtaining sleep staging and the estimation of REM sleep percentage over the total sleep time. A group of eight normal females constituted the control group, on which normality ranges were estimated. The pathologic subject was recorded during four different nights, at time intervals of at least 1 week, and during different phases of the disturbance. Some of the examined parameters (MEANNN, SDNN, RMSSD) confirmed reduced HRV in depression and bipolar disorder. REM sleep percentage was found to be increased. Lempel–Ziv complexity and sample entropy, on the other hand, seem to correlate with the depression level. Even if the number of examined subjects is still small, and the results need further validation, the proposed methodology and the calculated parameters seem promising tools for the monitoring of mood changes in psychiatric disorders

Associating Frailty and Dynamic Dysregulation between Motor and Cardiac Autonomic Systems

Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p<0.01, effect size=0.81$\pm$0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of 82% to 89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty.Comment: 16 pages, 3 tables, 4 figure

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety

A recent suicide attempt and the heartbeat: Electrophysiological findings from a trans-diagnostic cohort of patients and healthy controls

Suicidal behavior is influenced by a multitude of factors, making prediction and prevention of suicide attempts (SA) a challenge. A useful tool to uncover underlying pathophysiology or propose new therapy approaches are biomarkers, especially within the context of point-of-care tests. Heart rate variability (HRV) is a well-established biomarker of mental health, and measures the activity of the sympathetic and parasympathetic nervous system (PNS). Previous studies reported a correlation between lower PNS activity and suicidality. However, most studies involved participants from a healthy population, patients without history of suicide attempts, or patients with a single diagnosis. 52 in-patients with a recent suicide attempt (<6 months), and 43 controls without history of SA or psychiatric diagnoses confirmed study participation. The included patients age ranged between 18 and 65 years, 65% had psychiatric comorbidities. Patients with dementia, cognitive impairments, acute psychosis, chronic non suicidal self-harming behavior, or current electroconvulsive therapy were excluded. A 15-min resting state electrocardiography was recorded with two bipolar electrodes attached to the right and left insides of the wrists. The multiple regression analyses showed lower parasympathetic, and higher sympathetic activity in patients compared to controls. Partial correlation found a positive trend result between self-reported suicidality and the very low frequency band. ROC curve analysis revealed an acceptable to excellent clinical accuracy of HRV parameters. Therefore, HRV parameters could be reliable discriminative biomarkers between in-patients with a recent SA and healthy controls. One limitation is the lack of a control group consisting of in-patients without life-time suicidal ideation or attempts

Somatic salience and sensory precision in persistent depression

Persistent depression is a debilitating health condition with a poor prognosis even in the context of current gold-standard pharmacological and psychological interventions. A better understanding of the mechanisms contributing to its maintenance is needed to facilitate the development of more targeted psychological interventions. Bayesian predictive processing models of depression propose that negative emotional and physiological outcomes arise in depressive illness as a result of disturbed interoceptive precision estimation in depressed individuals; however, evidence from the clinical and cognitive neuroscience literatures suggests the hypothesis that sensory precision is attenuated in persistent depression across sensory modalities in general. A series of studies was designed to index sensory precision across somatic and auditory modalities and to identify the level at which any disruption manifested in persistently-depressed participants relative to controls. Study 1 (Chapter 3) measured baseline signal discriminability under conditions of focused attention. Study 2 (Chapter 4) measured the impact of failures of voluntary attention on signal discriminability. Study 3 (Chapter 5) used a simulation approach to model sensory precision in the first two studies and to identify mechanisms which could successfully predict the data. Studies 4 (Chapter 6) and 5 (Chapter 7) measured attentional capture by task-irrelevant and predictive sensory cues respectively. Study 6 (Chapter 8) partially replicated Studies 4 and 5 and used the resulting data to estimate the group-level sensory precision and salience parameters of a predictive processing model of precision optimization. The results suggest that sensory precision is attenuated in persistent depression across sensory modalities, and that this attenuation results from disturbances of voluntary and involuntary attention rather than baseline perceptual sensitivity. Under conditions of voluntary attention, reduced sensory precision may result from efforts at resource conservation; and under conditions of involuntary attentional capture, it may be related to a loss of target discriminability and salience. Conversely, the bottom-up salience of somatic stimuli was uniquely enhanced among depressed participants and was predicted by high anxiety and by low interoceptive sensibility. These findings open up new avenues for investigation of the mechanisms underlying persistent forms of depression, and have direct implications for clinical practice with respect to psychological intervention.Medical Research Council Studentshi

Autonomic nervous system biomarkers from multi-modal and model-based signal processing in mental health and illness

Esta tesis se centra en técnicas de procesado multimodal y basado en modelos de señales para derivar parámetros fisiológicos, es decir, biomarcadores, relacionados con el sistema nervioso autónomo (ANS). El desarrollo de nuevos métodos para derivar biomarcadores de ANS no invasivos en la salud y la enfermedad mental ofrece la posibilidad de mejorar la evaluación del estrés y la monitorización de la depresión. Para este fin, el presente documento se estructura en tres partes principales. En la Parte I, se proporciona unaintroducción a la salud y la enfermedad mental (Cap. 1). Además, se presenta un marco teórico para investigar la etiología de los trastornos mentales y el papel del estrés en la enfermedad mental (Cap. 2). También se destaca la importancia de los biomarcadores no invasivos para la evaluación del ANS, prestando especial atención en la depresión clínica (Cap. 3, 4). En la Parte II, se proporciona el marco metodológico para derivar biomarcadores del ANS. Las técnicas de procesado de señales incluyen el análisis conjunto de la variabilidad del rítmo cardíaco (HRV) y la señal respiratoria (Cap. 6), técnicas novedosas para derivar la señal respiratoria del electrocardiograma (ECG) (Cap. 7) y un análisis robusto que se basa en modelar la forma de ondas del pulso del fotopletismograma (PPG) (Ch. 8). En la Parte III, los biomarcadores del ANS se evalúan en la quantificacióndel estrés (Cap. 9) y en la monitorización de la depresión (Ch. 10).Parte I: La salud mental no solo está relacionada con ese estado positivo de bienestar, en el que un individuo puede enfrentar a las situaciones estresantes de la vida, sino también con la ausencia de enfermedad mental. La enfermedad o trastorno mental se puede definir como un trastorno emocional, cognitivo o conductual que causa un deterioro funcional sustancial en una o más actividades importantes de la vida. Los trastornos mentales más comunes, que muchas veces coexisten, son la ansiedad y el trastorno depresivo mayor (MDD). La enfermedad mental tiene un impacto negativo en la calidad de vida, ya que se asocia con pérdidas considerables en la salud y el funcionamiento, y aumenta ignificativamente el riesgo de una persona de padecer enfermedades ardiovasculares.Un instigador común que subyace a la comorbilidad entre el MDD, la patologíacardiovascular y la ansiedad es el estrés mental. El estrés es común en nuestra vida de rítmo rapido e influye en nuestra salud mental. A corto plazo, ANS controla la respuesta cardiovascular a estímulos estresantes. La regulación de parámetros fisiológicos, como el rítmo cardíaco, la frecuencia respiratoria y la presión arterial, permite que el organismo responda a cambios repentinos en el entorno. Sin embargo, la adaptación fisiológica a un fenómeno ambiental que ocurre regularmente altera los sistemas biológicos involucrados en la respuesta al estrés. Las alteraciones neurobiológicas en el cerebro pueden alterar lafunción del ANS. La disfunción del ANS y los cambios cerebrales estructurales tienen un impacto negativo en los procesos cognitivos, emocionales y conductuales, lo que conduce al desarrollo de una enfermedad mental.Parte II: El desarrollo de métodos novedosos para derivar biomarcadores del ANS no invasivos ofrece la posibilidad de mejorar la evaluacón del estrés en individuos sanos y la disfunción del ANS en pacientes con MDD. El análisis conjunto de varias bioseñales (enfoquemultimodal) permite la cuantificación de interacciones entre sistemas biológicos asociados con ANS, mientras que el modelado de bioseãles y el análisis posterior de los parámetros del modelo (enfoque basado en modelos) permite la cuantificación robusta de cambios en mecanismos fisiológicos relacionados con el ANS. Un método novedoso, quetiene en cuenta los fenómenos de acoplo de fase y frecuencia entre la respiración y las señales de HRV para evaluar el acoplo cardiorrespiratorio no lineal cuadrático se propone en el Cap. 6.3. En el Cap. 7 se proponen nuevas técnicas paramejorar lamonitorización de la respiración. En el Cap. 8, para aumentar la robustez de algunas medidas morfológicas que reflejan cambios en el tonno arterial, se considera el modelado del pulso PPG como una onda principal superpuesta con varias ondas reflejadas.Parte III: Los biomarcadores del ANS se evalúan en la cuantificación de diferentes tipos de estrés, ya sea fisiológico o psicológico, en individuos sanos, y luego, en la monitorización de la depresión. En presencia de estrés mental (Cap. 9.1), inducido por tareas cognitivas, los sujetos sanos muestran un incremento en la frecuencia respiratoria y un mayor número de interacciones no lineales entre la respiración y la seãl de HRV. Esto podría estar asociado con una activación simpática, pero también con una respiración menos regular. En presencia de estrés hemodinámico (Cap. 9.2), inducido por un cambio postural, los sujetos sanos muestran una reducción en el acoplo cardiorrespiratoriono lineal cuadrático, que podría estar relacionado con una retracción vagal. En presencia de estrés térmico (Cap. 9.3), inducido por la exposición a emperaturas ambientales elevadas, los sujetos sanos muestran un aumento del equilibrio simpatovagal. Esto demuestra que los biomarcadores ANS son capaces de evaluar diferentes tipos de estrés y pueden explorarse más en el contexto de la monitorización de la depresión. En el Cap. 10, se evalúan las diferencias en la función del ANS entre elMDD y los sujetos sanos durante un protocolo de estrés mental, no solo con los valores brutos de los biomarcadores del ANS, sino también con los índices de reactividad autónoma, que reflejan la capacidad deun individuo para afrontar con una situación desafiante. Los resultados muestran que la depresión se asocia con un desequilibrio autonómico, que se caracteriza por una mayor actividad simpática y una reducción de la distensibilidad arterial. Los índices de reactividad autónoma cuantificados por cambios, entre etapas de estrés y de recuperación, en los sustitutos de la rigidez arterial, como la pérdida de amplitud de PPG en las ondas reflejadas, muestran el mejor rendimiento en términos de correlación con el grado de la depresión, con un coeficiente de correlación r = −0.5. La correlación negativa implicaque un mayor grado de depresión se asocia con una disminución de la reactividadautónoma. El poder discriminativo de los biomarcadores del ANS se aprecia también por su alto rendimiento diagnóstico para clasificar a los sujetos como MDD o sanos, con una precisión de 80.0%. Por lo tanto, se puede concluir que los biomarcadores del ANS pueden usarse para evaluar el estrés y que la distensibilidad arterial deteriorada podría constituir un biomarcador de salud mental útil en el seguimiento de la depresión.This dissertation is focused on multi-modal and model-based signal processing techniques for deriving physiological parameters, i.e. biomarkers, related to the autonomic nervous system (ANS). The development of novel approaches for deriving noninvasive ANS biomarkers in mental health and illness offers the possibility to improve the assessment of stress and the monitoring of depression. For this purpose, the present document is structured in three main parts. In Part I, an introduction to mental health and illness is provided (Ch. 1). Moreover, a theoretical framework for investigating the etiology of mental disorders and the role of stress in mental illness is presented (Ch. 2). The importance of noninvasive biomarkers for ANS assessment, paying particular attention in clinical depression, is also highlighted (Ch. 3, 4). In Part II, themethodological framework for deriving ANS biomarkers is provided. Signal processing techniques include the joint analysis of heart rate variability (HRV) and respiratory signals (Ch. 6), novel techniques for deriving the respiratory signal from electrocardiogram (ECG) (Ch. 7), and a robust photoplethysmogram(PPG)waveform analysis based on amodel-based approach (Ch. 8). In Part III, ANS biomarkers are evaluated in stress assessment (Ch. 9) and in the monitoring of depression (Ch. 10). Part I:Mental health is not only related to that positive state ofwell-being, inwhich an individual can cope with the normal stresses of life, but also to the absence of mental illness. Mental illness or disorder can be defined as an emotional, cognitive, or behavioural disturbance that causes substantial functional impairment in one or more major life activities. The most common mental disorders, which are often co-occurring, are anxiety and major depressive disorder (MDD). Mental illness has a negative impact on the quality of life, since it is associated with considerable losses in health and functioning, and increases significantly a person’s risk for cardiovascular diseases. A common instigator underlying the co-morbidity between MDD, cardiovascular pathology, and anxiety is mental stress. Stress is common in our fast-paced society and strongly influences our mental health. In the short term, ANS controls the cardiovascular response to stressful stimuli. Regulation of physiological parameters, such as heart rate, respiratory rate, and blood pressure, allows the organism to respond to sudden changes in the environment. However, physiological adaptation to a regularly occurring environmental phenomenon alters biological systems involved in stress response. Neurobiological alterations in the brain can disrupt the function of the ANS. ANS dysfunction and structural brain changes have a negative impact on cognitive, emotional, and behavioral processes, thereby leading to development of mental illness. Part II: The development of novel approaches for deriving noninvasive ANS biomarkers offers the possibility to improve the assessment of stress in healthy individuals and ANS dysfunction in MDD patients. Joint analysis of various biosignals (multi-modal approach) allows for the quantification of interactions among biological systems associated with ANS, while the modeling of biosignals and subsequent analysis of the model’s parameters (model-based approach) allows for the robust quantification of changes in physiological mechanisms related to the ANS. A novel method, which takes into account both phase and frequency locking phenomena between respiration and HRV signals, for assessing quadratic nonlinear cardiorespiratory coupling is proposed in Ch. 6.3. Novel techniques for improving the monitoring of respiration are proposed in Ch. 7. In Ch. 8, to increase the robustness for some morphological measurements reflecting arterial tone changes, the modeling of the PPG pulse as amain wave superposed with several reflected waves is considered. Part III: ANS biomarkers are evaluated in the assessment of different types of stress, either physiological or psychological, in healthy individuals, and then, in the monitoring of depression. In the presence of mental stress (Ch. 9.1), induced by cognitive tasks, healthy subjects show an increment in the respiratory rate and higher number of nonlinear interactions between respiration and HRV signal, which might be associated with a sympathetic activation, but also with a less regular breathing. In the presence of hemodynamic stress (Ch. 9.2), induced by a postural change, healthy subjects show a reduction in strength of the quadratic nonlinear cardiorespiratory coupling, whichmight be related to a vagal withdrawal. In the presence of heat stress (Ch. 9.3), induced by exposure to elevated environmental temperatures, healthy subjects show an increased sympathovagal balance. This demonstrates that ANS biomarkers are able to assess different types of stress and they can be further explored in the context of depression monitoring. In Ch. 10, differences in ANS function between MDD and healthy subjects during a mental stress protocol are assessed, not only with the raw values of ANS biomarkers but also with autonomic reactivity indices, which reflect the ability of an individual to copewith a challenging situation. Results show that depression is associated with autonomic imbalance, characterized by increased sympathetic activity and reduced arterial compliance. Autonomic reactivity indices quantified by changes, from stress to recovery, in arterial stiffness surrogates, such as the PPG amplitude loss in wave reflections, show the best performance in terms of correlation with depression severity, yielding to correlation coefficient r = −0.5. The negative correlation implies that a higher degree of depression is associated with a decreased autonomic reactivity. The discriminative power of ANS biomarkers is supported by their high diagnostic performance for classifying subjects as having MDD or not, yielding to accuracy of 80.0%. Therefore, it can be concluded that ANS biomarkers can be used for assessing stress and that impaired arterial compliance might constitute a biomarker of mental health useful in the monitoring of depression.<br /