title: Large-scale physiological waveform retrieval via locality-sensitive hashing.
abstract: We propose a fast, scalable locality-sensitive hashing method for the problem of retrieving similar physiological waveform time series. When compared to the naive k-nearest neighbor search, the method vastly speeds up the retrieval time of similar physiological waveforms without sacrificing significant accuracy. Our result shows that we can achieve 95% retrieval accuracy or better with up to an order of magnitude of speed-up. The extra time required in advance to create the optimal data structure is recovered when query quantity equals 15% of the repository, while the method incurs a trivial additional memory cost. We demonstrate the effectiveness of this method on an arterial blood pressure time series dataset extracted from the ICU physiological waveform repository of the MIMIC-II database.
authors.forename: Yongwook Bryce
authors.initials: YB
authors.lastname: Kim
authors.forename: Una-May
authors.initials: UM
authors.lastname: O'Reilly
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual