Personalized Anomaly Detection in PPG Data using Representation Learning
  and Biometric Identification
Ramin Ghorbani Marcel J. T. Reinders David M. J. Tax
Photoplethysmography (PPG) signals, typically acquired from wearable devices,
hold significant potential for continuous fitness-health monitoring. In
particular, heart conditions that manifest in rare and subtle deviating heart
patterns may be interesting. However, robust and reliable anomaly detection
within these data remains a challenge due to the scarcity of labeled data and
high inter-subject variability. This paper introduces a two-stage framework
leveraging representation learning and personalization to improve anomaly
detection performance in PPG data. The proposed framework first employs
representation learning to transform the original PPG signals into a more
discriminative and compact representation. We then apply three different
unsupervised anomaly detection methods for movement detection and biometric
identification. We validate our approach using two different datasets in both
generalized and personalized scenarios. The results show that representation
learning significantly improves anomaly detection performance while reducing
the high inter-subject variability. Personalized models further enhance anomaly
detection performance, underscoring the role of personalization in PPG-based
fitness-health monitoring systems. The results from biometric identification
show that it's easier to distinguish a new user from one intended authorized
user than from a group of users. Overall, this study provides evidence of the
effectiveness of representation learning and personalization for anomaly
detection in PPG data.