A Simple Unsupervised, Real-time Clustering Method for Arterial Blood Pressure Signal Classification

Biomedical signal analysis often depends on methods to detect and distinguish abnormal or high noise/artifact signal from normal signal. A novel unsupervised clustering method suitable for resource constrained embedded computing contexts, classifies arterial blood pressure (ABP) beat cycles as normal or abnormal. A cycle detection algorithm delineates beat cycles, so that each cycle can be modeled by a continuous time Fourier series decomposition. The Fourier series parameters are a discrete vector representation for the cycle along with the cycle period. The sequence of cycle parameter vectors is a non-uniform discrete time representation for the ABP signal that provides feature input for a clustering algorithm. Clustering uses a weighted distance function of normalized cycle parameters to ignore cycle differences due to natural and expected physiological modulations, such as respiratory modulation, while accounting for differences due to other causes, such as patient movement artifact. Challenging cardiac surgery patient signal examples indicate effectiveness.