Search

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.

Spectrally filtered principal component analysis (SFPCA) is a method we developed to discriminate between seismic source types. It is based on the well-known principal component analysis but applied to seismic gradiometric data. In this article, we build on our previous efforts by testing the method on data collected in a small-scale field experiment using two source types generated by manually striking the ground at various source–receiver distances (source type A) and orientations relative to the ground surface (source type B). Using the SFPCA method that we originally developed in Challu et al. (2021), we found that we can achieve good discrimination performance for a wide range of experimental geometries and noise conditions. In addition to testing the SFPCA method using a supervised learning approach, we present an SFPCA-based discrimination method using an anomaly detection paradigm. Specifically, given a population of event-specific data (e.g., source type A), we demonstrate that an event with source type B will fall outside the accepted population range of source type A. Thus, SFPCA may have value as a seismic discriminant in the form of an anomaly detector, which may be useful if a sufficient training dataset is not available.