Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition: Wearable Systems, Modeling, and Advanced Biosignal Processing

This thesis aims at investigating how electrophysiological signals related to the autonomic nervous system (ANS) dynamics could be source of reliable and effective markers for mood state recognition and assessment of emotional responses. In-depth methodological and applicative studies of biosignals such as electrocardiogram, electrodermal response, and respiration activity along with information coming from the eyes (gaze points and pupil size variation) were performed. Supported by the current literature, I found that nonlinear signal processing techniques play a crucial role in understanding the underlying ANS physiology and provide important quantifiers of cardiovascular control dynamics with prognostic value in both healthy subjects and patients. Two main applicative scenarios were identified: the former includes a group of healthy subjects who was presented with sets of images gathered from the International Affective Picture System hav- ing five levels of arousal and five levels of valence, including both a neutral reference level. The latter was constituted by bipolar patients who were followed for a period of 90 days during which psychophysical evaluations were performed. In both datasets, standard signal processing techniques as well as nonlinear measures have been taken into account to automatically and accurately recognize the elicited levels of arousal and valence and mood states, respectively. A novel probabilistic approach based on the point-process theory was also successfully applied in order to model and characterize the instantaneous ANS nonlinear dynamics in both healthy subjects and bipolar patients. According to the reported evidences on ANS complex behavior, experimental results demonstrate that an accurate characterization of the elicited affective levels and mood states is viable only when non- linear information are retained. Moreover, I demonstrate that the instantaneous ANS assessment is effective in both healthy subjects and patients. Besides mathematics and signal processing, this thesis also contributes to pragmatic issues such as emotional and mood state mod- eling, elicitation, and noninvasive ANS monitoring. Throughout the dissertation, a critical review on the current state-of-the-art is reported leading to the description of dedicated experimental protocols, reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)

Applications of Blind Source Separation to the Magnetoencephalogram Background Activity in Alzheimer’s Disease

En esta Tesis Doctoral se ha analizado actividad basal de magnetoencefalograma (MEG) de 36 pacientes con la Enfermedad de Alzheimer (Alzheimer’s Disease, AD) y 26 sujetos de control de edad avanzada con técnicas de separación ciega de fuentes (Blind Source Separation, BSS). El objetivo era aplicar los métodos de BSS para ayudar en el análisis e interpretación de este tipo de actividad cerebral, prestando especial atención a la AD. El término BSS denota un conjunto de técnicas útiles para descomponer registros multicanal en las componentes que los dieron lugar. Cuatro diferentes aplicaciones han sido desarrolladas. Los resultados de esta Tesis Doctoral sugieren la utilidad de la BSS para ayudar en el procesado de la actividad basal de MEG y para identificar y caracterizar la AD.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic