Unmasking the Unexpected

Abstract

The integration of wearable technology into healthcare is revolutionizing health monitoring by enabling continuous tracking of vital metrics like heart rate and blood sugar. Devices such as smartwatches and glucose monitors empower proactive interventions, reducing hospital visits and personalizing care. For instance, wearables can detect irregular heart rhythms for early cardiovascular disease detection or assist individuals with diabetes in managing glucose levels. These advancements are enabled by technologies like photoplethysmography (PPG), a non-invasive method for real-time monitoring of physiological signals. Continuous monitoring generates time series data that captures dynamic health fluctuations over time. This data allows for identifying irregularities and deviations that isolated measurements might miss. Detecting anomalies, such as abrupt changes in heart rate or prolonged abnormal patterns, is essential for timely interventions in managing chronic conditions like hypertension and cardiovascular diseases.

However, the analysis of time series data introduces challenges. For instance, label scarcity arises because labeling health anomalies requires expert input, which is often infeasible for large datasets. Inter-subject variability becomes a concern as physiological patterns differ significantly across individuals, complicating model generalization. Furthermore, temporal dependencies in time series data add complexity, as observations are sequentially related and anomalies may not manifest as isolated points but as patterns or sequences deviating from normal behavior. Detecting subtle anomalies, minor deviations that accumulate over time but may signal early-stage conditions, becomes particularly challenging due to their resemblance to normal temporal variations and noise in the data. For example, a gradual change in heart rate variability might indicate the onset of an irregular rhythm but could easily blend into inherent variability if not carefully analyzed. Moreover, evaluation metrics for time series data are insufficient, failing to capture the temporal complexities of real-world applications. Consequently, conventional metrics can misrepresent model performance, leading to unreliable or misleading assessments.

This thesis addresses these challenges by advancing time series anomaly detection through innovative methodologies. A key focus is on addressing the limitations of existing evaluation metrics by introducing new evaluation metrics to better capture temporal complexities, ensuring reliable and meaningful performance assessments. Beyond evaluation, this thesis is guided by several core principles to address the challenges inherent in time series data analysis. Central to this is the use of unsupervised representation learning to tackle label scarcity and variability, enabling robust feature extraction from unlabeled data while maintaining generalizability. Finally, the thesis develops strategies for increasing sensitivity to subtle anomalies, providing effective solutions for identifying small yet significant deviations in complex datasets. Together, these contributions present a comprehensive framework for improving anomaly detection systems across diverse applications, bridging theoretical advancements with practical, real-world needs.