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Related Experiment Video

Updated: Mar 27, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Stream computing for biomedical signal processing: A QRS complex detection case-study.

B M Murphy, C O'Driscoll, G B Boylan

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 7, 2016

Summary

This summary is machine-generated.

Stream computing efficiently processes real-time physiological signals. This study demonstrates its application for accurate electrocardiogram (ECG) QRS detection, highlighting its clinical potential.

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Area of Science:

Computer Science
Biomedical Engineering
Data Science

Background:

Big Data technologies enable processing large datasets on commodity hardware.
Stream computing offers real-time, low-latency data processing.
Clinical needs exist for efficient physiological signal analysis.

Purpose of the Study:

To investigate the application of stream computing for physiological signal processing.
To present a case study of stream processing for QRS detection in ECG data.

Main Methods:

Applied stream processing techniques to electrocardiogram (ECG) data.
Focused on the specific problem of QRS complex detection.

Main Results:

Demonstrated the feasibility of using stream computing for real-time ECG analysis.
Successfully implemented QRS detection using this paradigm.

Conclusions:

Stream computing is a viable approach for clinical physiological signal processing.
This method shows promise for real-time analysis of ECG data, including QRS detection.