Kamruzzaman Methun, Salinas Jorge S, Kolla Hemanth, Sale Kenneth L, Balakrishnan Uma, Poorey Kunal
Sandia National Laboratories, Livermore, CA 94550, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Sensors (Basel). 2025 Sep 7;25(17):5586. doi: 10.3390/s25175586.
Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.
早期发现潜在的传染病爆发对于制定有效的干预措施至关重要。在本研究中，我们引入了针对从可穿戴设备收集的健康数据集量身定制的先进异常检测方法，在个体和群体层面都提供了见解。利用来自可穿戴设备的真实世界生理数据，包括心率和活动数据，我们开发了一个用于个体感染早期检测的框架。尽管有来自近期大流行的数据，但在数据收集方面仍存在重大差距，这阻碍了方法的开发。为了弥合这一差距，我们利用瓦瑟斯坦生成对抗网络（WGAN）来生成逼真的合成可穿戴数据，扩充我们用于训练的数据集。随后，我们使用这些扩充后的数据集来实施一种基于协峰度的技术，用于多变量时间序列数据中的异常检测。我们的方法包括对合成数据与建模所用实际数据相比的不确定性进行全面评估，以及与生理测量中微调异常检测阈值相关的不确定性。通过我们的工作，我们提出了一种用于多变量数据集中早期异常检测的增强方法，在医疗保健及其他领域具有广阔的应用前景。这个框架可能会彻底改变早期检测策略，并在未来大流行中对公共卫生应对工作产生重大影响。
