Key result
Stream computing was successfully applied as a case study to process physiological signals for real-time QRS complex detection from ECG data.
Stream computing paradigms from big data can be effectively applied to real-time physiological signal processing, such as QRS detection in ECGs.
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Feasibility shown for stream computing in real-time QRS detection; leaves open clinical utility and need for prospective validation.
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.
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Murphy et al. (2015) studied QRS detection from ECG data. Stream computing was evaluated. Stream computing was successfully applied as a case study to process physiological signals for real-time QRS complex detection from ECG data.
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