Machine learning enhancements for wearable device investigation of acute anxiety and cardiovascular diseases
type: CreativeWork
description: Promoting well-being and healthy ageing in young adults increasingly hinges on detecting and managing conditions that silently erode quality of life, chief among them cardiovascular health and anxiety-related disorders. This dissertation advances both fields by developing data-driven methods that move clinical assessment from infrequent clinic visits to continuous, patient-centred monitoring. We address two persistent challenges: (i) identifying early autonomic markers of cardiovascular health before life-threatening damage occurs; and (ii) recognising context-specific surges in anxiety that impair daily functioning yet often escape formal diagnosis. By integrating multimodal wearable signals with modern machine-learning techniques, the work demonstrates how artificial intelligence can support clinicians in forecasting cardiovascular risk, delivering just-in-time interventions for anxiety, and ultimately fostering healthier, more independent ageing. This work proposes new data-driven machine learning-based solutions utilizing health data from multiple modalities, such as electrocardiography (ECG), blood volume pulse (BVP), electrodermal activity (EDA) etc. signals, to improve early disease prediction and progression in physical and mental health disorders. We measure our ability to use these signals to classify anomalies in the cardiovascular health and physiological body reactions in persons with disorders. This thesis is a multidisciplinary effort that involves novel combinations of sensors, audio, machine learning, biomechanics, and dynamical analyses to better characterize physiological signals from the body. These studies on the integration of AI and health data may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. This may help providers to individualize or generalize treatment plans and design improved clinical trials; thus, help reduce the skyrocketing hea…
abstract: Promoting well-being and healthy ageing in young adults increasingly hinges on detecting and managing conditions that silently erode quality of life, chief among them cardiovascular health and anxiety-related disorders. This dissertation advances both fields by developing data-driven methods that move clinical assessment from infrequent clinic visits to continuous, patient-centred monitoring. We address two persistent challenges: (i) identifying early autonomic markers of cardiovascular health before life-threatening damage occurs; and (ii) recognising context-specific surges in anxiety that impair daily functioning yet often escape formal diagnosis. By integrating multimodal wearable signals with modern machine-learning techniques, the work demonstrates how artificial intelligence can support clinicians in forecasting cardiovascular risk, delivering just-in-time interventions for anxiety, and ultimately fostering healthier, more independent ageing. This work proposes new data-driven machine learning-based solutions utilizing health data from multiple modalities, such as electrocardiography (ECG), blood volume pulse (BVP), electrodermal activity (EDA) etc. signals, to improve early disease prediction and progression in physical and mental health disorders. We measure our ability to use these signals to classify anomalies in the cardiovascular health and physiological body reactions in persons with disorders. This thesis is a multidisciplinary effort that involves novel combinations of sensors, audio, machine learning, biomechanics, and dynamical analyses to better characterize physiological signals from the body. These studies on the integration of AI and health data may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. This may help providers to individualize or generalize treatment plans and design improved clinical trials; thus, help reduce the skyrocketing hea…
author: Dogan, Ayse
language: en
keywords: wearable devices, signal processing, digital phenotyping, label generation, ECG
date published: 2025-12
date modified: 2026-07-22T22:25:07Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois Urbana-Champaign

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2142/132474