title: Stream computing for biomedical signal processing: A QRS complex detection case-study.
abstract: Recent developments in "Big Data" have brought significant gains in the ability to process large amounts of data on commodity server hardware. Stream computing is a relatively new paradigm in this area, addressing the need to process data in real time with very low latency. While this approach has been developed for dealing with large scale data from the world of business, security and finance, there is a natural overlap with clinical needs for physiological signal processing. In this work we present a case study of streams processing applied to a typical physiological signal processing problem: QRS detection from ECG data.
authors.forename: B M
authors.initials: BM
authors.lastname: Murphy
authors.forename: C
authors.initials: C
authors.lastname: O'Driscoll
authors.forename: G B
authors.initials: GB
authors.lastname: Boylan
authors.forename: G
authors.initials: G
authors.lastname: Lightbody
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
mesh_terms: D004562:Electrocardiography / Q000145:classification*; D006801:Humans; D008492:Medical Informatics Computing; D012815:Signal Processing, Computer-Assisted